Accurate determination of surface reference data in digital photographs in ice-free surfaces of Maritime Antarctica

Science of the Total Environment 573 (2016) 290–302

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Science of the Total Environment journal homepage: www.elsevier.com/locate/scitotenv

Accurate determination of surface reference data in digital photographs in ice-free surfaces of Maritime Antarctica Pedro Pina a,⁎, Gonçalo Vieira b, Lourenço Bandeira a, Carla Mora b a b

CERENA, Instituto Superior Técnico, Universidade de Lisboa, Av. Rovisco Pais, 1049-001 Lisboa, Portugal CEG, Instituto de Geografia e Ordenamento do Território, Universidade de Lisboa, Rua Branca Edmée Marques, 1600-276 Lisboa, Portugal

H I G H L I G H T S

G R A P H I C A L

A B S T R A C T

• Automated surface identification in digital photographs of Antarctica • Field validation in Fildes and Barton Peninsulas, King George Island (62°S) • Classification of image objects with overall accuracies around 92% • Adequate methodology for obtaining accurate surface reference data • Possibility of applying at sub-metric resolution using current VHR satellite imagery

a r t i c l e

i n f o

Article history: Received 19 April 2016 Received in revised form 29 July 2016 Accepted 15 August 2016 Available online xxxx Editor: D. Barcelo Keywords: Image analysis Segmentation Object-based classification Ice-free surfaces Polar regions

a b s t r a c t The ice-free areas of Maritime Antarctica show complex mosaics of surface covers, with wide patches of diverse bare soils and rock, together with various vegetation communities dominated by lichens and mosses. The microscale variability is difficult to characterize and quantify, but is essential for ground-truthing and for defining classifiers for large areas using, for example high resolution satellite imagery, or even ultra-high resolution unmanned aerial vehicle (UAV) imagery. The main objective of this paper is to verify the ability and robustness of an automated approach to discriminate the variety of surface types in digital photographs acquired at ground level in ice-free regions of Maritime Antarctica. The proposed method is based on an object-based classification procedure built in two main steps: first, on the automated delineation of homogeneous regions (the objects) of the images through the watershed transform with adequate filtering to avoid an over-segmentation, and second, on labelling each identified object with a supervised decision classifier trained with samples of representative objects of ice-free surface types (bare rock, bare soil, moss and lichen formations). The method is evaluated with images acquired in summer campaigns in Fildes and Barton peninsulas (King George Island, South Shetlands). The best performances for the datasets of the two peninsulas are achieved with a SVM classifier with overall accuracies of about 92% and kappa values around 0.89. The excellent performances allow validating the adequacy of the approach for obtaining accurate surface reference data at the complete pixel scale (sub-metric) of current very high resolution (VHR) satellite images, instead of a common single point sampling. © 2016 Elsevier B.V. All rights reserved.

1. Introduction ⁎ Corresponding author. E-mail addresses: [email protected] (P. Pina), [email protected] (G. Vieira), [email protected] (L. Bandeira), [email protected] (C. Mora).

http://dx.doi.org/10.1016/j.scitotenv.2016.08.104 0048-9697/© 2016 Elsevier B.V. All rights reserved.

The Northern Antarctic Peninsula and the South Shetland Islands region are regarded as very sensitive concerning climate change since

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they are suffering, for more than half-a-century, one of the most rapid increases of the mean annual air temperatures (Turner et al., 2005). In addition, the low altitude permafrost environment in the South Shetlands is currently slightly below 0 °C (Vieira et al., 2010), being at a critical threshold which may suffer drastic changes if this warming trend is to continue. A change in the lower permafrost boundary will induce modifications to hydrology and slope dynamics with consequences for the terrestrial ecosystems in these ice-free areas (Vieira et al., 2010; Bockheim et al., 2013). In particular, the ice-free areas of Maritime Antarctica show complex mosaics of surface covers, with wide patches of diverse bare soils and rock (López-Martínez et al., 2012), together with various vegetation communities dominated by lichens and mosses (Longton, 1988; Bokhorst et al., 2007). The microscale variability is difficult to characterize and quantify (Cannone, 2004; Singh et al., 2015), but is essential for ground-truthing and for defining automated classifiers for large areas using high resolution satellite imagery or ultra-high resolution unmanned aerial vehicle (UAV) imagery (Schmid et al., 2012; Lucieer et al., 2014; Andrade et al., 2016). The best way to properly characterize this microscale variability, so to accurately use coarser resolution imagery, is through the collection of ground reference information, a fundamental procedure for calibrating and validating the use of remotely sensed data. This aspect becomes more relevant in Antarctica, where the access to the field is more difficult than on any other place on Earth, due to admission restrictions, and meteorological and logistical issues. Nonetheless, when access to the field is possible, the amount of sites to survey is also restricted in each season, and only mid- to long-term planned campaigns allow constructing detailed and large ground reference datasets. Furthermore, the data collected is normally less than planned, due to the frequent uncertainties related to the development of the schedules of the campaigns triggered by the meteorology or logistical constraints. The extraction of meaningful information from the collected data should in this way be maximized, also because a later revisit to the field for verification or for additional monitoring may be impracticable. Consequently, the inventorying and monitoring of the ice-free surfaces in this Antarctic region is a fundamental activity being developed for some decades during the field campaigns, sometimes jointly with remotely acquired data (see for example, among some of the most recent publications, Fretwell et al. (2011), Shin et al. (2014), Vieira et al. (2014), Schmid et al. (2015) or Casanovas et al. (2015)). In particular, the sampling, the interpretation of the data and the mapping of the surfaces, normally more focused on single covers, is typically aided by photographic images taken in-situ for each relevant feature of interest (López-Martínez et al. (2012); Michel et al. (2014)). The technological development of digital imaging has simplified the amount of images that can be acquired and stored. Therefore, analysing details of the different ice-free surfaces in specific locations has improved (the precision of the location of interest will depend on the GPS used). Consequently, numerous images are acquired by the end of each campaign that are later analysed in the laboratory. Besides the qualitative use of the images for identifying or inventorying, for instance, the type of rock or mineral, the species of moss or of lichen or the type of patterned ground, a coarse estimation of the amount of occurrence of each class in standardized plot is sometimes performed to monitor the temporal variation of a given surface type (Cannone, 2004, Kim et al., 2007, Shin et al., 2014). The possibility of having precise characterizations of the surface through the automated analysis of these images (at sub-millimetric scale) would be an important advancement for, for instance, constructing more correct reference or ground-truth datasets that could be more accurately used, among others, in the training/validation procedures of remotely sensed imagery classification. This is particularly evident for very high resolution (VHR) satellite imagery (with sub-metric resolution), but also for images (at milli- to centimetric scales) captured by unmanned aerial vehicles, whose usage is currently increasing very

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rapidly (Bandeira et al., 2014; Lucieer et al., 2014; Turner et al., 2014). This should play a key-role in further inventorying and monitoring regions in Antarctica difficult to access. Some image analysis approaches addressing the quantification of reference surface data in digital images captured on the ground are found in the literature. Almost all of them refer to vegetation identification and mapping studies, namely plots in Arkansas (Luscier et al., 2006), plant covers in the Canadian Arctic (Chen et al., 2010), rangeland vegetation (Louhaichi et al., 2010), lichen communities in James Ross Island in the Antarctic Peninsula (Bohuslavová et al., 2012), weeds and crop plants in maize fields (Montalvo et al., 2013), floral diversity in desert ecosystems (Ksiksi and El-Keblawy, 2013) or the dead and living biomass (Bauer and Strauss, 2014). The image analysis methods employed by these authors are varied, from simple to more complex operators, based on pixel- or object-based classification procedures (Blaschke, 2010) and with good detection performances on the dedicated targets. Since these methods are tailored for the identification of each specific vegetation or agricultural problem, they naturally lack a degree of generalization, preventing its simultaneous application to a multitude of surface types. Thus, the necessity of developing such approach emerges. The main objective of this paper consists on developing a methodology that should be able to compute accurately the amount of each surface type in digital photographs taken on the ground in typical ice-free surfaces of Antarctica, where their diversified components (rock, soil or vegetation) can be simultaneously detected. In addition, it should be effective and of generalized interest. For achieving that purpose, the procedure should be robust enough to be applied on images captured with common compact cameras, where the colour and the spatial resolutions may not be extreme. The development and testing of such image analysis based approach is described and tested in this paper. In brief, it consists of an objectbased procedure, that starts by identifying the coloured homogeneous regions of the images (the objects), which are then individually labelled by a supervised classifier. The datasets used in the design and validation of this method comprise surface images acquired in field campaigns in Maritime Antarctica, in Fildes and Barton peninsulas of King George Island, South Shetland Islands. 2. Study areas The study areas are located in Fildes and Barton peninsulas in King George Island (South Shetland Islands, Maritime Antarctica — Fig. 1). Fildes peninsula (62°12′S, 58°58′W) is one of the largest ice-free regions of the South Shetlands with about 29 km2 (Peter et al., 2008) and consists of a sequence of Late Cretaceous volcanoclastic rocks (basalts, andesite to dacite), overlaid by limestones, tuffaceous conglomerates, sandstones and clays of early-mid-Eocene–Oligocene age. The relief is dominated by two high structural volcanic platforms (Meseta Sul, 167 m a.s.l. and Meseta Norte, 155 m a.s.l.), with low lying erosional platforms between and around them, mainly with altitudes below 50 m a.s.l. The selected study site is located in Meseta Norte (Fig. 1), due to the significant vegetation and geomorphological diversity, which were the basis for mapping the Usnea spp. communities in a testing hypothesis for using it as a proxy for ground surface cooling (Vieira et al., 2014). Barton peninsula (62°14′S, 58°46′W) is about 10 km2 and is composed of volcanic and plutonic rocks together with minor thin-bedded sandstone and siltstone. Volcanic rocks consist mainly of basalt, basaltic andesite, agglomerate and lapilli tuff, while plutonic rocks are mainly granodiorite and diorite (Kim et al., 2000). The peninsula shows a rugged topography, with a wide and gentle slope in the central area, ranging from about 90 to 180 m a.s.l. (Kim et al., 2007), with the highest peak close to 300 m a.s.l. The smaller dimension of this peninsula, when compared to Fildes, allowed collecting photos from diverse locations that showed the diversity of vegetation and ground surfaces.

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Fig. 1. The location of the study areas Fildes and Barton peninsulas in the a) Northern Antarctic Peninsula, b) within the South Shetland Islands and c) on the south western part of King George Island. JES stands for Julio Escudero Station and KSS for King Sejong Station.

3. Methods The method proposed is built in three main stages as schematically shown in the diagram of Fig. 2. The first stage encompasses image acquisition and pre-processing tasks, the second refers to the image segmentation stage, while the last one comprises the object-based classification procedure. The details of each stage are presented in the following subsections.

with 1600 × 1200 pixels, with a resolution of 0.5 mm/pixel (small differences may locally exist since surfaces are not completely flat) and stored in a file format without suffering lossy compression. An example of an image captured with this system is shown in Fig. 3b. The lower part of the pole is visible on the bottom left part of the images, so the region of interest of the images for further processing must be smaller than the whole photo field of view. The cropped image is 1600 × 730 pixels, as can be seen in the masked example shown in Fig. 3c.

3.1. Image acquisition 3.2. Image segmentation Vertical images of the ground surface were acquired with a Leica fixed zoom camera incorporated on the console (or keypad) of a global navigation satellite imagery (GNSS) Leica Viva which was used to collect coordinates in kinematic mode with one centimetre accuracy. A base station was installed at known coordinates in the survey area and differential correction was done using a VHF radio link. The console is attached to a mounting bracket which in turn is fixed to the rover pole (see Fig. 3a). The position of the mounting bracket on the pole is fixed, allowing capturing images of the surface at a fixed distance and hence with the same spatial resolution. The images are in colour (or RGB)

The segmentation of the images, a primordial task of image analysis, consists on the delineation of their homogeneous regions (also generically called objects) according to their spectral or colour properties. For the segmentation of the Antarctic surface images some preliminary tests with two of the most common and robust approaches were made: region growing and the watershed transform. Region growing (Adams and Bischof, 1994) requires the initial implantation of seeds in each region of interest to segment, which groups in an iterative procedure the neighbouring pixels showing similar spectral properties. The tests have

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Fig. 2. Schematic diagram with the main steps of the proposed methodology.

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proven that the selection of the right seeds was frequently not well succeeded. On the contrary, the watershed transform performed nicely in most situations, so it was selected for performing the segmentation tasks. The watershed transform was proposed in the frame of mathematical morphology (Beucher and Lantuéjoul, 1979; Roerdink and Meijster, 2000) and is one of the most successful and robust operators in image segmentation, whose fast algorithmic implementations have also greatly contributed to its popularity (Vincent and Soille, 1991). This transform assumes the digital images to be topographic surfaces. Through this analogy, it acts like it floods the surface (or the image) from each local minimum until the water rising from adjacent catchment basins (or adjacent objects) touches each other. When this situation is ready to occur, a dam is installed on the surface (a line is drawn on the image) to prevent the merging of the water coming from the adjacent basins. The union of all dams corresponds to the watershed lines, that is, the lines that separate distinct adjacent regions and, therefore, delineate homogeneous regions of the image. Since what it is intended to delineate are the edges of the objects, the image of the magnitude component of the gradient is used to compute the watershed lines instead of the initial image itself (Soille, 2004). Other edge detectors, like Sobel, Prewitt or Canny (Gonzalez and Woods, 2008) were also tested, but without noticeable differences on the outputs. But the direct application of the watershed transform to the gradient image (i.e., without any previous filtering) produces, as in the great majority of situations, an over-segmentation of the image since every minimum is taken into account. To overcome this drawback, a couple of procedures can be applied (Soille, 2004): one before and the other after the application of the watershed transform. The first one intends to remove unimportant minima or regions from the input image (that is, points with low contrast within its neighbourhood) before the application of the watershed transform, having in mind that if they are removed from the start they cannot later constitute an object (this filtering is controlled by the scale parameter ‘S’, the higher its value, the less are the remaining regions of interest). The second option consists on merging adjacent basins resulting from the application of the watershed transform and whose colour properties are below a given threshold (this is naturally called the merge parameter ‘M’).

Fig. 3. D-GPS system used in Fildes and Barton peninsulas field campaigns (2012 and 2013): (a) testing the Leica GS 10 equipment near Julio Escudero Station in Fildes peninsula, the photographic camera is on the back of the green console mounted on the pole; (b) image acquired with the photographic camera of the mobile console showing the pole on the left (image 1600 × 1200 pixels); and (c) masking for obtaining the region of interest of the image for further processing (image 1600 × 730 pixels).

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Several segmentations were performed varying the values of S and M in a small set of images from Fildes and Barton peninsulas, so to find the most adequate segmentation parameters (the image of Fig. 4a is used to illustrate the findings) that shall be used afterwards in all image datasets. The evaluation of these exploratory segmentations was made visually, taking simultaneously into consideration two main criteria: (i) the number of objects in which the image is segmented should be as low as possible and (ii) each object should contain only information related to a single type of surface. In practice, the optimal segmentation consists of avoiding an excessive over-segmentation of the image (there are adjacent basins belonging to the same surface type that should be merged) and an undersegmentation (adjacent regions belonging to different surface types that are merged into the same object and that should not). An over-segmentation of the image, if not too detailed, is not problematic, since these regions can still be assigned with the same label afterwards by the subsequent classification procedure. Anyhow, the detail

should not be too high (an excess of many small objects), since each object would mainly contain local information, missing the context that could be very distinctive from the samples representing the class. On the contrary, under-segmentation must be strictly avoided since it would be really problematic, as distinct regions merged into single objects can no longer be recovered which, in addition, would naturally increase their grade of spectral mixture. Finding the optimal segmentation for these samples, besides being done carefully and attentively, did not require a fine and extensive tuning or regulation of the pair of parameters involved in the procedure. Examining roughly the image datasets it is possible to verify at once that the values of S could not be very high, since small objects such as clasts or lichen branches would be filtered out. For the merging it is possible to be much less conservative and use higher values for M, since the number and nature of classes involved is relatively low and distinct. Several combinations of both parameters were tested (first with coarser steps of 10 units and later with steps of 5 units for fine tuning, in the range 0–

Fig. 4. Illustration of the evaluation of the segmentation in a Fildes image: (a) initial; (b) over-segmentation (S = 5, M = 5); (c) adequate-segmentation (S = 15, S = 80); and (d) undersegmentation (S = 25, M = 90).

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100), which are illustrated with the image of a moss blanket with soil and clasts of several dimensions in Fig. 4a (a better insight is obtained in a zoomed region shown at the right). For a low simplification of the procedure (S = 5, M = 5), it is clearly noticeable that too many regions are produced, providing an over-segmentation of the image (Fig. 4b). Increasing both parameters (S = 15, M = 80), one can observe the (adequate) disappearance or merging of many objects, allowing the watershed lines to be closer to the real contours of the different objects (Fig. 4c); although the fragmentation is still somewhat high, there are not distinct surface classes merged into the same object. The parameters can still be increased (S = 25, M = 90) and as a result some regions of the image seem better delineated (for instance, the moss blanket is less fragmented), but the undesired situations start to occur due to an under-segmentation, as some clasts are now merged with the moss blanket (Fig. 4d). From these tests, it was determined that the best parameters for the segmentation task were S = 15 and M = 80, and although some structures are still fragmented into several objects, almost none of them includes distinct surfaces into the same region. 3.3. Object-based classification When the objects or homogeneous regions in an image are identified by the segmentation, the procedure can take care of their individual classification taking into account several of their properties (also referred as features, attributes or descriptors) to make the decision. These properties can be diverse and are related to geometric, colour and textural characteristics of the objects. In the procedure used in the current application, the geometric properties of each object concern the area, perimeter, shape and elongation factors. The shape factor compares the area of the object with the square of its perimeter (the shape factor of a circle is 1). The elongation factor is obtained from the ratio between its minor and major axes. The colour properties concern basic statistics of the pixels constituting each object in each colour band (R, G and B): average, extrema (minimum and maximum) and standard deviation. The textural properties used are constituted by simple descriptors of the spatial arrangement of intensities in a given region. It consists on computing the properties range, mean, variance and entropy for all pixels in a given kernel window (a square of size 3 is selected), followed by the computation of the average of all pixels constituting each object in each colour band. The entropy of an image is a statistical measure that informs about the degree of randomness of its texture. Therefore, for each object obtained in the segmentation procedure, the following 28 properties are computed: 4 geometric (area, perimeter, shape and elongation factors), 12 colour (mean, minimum, maximum, and standard-deviation in each of the three bands) and 12 textural features (range, mean, variance, and entropy, also in each of the three bands). The assignment of a label to each object based on a learning task with representative object samples of each defined label is the purpose of the supervised classification approaches selected. It consists of two main phases: first on the selection of object samples corresponding to each of the classes constituting the surface (training phase) and second, on the decision of classifying each object with one of those labels (testing or validation phase). In the supervised classification, the number of classes and their representative samples are previously defined. In this case, six classes are selected: two of geological nature (bare rock and bare soil), three of vegetation (moss formations of types 1 and 2, and lichen formations) and one for the shadows produced by the irregularity of the surface (mainly by the overlapping clasts of rocks). The content of each class is better described in the next section. It should be clarified that the objectives intended to achieve with this study are not to be exhaustive in fully testing numerous classifiers, nor by providing all the details or performances obtained in exploratory phases. For validating the processing sequence, it is good enough to

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have at least one classifier that performs well enough in the large majority of situations. Thus, two of the most robust and successful classifiers in a large variety of applications were selected for testing: Support Vector Machine (SVM) and k-Nearest Neighbour (kNN). In the early exploratory phases of the development of this method, other supervised classification methods (i.e., maximum likelihood) were tested, without robust performances. Besides that, also in the early stage of this research, pixel based-methods were evaluated, but abandoned due the low discriminant power of the colour content of each individual pixel. SVM is a supervised kernel method (Vapnik, 1995) that uses an implicit transformation to a higher dimensional space in order to achieve a good separability by means of a linear classifier in the new space. It has the ability to successfully handle data with unknown statistical distributions and with small training sets. The delineation of the decision boundary is obtained through the use of kernel functions that map the training data into a higher-dimensional space in which the classes can be linearly separated by a hyperplane. Sensitivity tests to evaluate different transformation kernels (linear, polynomial and radial basis function (RBF)) and the associated parameters of the SVM classifier were performed. The kernel that showed better results is the radial basis function (RBF), with gamma = 0.03 and C = 100 for their parameters (gamma defines the radius of influence of each training sample and C, the penalty parameter, controls how examples located on the wrong side of the decision region are penalized). kNN is a non-parametric classifier which assigns an object of unknown class to the most frequented class among the ‘k’ labelled training objects that are close to it (Altman, 1992). Closeness is defined in terms of a metric distance (normally on the Euclidean space) with the input measurement variables as the axes of the feature space. Each object is selected by a majority vote of its neighbours, being the object assigned to the most common of its ‘k’ nearest neighbours. Different numbers of neighbours (from 1 to 9, with step 2 to avoid ties) were tested, achieving a higher performance when the classifier k = 3. The classification of the segmented example provided in Fig. 4c is presented to illustrate the final step of the methodology: Fig. 5a shows a colour composition of some of the features used (the mean colour of each object), while Fig. 5b respects the thematic classified image using the SVM method. 3.4. Evaluation metrics The evaluation procedure is built upon metrics based on the error or confusion matrix, which is constructed from the comparison of reference or ground-truth data, manually built from the identification of a set of objects resulting from the segmentation procedures, with the outputs of the classifiers. In particular, the global evaluations and discussions are based on two of the most common performance quantities: the overall accuracy (oa) and the kappa index of agreement (k) (Congalton, 1991). In addition, the confusion matrix is used to detail the performance of each surface class, quantifying the respective accuracies (producer (prod) and user (user)) and errors, distinguishing between those performed in excess (errors of commission, com) from the ones by missing (errors of omission, omi). 4. Data 4.1. Datasets organization For testing the method two distinct sets of images were organized, one for each peninsula, which are processed separately. Each dataset is constituted by 40 representative surface images acquired with the same equipment during the field campaigns developed from 04 to 26 January 2012 (Fildes) and from 09 to 29 January 2013 (Barton). To better evaluate the robustness of the methodology, a cross-validation strategy was followed, so each subset is used at a time for training and later for testing. This kind of procedure, that uses all images in

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Fig. 5. Classification of the image of Fig. 4: (a) colour composition of the mean grey level features of each object (in each band); (b) final result using the SVM classifier (RBF kernel).

both roles (independently), provides much better results on how the SVM and kNN classifiers can generalize, that is, indicates more precisely if the classification errors are kept (in each step of the cross-validation) as low as if they were achieved by fitting parameters to the training set itself. This way, each dataset was divided into 4 subsets or folds of 10 images each (identified as F1, F2, F3 and F4 for Fildes and B1, B2, B3 and B4 for Barton). This 4-fold cross-validation procedure means that, for instance for the Fildes dataset, when F1 is used for training, F2, F3 and F4 are used for testing. Next, F2 is used for training, while F1, F3 and F4 are used for testing, and so forth, until all sub-datasets are used once for training. The same procedure is applied for the 4 sub-datasets of Barton. It should be clarified that in this division, the subsets were built to be as similar as possible, containing the same number of images/covers of the same type acquired at about the same day and time. This equilibrium is crucial for the classification since each subset will be used at a time for training and hence each one should be representative of the whole diversity of the terrain at this scale of analysis and also demonstrative of the meteorological (solar radiation) conditions at the time of image acquisition.

4.2. Surface classes The processed images respect only ice-free surfaces (dominated by bare soil and different vegetation covers), excluding those where snow and water are present. Anyhow, these classes can be easily added to the procedure if their presence is relevant, for instance,

when the construction of reference surface information is synchronous (in the same day) to the remotely sensed imagery. The definition of the classes in the procedure is based on a minimum amount of the surface that can be, for instance, perceived on a very high resolution satellite image through mixture model approaches (probabilistic models that infer the contribution of surface classes to the spectral or colour attribute value of an individual pixel). Those classes occurring in very small areas are not considered. Also, the discrimination between different types of rocks, soils, mosses or lichens is outside the scope (and possibilities) of the current type of spectral data that could be collected with the used standard camera. Thus, the selected surfaces consist of six classes, and are the same for both peninsulas: 1. Bare rock (BR) — consists of outcrops and clasts, boulders, gravels and coarse sands. 2. Bare soil (BS) — includes smaller fractions of soils, like fine sands, silts and clays. 3. Moss formations type I (MF1) — comprises formations whose colours are predominantly green, namely the genders Andreaeaceae, Dicranaceae or Encalypataceae. 4. Moss formations type II (MF2) — consists of mosses predominantly dark (black, grey or brown), like the gender Ditrichaceae. 5. Lichen formations (LF) — contains mainly Usnea spp., of light yellow to light green colours. 6. Shadow (SHA) — it refers to the colourless regions on the neighbourhood of some of the objects of the previous classes created by the irregularities of the surface. The shadows are mainly common

Table 1 Characteristics of the training samples for each subset of images (F1 to F4) for Fildes peninsula. Classes\subsets

BR BS MF1 MF2 UF SHA Total (number) Total (%)

F1

F2

F3

F4

Objects

Pixels

Objects

Pixels

Objects

Pixels

Objects

Pixels

171 57 56 31 67 22 404 0.36

172,475 139,648 139,946 58,902 12,338 3357 526,666 4.5

151 44 50 34 52 31 362 0.32

223,863 79,325 122,003 60,213 4546 6005 495,955 4.2

185 59 58 34 50 38 424 0.27

232,269 110,206 50,666 96,875 5637 12,921 508,574 4.4

159 57 41 38 71 32 398 0.31

167,382 124,620 138,692 53,527 9409 6297 499,927 4.3

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Fig. 6. Several classification examples using the SVM model for images of: (a–c) Fildes peninsula and (d–f) Barton peninsula. In each line the original image is on the left and the thematic on the right. The colour codes respecting each of the six surface classes are indicated in Fig. 5.

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Table 2 Classification performances measured by the overall accuracy (oa, in %) and kappa index (k) with the classifiers Support Vector Machine (SVM) and k-Nearest Neighbour (kNN) for Fildes subsets (F1 to F4). Testing F1

Training

F1 F2 F3 F4 Avg

oa k oa k oa k oa k oa k

F2

F3

F4

Avg

SVM

kNN

SVM

kNN

SVM

kNN

SVM

kNN

SVM

kNN

– – 96.8 0.96 94.3 0.92 95.1 0.93 95.4 0.94

– – 95.1 0.93 88.3 0.83 86.7 0.82 90.2 0.87

95.6 0.94 – – 87.2 0.83 91.3 0.88 91.4 0.88

96.0 0.95 – – 86.4 0.79 86.5 0.82 89.8 0.86

90.3 0.86 87.8 0.82 – – 87.5 0.82 88.5 0.83

91.7 0.89 86.4 0.81 – – 85.2 0.80 87.8 0.84

89.8 0.86 86.5 0.82 86.8 0.82 – – 87.8 0.83

91.2 0.88 84.4 0.79 86.1 0.79 – – 87.1 0.82

92.0 0.89 90.5 0.87 89.4 0.86 91.3 0.88 90.8 0.88

93.0 0.91 88.8 0.85 87.0 0.80 86.2 0.81 88.8 0.85

and also occupy larger portions of the images dominated by piles of rock clasts.

see a good global agreement between the type of surface and the label assigned by the classifier. The overall classification performances are high for both classifiers: oa of 90.8% (SVM) and 88.8% (kNN) and k of 0.88 (SVM) and 0.85 (kNN). These performances refer to the average of the classifications when each of the 4 subsets is used for training. From the details presented in Table 2, one can see that on average the oa and k values for both classifiers are kept at high standards and that the variations shown with each different training subset are not substantial: for the SVM classifier the average values vary between 86.5 and 96.8% (for oa) and 0.82 and 0.96 (for k), while for the KNN classifier those are a bit smaller but still of the same high level, that is, between 84.4 and 96.0% (for oa) and 0.79 and 0.95 (for k). It is also clear that globally SVM outperforms kNN, and also in the majority of the situations (in 9 out of the 12 classifications performed), both in oa and k values, but without significant discrepancies. A breakdown analysis by class or surface is also important. For the SVM classifier (Table 3), the individual accuracies obtained (both producer and user) by each of the defined classes can be considered very good, with four of them achieving high values, namely above 82–90% (BR, BS, MF1 and MF3). The other two classes (LF and SHA) show high user accuracies (above 85%) and inferior, but still good, producer values (in the range 69–75%). The analysis of the confusion matrix permits to verify the origin of the misclassifications: LF is sometimes confused with BR class, while for SHA the incorrectness comes together with MF2 class (Table 2). The analysis of the images also permits understanding that this confusion comes from the high colour similarity of the strongly illuminated edges of the rock clasts (very white and close to saturation) with some branches of the Usnea (which exhibit the same colour behaviour). For the SHA class, the confusion comes with the class that shows a closer spectral signature, that is, the MF2 constituted by mosses of darker colours; the spectral signature of SHA depends naturally on the illumination conditions at the time of acquisition of the images but also on the geometry and colour characteristics of the background surface, showing this way a wider spectral range, that overlaps partially in feature space with that of the MF2 class. A similar conclusion can be drawn by analysing in detail the omission and commission errors obtained.

For the identification of the moss and lichen formation classes, Øvstedal and Lewis Smith (2001) and KOPRI (2014) were followed. 5. Experimental results The classification of each dataset was performed with both SVM and kNN classifiers in the above mentioned 4-fold cross-validation strategy, hence a total of 12 classifications are performed for each peninsula dataset. The results and breakdown analysis are presented and discussed next. 5.1. Classification of Fildes images A supervised classification scheme is used, so a training phase based on the selection of a given set of samples describing each of the six defined classes must be implemented. These samples, that should take account of the internal variability of each cover type, are constituted by representative objects selected interactively on the computer screen. The training sets for each subset of 10 images were constructed to have a size as similar as possible: each sample contains about 400 objects, which in turn are constituted by a total of around 500k pixels. There is some small variation in the dimensions of the same class from subset to subset, due to the local characteristics of the surfaces. Nevertheless, the proportion of the number of objects in each subset is very similar and small (only around 0.3% of the total number of objects resulting from the segmentation is used), as well as, their respective amount of pixels (only about 4% of the pixels of the whole image are included in each subset). The detailed characteristics of each subset sample for Fields peninsula (F1 to F4) per surface class (number of objects and respective total area in pixels) are presented in Table 1, and show the mentioned well-balanced constitution. Using these samples, a total of 12 classifications with each of the two classifiers were performed. Some examples illustrating the classification with the SVM method are shown in Fig. 6a–c. Visually, one can already

Table 3 Overall confusion matrix in percentage with respective accuracies (%) and errors (%) for SVM classifier in Fildes subsets (F1 to F4)·(Prod—producer, Com—commission, Omi—omission). Class

Classified

BR BS MF1 MF2 LF SHA

Reference

Accuracies

Errors

BR

BS

MF1

MF2

LF

SHA

Total

Prod

User

Com

Omi

94.5 3.6 0.0 1.5 0.4 0.0

10.3 86.2 2.2 1.2 0.0 0.0

0.2 8.8 90.6 0.4 0.1 0.0

1.6 4.7 0.0 92.7 0.0 0.9

29.5 1.2 0.1 0.0 69.1 0.0

4.1 0.1 0.0 20.4 0.1 75.3

37.8 23.5 20.9 15.4 1.2 1.2

94.5 86.2 90.6 92.7 69.1 75.3

91.7 82.8 97.5 92.3 87.2 87.4

8.3 17.2 2.5 7.7 12.8 12.6

5.5 13.8 9.4 7.3 30.9 24.7

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299

Table 4 Overall confusion matrix in percentage with respective accuracies (%) and errors (%) for kNN classifier in Fildes subsets (F1 to F4) (Prod—producer, Com—commission, Omi—omission). Class

Classified

Reference

BR BS MF1 MF2 LF SHA

Accuracies

Errors

BR

BS

MF1

MF2

LF

SHA

Total

Prod

User

Com

Omi

94.0 3.7 0.1 1.4 0.7 0.0

12.5 81.9 3.9 1.6 0.0 0.0

0.6 13.7 85.1 0.5 0.0 0.0

2.8 2.0 0.5 93.5 0.0 1.3

28.1 3.4 0.0 0.0 68.4 0.1

3.3 0.0 0.0 19.3 0.1 77.3

38.8 22.9 18.9 16.9 1.3 1.2

94.0 81.9 85.1 93.5 68.4 77.3

90.2 79.7 94.8 92.5 77.1 82.8

9.8 20.3 5.2 7.5 22.9 17.2

6.0 18.1 14.9 6.5 31.6 22.7

For the kNN classifier (Table 4), the global view is not very different from the one seen with the SVM classifier, with an overall small decrease of all classes. The higher accuracies are given by the same four classes (BS decreased a bit more than the others), while the less favoured ones are shown the same as the other two. The confusion between BS and BR increased marginally, maybe due to a lesser ability of this classifier to deal with the clasts that in some images are coated by tiny particles of soil (dust). 5.2. Classification of Barton images The same strategy adopted for Fildes peninsula images, was repeated for the Barton peninsula dataset. The same 4-fold cross-validation procedure, the same number of classes (six) and the same pair of supervised classifiers (SVM and kNN) with the same parameter values was evaluated. A balanced set of training samples was obtained in the same matter, selecting training samples as similar as possible, that is, with about the same number of objects and occupying the same amount of area. The segmentation in Barton images was also performed with the same parameter values. A somehow higher number of objects were obtained, which in turn are, on average, also a bit smaller on size than those of Fildes. For this reason, a few more objects for Barton (around 470, compared to the ~400 in Fildes) were selected. The relative number of objects and respective area used to construct the reference/training datasets is very similar to those of Fildes examples. The details of the characteristics of each subset are shown in Table 5. The classification outputs show for Barton a rather distinct behaviour of the two decision systems (Table 6). The SVM classifier keeps the same high performance level as before, even also a bit better than in Fildes (91.8% for oa 0.89 for k). The kNN falls to an, although still interesting, inferior performance level (81.3% for oa and 0.73 for k). The details presented in Table 6 for the 12 classifications of the 4-fold cross-validation procedure, show that the decision is now made on two distinct levels: the SVM classifier performance varies between 87.2 and 97.4% (for oa) and 0.82 and 0.96 (for k), while the values for the KNN rate a few points below and in a wider range of variation, that is, between 71.9 and 89.7% (for oa) and 0.62 and 0.83 (for k). In this dataset, SVM outperforms kNN in each of the 12 individual classifications. The individual accuracies obtained by the SVM classifier (both for producer and user) for each class achieve a little higher level than in Fildes peninsula (Table 7). The higher accuracies (above 85–95%) are

again for the same 4 classes (BR, BS, MF1 and MF2). The LF also improves a bit (~ 72%), but on the contrary, the SHA class gets a step down (b 60%). The analysis of the confusion matrix permits verifying the exact same major problems encountered in Fildes datasets: confusion between LF and BR classes and between SHA and MF2 classes. The reasons for this exchange are also the same that were identified in Fildes images and reported earlier. For the kNN classifier, as already seen, the global view in Barton is rather different from Fildes, with a clear decrease in the overall performance (Table 8). Here, only the two bare surface classes (BR and BS) maintain the same high level in the accuracy range of 80–90%. One of the vegetation covers (MF1) achieves the worst performance (range 60–70%), while the other 3 classes (MF2, LF and SHA) lie in the middle ranges (around 70%). Although these are not disastrous performances they cannot compare with those obtained with the SVM classifier.

6. Discussion For Fildes, the individual dispersion between different testing subsets (when the training subset is the same) is not significantly high between the 2 classifiers: the range between the best and worst performances is about 10% (oa) and 0.14 (k) for the SVM classifier and a bit wider for the kNN based decision, namely 12% (oa) and 0.16 (k). These low variations are a good indicator of the quality achieved by the method, which in addition also points to an adequate representativeness of the training samples used. The same set of objects that one time is used only for training the classifier and on other three times is used only for validating the procedure with such high performances deserves that recognition. The experimental results highlight that the SVM classifier always performs better than the kNN classifier in each of the 12 classifications, both for the oa percentages and the k values. In 10 of these classifications the differences are very small (below 3% for oa and below 0.03 for k), while in the only 2 remaining situations the difference calls a bit more the attention, but still without being significant: differences of 4.6% (oa) and 0.07 (k) (when F3 is used for training and F1 for testing) and of 7.5% (oa) and 0.11 (k) (F4 training with F1 testing). Since these bit higher deviations are due to the decrease of the kNN performances (SVM keeps the performances around the same values), one may think that these particularly lower achievements are due to an

Table 5 Characteristics of the training samples for each subset of images (B1 to B4) for Barton Peninsula. Classes\subsets

BR BS MF1 MF2 LF SHA Total (number) Total (%)

B1

B2

B3

B4

Objects

Pixels

Objects

Pixels

Objects

Pixels

Objects

Pixels

162 52 44 46 124 46 474 0.29

276,464 100,604 42,752 86,474 16,462 24,870 547,626 4.7

167 54 83 42 102 33 481 0.28

237,622 88,860 19,838 154,922 8992 24,338 534,572 4.6

170 63 46 41 99 41 460 0.25

290,645 129,492 46,657 113,390 4773 11,365 596,322 5.1

175 59 41 43 119 47 484 0.27

215,736 127,521 92,585 57,858 5064 22,030 520,794 4.5

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Table 6 Classification performances given by the overall accuracy (oa, in %) and kappa index (k) with the classifiers Support Vector Machine (SVM) and k-Nearest Neighbour (kNN) for Barton subsets (B1 to B4). Testing B1

Training

B1 B2 B3 B4 Avg

oa k oa k oa k oa k oa k

B2

B3

B4

Avg

SVM

kNN

SVM

kNN

SVM

kNN

SVM

kNN

SVM

kNN

– – 92.3 0.89 91.6 0.87 90.7 0.86 91.5 0.87

– – 77.2 0.70 86.4 0.79 81.2 0.73 82.3 0.75

95.8 0.94 – – 89.3 0.84 90.1 0.86 91.7 0.88

89.7 0.83 – – 80.4 0.72 80.7 0.70 86.2 0.78

90.4 0.86 87.2 0.82 – – 91.0 0.88 89.6 0.85

81.2 0.68 71.9 0.63 – – 78.7 0.71 77.8 0.69

97.4 0.96 88.1 0.84 95.6 0.94 – – 94.3 0.92

80.5 0.71 72.8 0.62 78.4 0.70 – – 78.0 0.69

94.0 0.92 88.9 0.85 93.0 0.90 90.6 0.87 91.8 0.89

85.5 0.76 73.7 0.65 81.6 0.74 80.1 0.72 81.3 0.73

inability of the classifier itself and not to the construction of the training samples. For Barton, the situation is very similar for the SVM classifier and a bit distinct for the kNN decision system: the range of variation between extreme performances are about the same as in Fildes for the SVM method (10% for oa and 0.14 for k), and worse for kNN classifier (18% for oa and 0.21 for k). Globally, and looking only for the classifier that performs at high level in both peninsulas (the SVM classifier), this cross-validation procedure allows verifying the robustness of the processing sequence. First, when the same subset is trained by different subsets the average values obtained (bottom line of Table 2), show high values with very acceptable ranges (oa in 88–95% and k in 0.83–0.94). Second, when the same subset is used for testing the other subsets (averages on the right column of Table 2), its ability to provide adequate information to several subsets, also with a lower dispersion range (oa in 89–92% and k in 0.86–0.89), is reinforced. The classification method proposed is able to deal with relatively large variations in the intrinsic colour properties of the surfaces, since the images were captured in different ice-free areas (Fildes and Barton peninsulas), in different years (January 2012 and January 2013) and at different hours of the day (in periods normally between 11:00 and 16:00, local time GMT-3). Anyhow, care should be taken on their acquisition, in whatever environmental conditions. Images captured in extreme solar intensity conditions (foggy, low and dense cloud covers, clear blue sky or at very early/late hours of the day) should be avoided, especially if simple cameras are being used, since the camera acquisition settings are limited or inexistent (like the one used with the field equipment that is fully automatic). The images captured in these conditions normally show low contrast and narrow colour dynamics (too dark or close to saturation) and pose additional discrimination difficulties to the algorithm. Nevertheless, the cloudy skies were the most common situation during daytime in our sites and, when they were not too low, the quality of the images was good enough. The estimated fraction of each surface on our datasets (shown in Tables 3–4 and 7–8) must not be extrapolated and used as an indicator of the occupation of each cover type in each of the peninsulas. Although

the large majority of pixels in these images (about two-thirds) correspond to bare surfaces (rocks and soils) and naturally derive from what is perceived on the ground, anything else than these bare surfaces that currently dominate the ice-free regions of this Antarctic area can be inferred. The images acquired in the field did not follow any sampling procedure designed to estimate the percentage of each type at local or regional scale (at the dimension of the ice-free regions of the peninsulas), nor the construction of the subsets of images intended to be precisely representative of the areas occupied by each type. It should be reinforced that what it was really intended to verify is the extension of the robustness and ability of the method to discriminate the highest number and variety of surface types at ground level. Therefore, these values are only valid for testing the methodology and for illustrating the type of output that can be used for further processing, namely, for providing an accurate amount of spectral mixture at each individual image or location which, for instance, represents the highest panchromatic scale of the current VHR satellite imagery (spatial resolutions of 0.40–0.50 cm/pixel). The use of the detailed imagery obtained at ground level together with satellite imagery must be done with the same care as with any other reference data (Congalton and Green, 2008). Since the output of this approach is a relative value (percentage of area occupied by each surface type in a given support) and the flat surfaces under analysis (vegetation, soils and rocks) show a relatively slow dynamics (except, for snow and water), the ground information and satellite imagery do not need being perfectly contemporary. Phenological changes in the vegetation formations are minor and if imagery is obtained late in the snow melt season, variability will be minimal. The current approach will also allow quantifying how long that temporal discrepancy is acceptable, providing higher details and in more locations of the dynamics of the Antarctic landscape, when compared to other field monitoring techniques (Selkirk and Skotnicki, 2007; Victoria et al., 2009, 2013; Bohuslavová et al., 2012; Shin et al., 2014). Another important issue to be addressed is when new samples are acquired in the field from other regions, other years or with different environmental conditions not yet represented in the classification model. This implies an updating of the model with the new training samples. In

Table 7 Overall confusion matrix in percentage with respective accuracies (%) and errors (%) for SVM classifier in Barton subsets (B1 to B4) (Prod—producer, Com—commission, Omi—omission). Class

Classified

BR BS MF1 MF2 LF SHA

Reference

Accuracy

Errors

BR

BS

MF1

MF2

LF

SHA

Total

Prod

User

Com

Om

96.0 1.4 1.1 1.0 0.5 0.0

1.1 97.5 1.3 0.0 0.0 0.2

0.3 7.0 85.2 7.4 0.1 0.1

3.4 0.0 0.2 88.2 0.0 8.2

21.6 0.1 4.3 1.4 71.4 1.1

0.7 0.3 0.5 40.9 0.3 57.3

43.1 22.3 10.1 19.3 1.5 3.6

96.0 97.5 85.2 88.2 71.4 57.3

97.0 93.8 91.3 85.8 82.6 56.8

3.0 6.2 8.7 14.2 17.4 43.2

4.0 2.5 14.8 11.8 28.6 42.7

P. Pina et al. / Science of the Total Environment 573 (2016) 290–302

301

Table 8 Overall confusion matrix in percentage with respective accuracies (%) and errors (%) for the kNN classifier in Barton subsets (B1 to B4) (Prod—producer, Com—commission, Omi—omission). Class

Classified

BR BS MF1 MF2 LF SHA

Reference

Accuracy

Errors

BR

BS

MF1

MF2

LF

SHA

Total

Prod

User

Com

Om

89.6 3.3 0.1 2.3 4.6 0.1

11.9 84.2 3.3 0.5 0.1 0.0

10.6 19.9 59.6 7.6 1.9 0.3

6.6 0.1 1.0 70.5 0.3 21.4

17.5 0.7 5.7 0.8 74.3 0.8

1.0 0.5 1.1 25.6 0.5 71.4

47.9 14.2 4.5 15.3 7.4 10.8

89.6 84.2 59.6 70.5 74.3 71.4

90.6 80.3 74.4 71.9 66.7 67.9

9.4 19.7 25.6 28.1 33.3 32.1

10.4 15.8 40.4 29.5 25.7 28.6

the traditional way, a new classification model is built including all the samples, the old and the new ones. This time consuming procedure has to be repeated every time a new sample is available. A solution for this problem can be obtained by the use of adaptive incremental learning techniques (Bruzzone and Prieto, 1999; Ruiz et al., 2014; Ristin et al., 2016), in which the learning system adjusts continuously its parameters and its structure to the new samples, without the necessity of rebuilding the entire model from scratch each time a new pattern needs to be added. These techniques aim at keeping the detection performances (minimizing the errors) and thus enable growing the dimension of the training set without great computational efforts. The improvement of the current approach with incremental learning tasks is foreseen and will permit iteratively building richer models containing the largest possible variability of the ice-free surfaces of Antarctica. 7. Conclusions A robust methodology to obtain precise ground reference data in images acquired at ground level in ice-free regions in Antarctica is presented. The diversity of surface types, areas of testing, distinct years and time periods of acquisition of the images are successfully managed by one supervised classifier (the SVM) in the object-based strategy. It is the a-priori delineation of homogeneous regions (the objects) and the possibility of extracting a larger number of features (morphometric, colour and textural) when compared to a pixel based-approach, which permits achieving these high performances. In particular, the SVM classifier also demonstrated that it outperforms concurrent alternatives with the same high values in different datasets and in different operational situations. The strengths of the approach lie on the combination of the type of information used (simple colour images obtained by common digital cameras) and the relatively straightforward preparation of the supervised processing sequence. The segmentation can be coarsely tuned with few parameters, the number and type of features describing the objects does not need to be very focused on specific types and the amount of representative samples needed to adequately train the classifier is relatively very small. At the same time, the low spectral resolution (only colour) and the use of standard photographic cameras requires some care in extreme environmental conditions, to avoid acquiring poorly contrasted and unusable images. It must be emphasized that although the method presented is applied now to RGB images and six surface types in ice-free areas of Maritime Antarctica, its application is not limited to this configuration and can be considered for a much more generalized practice. Neither is it limited to colour images nor to these specific classes. It can be applied to images constituted by a higher number of spectral bands (multispectral) and consequently to a higher number of surface covers. The increase of spectral information should allow discriminating different types of rocks, soils, mosses or lichens. The increase in the amount of spectral data or/and in the number of classes would naturally require a higher computational effort. Moreover, due to the object-based conception of the classification (with the extraction of robust descriptors of the homogeneous regions of the image, no matter what surface type they represent) and to the supervised learning strategy (any new

class will be described in advance to the decision system), this method can be considered suitable for applying in any other type of surface or landscape of our planet. For immediate future work, it is also intended to incorporate the more precise outputs of the method now presented as reference surface data in the training and testing of remotely sensed imagery in a wide range of spatial resolutions and acquired by a diversified type of platforms (UAV but also satellites) in order to improve the quality of the resulting thematic mapping products. This is a very useful and robust tool to aid in the better characterization of the ice-free surfaces of Antarctica at several landscapes scales. Not only can it provide detailed surface cover at the microscale and hence be used to precisely detect multi-temporal changes, but it also can in turn be helpful to improve the accuracy and level of discrimination of the classification of remotely sensed imagery, in regions where field work is seldom performed or inaccessible, by substituting the traditional single point sampling description of each location by an accurate proportion in a given support area.

Acknowledgements This contribution was developed in the frame of the projects SNOWCHANGE, HISURF1 and HISURF2 from PROPOLAR/FCT-Portugal and PERMANTAR-3 (PTDC/AAG-GLO/3908/2012) from FCT-Portugal. Thanks are also due to INACH — Chile for support at the Escudero station and logistics in Fildes peninsula and to KOPRI — South Korea for support at King Sejong station and logistics in Barton peninsula.

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