Jeffries Matusita based mixed-measure for improved spectral matching in hyperspectral image analysis

International Journal of Applied Earth Observation and Geoinformation 32 (2014) 138–151

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Jeffries Matusita based mixed-measure for improved spectral matching in hyperspectral image analysis S. Padma ∗ , S. Sanjeevi Department of Geology, Anna University, Chennai 600025, India

a r t i c l e

i n f o

Article history: Received 4 February 2014 Accepted 4 April 2014 Available online 4 May 2014 Keywords: Spectral matching Hyperspectral image Jeffries-Matusita Spectral Angle Mapper Classification

a b s t r a c t This paper proposes a novel hyperspectral matching technique by integrating the Jeffries-Matusita measure (JM) and the Spectral Angle Mapper (SAM) algorithm. The deterministic Spectral Angle Mapper and stochastic Jeffries-Matusita measure are orthogonally projected using the sine and tangent functions to increase their spectral ability. The developed JM-SAM algorithm is implemented in effectively discriminating the landcover classes and cover types in the hyperspectral images acquired by PROBA/CHRIS and EO-1 Hyperion sensors. The reference spectra for different land-cover classes were derived from each of these images. The performance of the proposed measure is compared with the performance of the individual SAM and JM approaches. From the values of the relative spectral discriminatory probability (RSDPB) and relative discriminatory entropy value (RSDE), it is inferred that the hybrid JM-SAM approach results in a high spectral discriminability than the SAM and JM measures. Besides, the use of the improved JM-SAM algorithm for supervised classification of the images results in 92.9% and 91.47% accuracy compared to 73.13%, 79.41%, and 85.69% of minimum-distance, SAM and JM measures. It is also inferred that the increased spectral discriminability of JM-SAM measure is contributed by the JM distance. Further, it is seen that the proposed JM-SAM measure is compatible with varying spectral resolutions of PROBA/CHRIS (62 bands) and Hyperion (242 bands). © 2014 Elsevier B.V. All rights reserved.

1. Introduction Spectral signatures or spectral fingerprints characterize the materials based on the absorptance, reflectance and transmittance of the electromagnetic radiation. These signatures are simply the plots of the spectral reflectance of an object as a function of wavelength (Lillesand et al., 2007). Efficient utilization of information from the spectral signatures is achieved through the spectral matching methods (Homayouni and Roux, 2004). This method involves the characterization of an unknown or target pixel by matching the spectra of that pixel with the spectra of a set of reference or known pixels present in a spectral library. The matching algorithm defines the manner in which the unknown and known spectra are compared (Vishnu et al., 2013). Spectral matching algorithms exhibit better performance than the anomaly detectors, because they look for specific spectrally defined targets (Manolakis et al., 2009). With the advent of hyperspectral datasets which have narrow and contiguous bands, each image pixel vector possesses

∗ Corresponding author. Tel.: +91 9445452883. E-mail addresses: [email protected], [email protected] (S. Padma). http://dx.doi.org/10.1016/j.jag.2014.04.001 0303-2434/© 2014 Elsevier B.V. All rights reserved.

richer spectral information than the multispectral datasets (Du and Chang, 2001). In recent times, spectral matching algorithms have been developed to adapt and extract information from these real-time hyperspectral datasets. Though several spectral matching approaches were developed to increase the information extraction from hyperspectral data, each of these methods has its own limitation in utilizing the band-level information and their performances is not of acceptable quality. Further the Jeffries-Matusita distance has only been used in the context of image classification and not in spectral matching. Hence, the aim of this paper is to develop and demonstrate an algorithm and approach for efficient spectral matching in hyperspectral image datasets. The novelty of this combined approach is in the incorporation of JM distance as a measure of spectral match and using it in conjunction with a deterministic approach Spectral Angle Mapper (as a mixed measure). To have a better understanding of the performance of the proposed spectral matching technique, it is implemented on the hyperspectral datasets namely PROBA/CHRIS (62 bands) and Hyperion (224 bands). The efficiency of the developed algorithm to extract information from different sets of hyperspectral data is assessed. As a contribution to the study of mangrove ecosystem science, the JM-SAM mixed measured is used in the analysis of Pichavaram

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mangroves in Southern India. Coastal ecosystem study is perhaps one of the main beneficiaries of the application of hyperspectral data and spectral matching techniques. In the Indian context, several studies on the mangrove ecosystem utilized the multispectral data for analysis (Selvam et al., 2003, 2010; Ajithkumar et al., 2008). However, it is difficult to map such a complex mangrove ecosystem at species level. The hyperspectral images are efficient for such mapping different species with closely matching spectra pose a challenge (Du et al., 2004; Dudeni and Debba, 2009). Hence, it is believed that to accurately map all species of the mangrove ecosystem and to have a better understanding of such system, the proposed JM-SAM mixed novel approach will be a useful tool. The traditional Spectral Angle Mapper (SAM) (Kruse et al., 1993) is a deterministic matching measure which is commonly used for material identification. It is simply the measure of dot-product angle between the target and reference spectra. The stochastic Jeffries-Matusita distance measure (JM) (Swain and King, 1973; Bruzzone et al., 1995) is used to quantify the spectral separability in target detection. Unlike the Transformed Divergence (TD) which calculates the divergence as a function of normalized distance between two classes, the JM measure represents the average distance between two class density functions. The combination of the deterministic Spectral Angle Mapper (SAM) and stochastic Jeffries-Matusita measure yields a new hyperspectral matching technique, called JM-SAM measure, which is proved to utilize the band-wise spectral information for precise spectral signature identification. 2. Need for spectral matching Many spectral matching algorithms, ranging from the traditional clustering techniques to the highly sophisticated models such as Constrained Energy Minimization (Harsanyi, 1993), Adaptive Coherence Estimator (Kraut and Scharf, 1999), Artificial Neural Network based matching such as Hopfield Recurrent Neural Network (Cantero et al., 2004) and Self Organizing Feature Maps (Yang et al., 2011) have evolved in the recent years. An increasing demand in using spectral imagery to detect specific objects and targets of interest is observed as each spectral signature bears the distinct characteristics of the various earth materials (Guo and Osher, 2011). Further, the availability of huge spectral data through hyperspectral imaging leads to the development of automated spectral matching techniques that can derive thematic maps from satellite imagery (Vishnu et al., 2013). Many hyperspectral image datasets are becoming available and are being constantly used for various applications. Though the spectral signature matching measures were initially utilized for mineral identification (van der Meer and Bakker, 1997; Drake et al., 1999), they are now being applied in a range of fields such as identification of natural salt crusts (Howari, 2003), ocean bathymetry modeling (Louchard et al., 2003), oil spill detection (Andreoli et al., 2007), vegetation-species discrimination (Dudeni et al., 2009) and time-series based land-cover classification (Gupta and Rajan, 2010). Researches on lunar geological feature mapping (Evans, 2007) and the advent of planetary spectral databases like NASA’s Compact Reconnaissance Imaging Spectrometer for Mars (CRISM) and Arizona State University’s Thermal Emission Spectral Library (TES) also stand as evidence to the need for matching techniques. 3. Need for combined algorithms in spectral matching Spectral matching methods are continuously evolving to suit the constraints of dealing with the hyperspectral datasets. One way to improve the performance of the measure is by modifying

139

its components. This can be seen in the Modified Spectral Angle Mapper (MSAM) (Staenz et al., 1999), which is the improved version of the Spectral Angle Mapper (SAM) (Kruse et al., 1993). The insensitivity of the shape measure-SAM to illumination effects is overcome by including the hyper angle component in the MSAM measure, which considers both the shape and magnitude of the spectra. Normalized Euclidean Distance (Robila and Gershman, 2005) and Cross Correlogram Spectral Matcher-Continuum Removed (van der Meer, 2000) are some of the other examples of modifying an existing approach for a better performance. Apart from modifying an existing approach, the spectral capabilities of two quantitative algorithms are combined to increase the efficiency of matching. Examples include the Spectral Similarity Value (Homayouni and Roux, 2004; Granahan and Sweet, 2001) and Normalized Spectral Similarity Score (Nidamanuri and Zbell, 2011) which combine the spectral angle (SAM) and amplitude component of the Euclidean Distance (ED). Vishnu et al. (2013) also confirm that combination of methods results in increased precision in identifying spectrally similar materials. The success of a matching method rests on its ability to discriminate subtle differences in samples that are inherently similar (Li et al., 2006). The stochastic or divergence measures provide a better spectral characterization compared to the deterministic approaches. This was evident when the mixed measure of Spectral Information Divergence-Spectral Angle Mapper (SID-SAM) developed by Du et al. (2004) displayed an increased accuracy in the characterization of Savannah tree species (Dudeni and Debba, 2009) and in automatic end-member extraction (Li and Zhang, 2011). On these lines, the hybrid measure of Spectral Information Divergence-Spectral Correlation Angle (SID-SCA) was proposed by Naresh Kumar et al. (2011) to improve the discrimination of crop types. Another example of such a combination is the Hidden Markov Model-Information Divergence (HMMID), which outperformed the Spectral Information Divergence (Du and Chang, 2001). Further, Homayouni and Roux (2004) state that the fusion of Spectral Similarity Value (SSV), Constrained Energy Minimization (CEM) and Modified Spectral Angle Measure (MSAM) will increase the matching accuracy of material mapping. Though combination of two or more approaches is suggested, it is to be mentioned that most of the existing algorithms do not incorporate signature separability to quantify spectral match. In this paper, we propose to incorporate such measures.

4. Spectral Angle Mapper (SAM) The deterministic Spectral Angle Mapper is a measure of the spectral angle between the target spectrum and the reference spectra (Kruse et al., 1993; Staenz et al., 1999; Robila and Gershman, 2005; Nidamanuri and Zbell, 2011; Singh et al., 2012). A smaller angle is obtained in the case of higher similarity between the spectra. SAM measure is also called as the dot-product angle between the spectral vectors. SAM () measured between the spectra S1 and S2 along the wavelength  is given as:

⎡  = cos−1 ⎣ 



⎤ S1 ()S2 () d

 12 

S1 () d



⎦

(1)

S2 ()

Though SAM captures the intrinsic properties of materials in terms of spectral angle (Vishnu et al., 2013), it is insensitive to illumination effects. Hence, SAM has difficulty in identifying spectrally similar materials. Several improvisations have been made to the basic SAM measure, and it is used in combination with the stochastic divergence measures (Du et al., 2004).

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5. Signature separability as a measure of spectral match The objective of spectral or signature separability and spectral matching are considered to be similar in the context of target identification. The distance between the classes in the signature separability process determines the nature of the separability measure. This least separable distance between the classes is similar to the least probabilistic or geometrical measure between the reference and target spectra during spectral matching. It is observed that Divergence is a pair-wise distance measure and ‘m’-wise (m > 2), generalization does not exist. While computing Divergence or Transformed Divergence, the a priori probability of each spectral class is used as weights. The limitation of this approach is that Divergence behaves as a function of normalized distance between the classes and it differs considerably from the behavior of the probability of the correct match (Swain and Davis, 1978). Alternatively, the Jeffries-Matusita (JM) distance between the two spectral classes represents a measure of the average distance between the two class density functions. The presence of the exponential factor in the equation representing the JM distance (Eq. (3)), gives an exponentially decreasing weight to increasing separation between the spectral classes. Such an approach overcomes the limitation of Transformed Divergence. Further, it is observed that the JM measure is an attractive method if class separation distances are of interest and possess good feature ranking for two class comparisons (Laliberte et al., 2012). Hence, this study attempts to model the JM measure like the Spectral Information Divergence to extract the band-wise information content for matching the spectra. The JM measure for assessing non-normally distributed classes (Chang, 2003; Ghiyamat et al., 2013) is used to avoid the limitation of extending the measure to small sample sizes (Laliberte et al., 2012). 5.1. Jeffries-Matusita distance measure Jeffries-Matusita distance is one of the spectral separability measures commonly used in remote sensing applications. According to Swain et al. (1971), JM distance provides a much reliable criterion because as a function of class separability, it behaves much more like probability of correct classification. The probability density of the spectral vectors, S1 and S2 for the bands (l = 1, 2,. . ., L) is pl and ql and the JM distance (Swain et al., 1971; Chang, 2003; Ghiyamat et al., 2013) is given as:



L



2 JMD(S1 , S2 ) =  pl − ql

(2)

l=1

If the pl and ql are of normal distribution, then the JM distance is represented as: JM(S1 , S2 ) = 2(1 − e−B )

(3)

where Bhattacharya distance (B) measuring the mean (M) and variance (V) of the spectral vectors is defined as: B(S1 , S2 ) =



VS1 + VS2 1 (Ms1 − Ms2 )T 8 2



+

1 ln 2

|VS1 + VS2 /2|

|VS1 ||VS2 |

−1

(Ms1 − Ms2 )

Jeffries-Matusita distance is similar to the SID measure as it measures the band-wise information between the spectral vectors (Chang, 2003). 6. Development of the proposed combined spectral matching algorithm After a detailed review of the various spectral matching measures, the combined measure of Jeffries-Matusita distance (JM) and Spectral Angle Mapper (SAM) is proposed. The proposed algorithm (JM-SAM) is developed by combining the deterministic Spectral Angle Mapper (SAM) and the stochastic Jeffries-Matusita Measure (JM) by the tangent and sine functions. The tangent and sine trigonometric functions are used to calculate the perpendicular distance between the target and reference (S1 and S2 ), respectively, instead of the cosine function which projects one spectrum along the other (Du et al., 2004; Naresh Kumar et al., 2011). The JM-SAM algorithm is measured as: JM-SAM(TAN) = JM(S1 , S2 ) × tan(SAM(S1 , S2 ))

(5)

JM-SAM(SIN) = SID(S1 , S2 ) × sin(SAM(S1 , S2 ))

(6)

While the spectral angle component of SAM provides increased match for distinct materials, the self-information component of JM improves the detection of spectrally similar materials. The algorithm considers the geometrical aspects (angle, distance) and band-information between the spectral vectors. The best match in JM-SAM algorithm is characterized by having the least-separable distance between the spectral vectors at each band along with the least spectral-angle between the vectors. The performance of this matching measure is compared with the individual measures of SAM and JM based on their respective relative spectral discriminatory probability (RSDPB) (Du et al., 2004) and relative spectral discriminatory entropy (RSDE) (Du et al., 2004). 7. Performance measures The performances of the spectral matching algorithms in matching the target and reference spectra are evaluated by the following measures. 7.1. Relative spectral discriminatory probability (RSDPB) The relative spectral discriminatory probability (RSDPB) (Chang, 2003; Du et al., 2004; Dudeni et al., 2009) is the measure of likelihood of the identification of the target signature ‘t’ from a set of spectral signatures or spectral library, . It is simply the normalized distance determined to estimate the similarity between the target and the respective reference spectra. For a spectral library , comprising of ‘K’ signatures (S1 , S2 ,. . ., Sk ), the RSDPB measure (Chang, 2003) is given as: pm (k) = t,



m(t, Sk )

L

j=1

(4)

In the present study, the spectral vectors chosen for matching are of non-normal distribution nature. Hence the basic equation (Eq. (3)) may not result in accurate matching (Ghiyamat et al., 2013). Eq. (4) is used for the non-normally distributed spectral densities (Chang, 2003; Ghiyamat et al., 2013). Here the

L

m(t, Sj )

(7)

where m(t, Sj ) is the normalization constant determined by j=1 the spectral matching measures in identifying target ‘t’ from the set of reference spectra or spectral library ‘’. m(t, Sk ) is the spectral matching measure between the target spectra relative to the reference spectra Sk in the library . From the resulting RSDPB vec(S ), pm (S ),. . ., pm (S )]T , the reference unit with a least tor, [pm t, 1 t, 2 t, k relative probability is assumed as the best match for the target.

S. Padma, S. Sanjeevi / International Journal of Applied Earth Observation and Geoinformation 32 (2014) 138–151 Table 1 Specifications of the hyperspectral images used in the study.

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Table 2 Skewness and Kurtosis measures for the extracted spectral signatures.

Sensor parameters

PROBA/CHRIS

EO-1 Hyperion

Spectral signature

Skewness

Kurtosis

Spectral range Spatial resolution Radiometric resolution Swath width Spectral resolution Spectral coverage Date of acquisition Number of bands Format of HDF data

0.4–1.05 ␮m 34 m 12 bits/pixel 14 km 12 nm Continuous June 04, 2003 62 BSQ

0.4–2.5 ␮m 30 m 16 bits/pixel 7.5 km 10 nm Continuous January 03, 2013 242 (pre-corrected) BSQ

PROBA/CHRIS Dense scrub Sparse scrub Weathered rock Moist sand Clear/deep water Turbid/shallow water Shadow

0.107 0.195 −0.253 −0.331 0.958 1.231 1.843

−1.628 −1.634 −1.372 −0.640 −0.520 0.358 2.370

Hyperion Avicennia Rhizopora Paddy Groundnut Mudflat Sand Clear/deep water Turbid/shallow water

1.210 1.463 0.825 0.817 −0.099 −0.884 1.372 1.164

0.326 0.956 −0.446 −0.442 −1.137 0.859 0.461 −0.389

7.2. Relative spectral discriminatory entropy (RSDE) The performance of the spectral matching algorithms is further assessed by a measure called relative spectral discriminatory entropy (RSDE). Using the RSDPB vector [pm (S ), pm (S ),. . ., t, 1 t, 2 (S )]T , the RSDE (Chang, 2003; Du et al., 2004; Dudeni et al., pm t, k 2009) measures the uncertainty of matching the target spectra (t) with the reference spectra in the spectral library (). The RSDE measure is given as: K

m HRSDE (t, ) = −

pm (S )log2 pm (S ) t, K t, K

(8)

k=1 m It is to be noted that, larger the value of HRSDE (t, ), smaller is the chance of identifying target ‘t’ from the set of reference spectra in the library .

8. Implementation of the JM-SAM algorithm to PROBA/CHRIS and Hyperion images Having developed the JM-SAM combined algorithm, its potential is demonstrated by applying it to two sets of hyperspectral image data, namely PROBA/CHRIS and EO-1 Hyperion. The workflow involves of developing the JM-SAM algorithm, followed by the implementation of the algorithm for the hyperspectral datasets in the MATLAB environment. The performance of the algorithm compared to the SAM and JM measures based on the values of relative spectral discriminatory probability (RSDPB) and the relative spectral discriminatory entropy (RSDE) is discussed in Section 8.5 (Fig. 1). 8.1. Hyperspectral datasets 8.1.1. PROBA/CHRIS image The atmospherically corrected PROBA/CHRIS dataset with 62 bands of the Lake Argyle region, Western Australia is used in this study (Table 1 and Fig. 2). Lake Argyle is Australia’s largest expanse of freshwater covering an area of more than 900 square kilometers with rich flora and fauna. The region experiences a dry tropical climate with rainfall being monsoonal. The average maximum temperatures range from 38 ◦ C in December to 30 ◦ C in July. Due to the presence of wetlands, Lake Argyle is recognized as a Ramsar protected wetland site from 1990 (Hale and Morgan, 2010). 8.1.2. Hyperion image The EO-1 Hyperion image (Table 1 and Fig. 3) of Pichavaram mangrove region is Tamil Nadu, Southern India is used in this study. There are 13 species of mangroves such as Avicennia and Rhizophara that occur in this region along with horticulture and crop-land. Pichavaram is dominated by these two species which constitute around 89% of the total population of mangroves. The climate is sub-humid with maximum rainfall during the northeast monsoons

(Selvam et al., 2003). The Rhizopora zone, as a narrow strip along the tidal creeks and channels occurs at breadth of 4 m, while the Avicennia zone is of 90 m. Groundnut and paddy are the major crops cultivated in Pichavaram (Selvam et al., 2002). 8.2. Pre-processing and extraction of spectra The preprocessing of the PROBA/CHRIS and Hyperion dataset is carried out using the FLAASH module available in ENVI image processing package. This tool converts the radiance measured by the sensor into reflectance values. The differences between the raw and atmospherically corrected spectra of the various landcover classes obtained from PROBA/CHRIS and Hyperion datasets are depicted in Figs. 4 and 5, respectively. From the PROBA/CHRIS data of the Lake Argyle region, seven spectral classes (dense scrub, sparse scrub, weathered rock, moist sand, clear/deep water, turbid/shallow water and shadow) are chosen. These seven spectral samples represent the fundamental landcover types of the Lake Argyle region. The FLAASH corrected spectral signatures of these seven classes are shown in Fig. 4. Similarly, for the Hyperion image of the coastal region of Pichavaram, Southern India, eight spectral classes (Avicennia, Rhizopora, paddy, groundnut, mudflat, sand, clear/deep water and turbid/shallow water) were identified and chosen. These spectral samples represent the rich and complex nature of the mangrove ecosystem. The FLAASH corrected spectral signatures of these eight classes are shown in Fig. 5. 8.3. Test for non-normality The mean signature vectors extracted from the PROBA/CHRIS and Hyperion datasets are considered to be non-normal in nature. The skewness and kurtosis measures for the signature vectors are calculated to ensure the non-normality in distribution (Ghiyamat et al., 2013). Since the JM measure used in this study performs accurately for non-normally distributed vectors, these tests were carried out to ensure the precision of such implementation. The skewness and kurtosis measure for the spectral vector is displayed in Table 2. 8.4. Implementation of the spectral matching algorithm 8.4.1. Pixel-based analysis The spectral comparisons, calculation of matching values and plotting of RSDPB and RSDE measures are done using MATLAB and Microsoft Excel packages. The seven spectral signatures (dense

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Hyperspectral Image (PROBA/CHRIS, Hyperion)

Pre-processing (Radiometric correction, Bad band removal)

Extraction of spectral signatures of landcover classes

Assessing non-normality of landcover classes

Development of the JM-SAM combined algorithm SAM

JM Implementation of algorithms on the hyperspectral signatures

Comparison

Pixel based analysis (RSDPB and RSDE)

Image classification and Post-classification accuracy

Performance Evaluation of Spectral Matching Algorithm Fig. 1. Flowchart depicting the development, implementation and evaluation of spectral matching algorithm.

scrub, sparse scrub, weathered rock, moist sand, clear/deep water and turbid/shallow water) derived from the PROBA/CHRIS image are compiled to form a spectral library (reference spectra). The similarity and dissimilarity of each of these spectra from the other members of the spectral library is estimated for SAM, JM and the JM-SAM methods using Eqs. (1), (2), (5) and (6), respectively. The spectral matching values of each target with respect to the other members in the library are presented in Table 3. The likelihood of identifying the target spectra with respect to each reference spectra in the library is measured as RSDPB through Eq. (7) and the

respective plots are presented in Fig. 6. RSDE measures of the spectral matching algorithms, estimated using Eq. (8) are presented in Fig. 8. In the Hyperion dataset, eight spectral signatures (Avicennia, Rhizopora, paddy, groundnut, mudflat, sand, clear/deep water and turbid/shallow water) are used for of spectral matching. The spectral matching measures computed using Eqs. (1), (2), (5) and (6) are presented in Table 4. The RSDPB and RSDE plots of the spectral matching algorithms calculated using Eqs. (7) and (8) are presented in Figs. 7 and 9, respectively.

Fig. 2. Left: Map showing the location of Lake Argyle region, Australia; Right: PROBA/CHRIS image of Lake Argyle, FCC (R = B38, G = B23, B = B18). (For interpretation of the references to color in this figure legend, the reader is referred to the web version of the article.)

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Fig. 3. Left: Map showing the location of Pichavaram region, south India; Right: Hyperion image of Pichavaram, FCC (R = B40, G = B30, B = B20). (For interpretation of the references to color in this figure legend, the reader is referred to the web version of the article.)

a

b

80 70

6000 Reflectance (\100) %

60 DN values (12 bits)

7000

50 40 30 20

5000 4000 3000 2000 1000

10

0

0 0

10

20 30 40 Band Number

50

410

60

510

610

710

810

910

Wavelength (in nm)

Moist Sand Clear/Deep Water Turbid/ Shallow Water

Dense Scrub Sparse Scrub Weathered Rock

Shadow

Fig. 4. Illustration of the reference spectra of landcover types in Lake Argyle region obtained from PROBA/CHRIS image. (a) Before and (b) after application of FLAASH module.

8.4.2. Spectral matching based image classification Apart from the pixel-based matching, the consistency of the developed algorithm’s performance for a spatial extent is illustrated through image classification.

5000

6000

4000

5000

3000 2000 1000

Water Absorption Bands

7000

Water Absorption Bands

b

6000

Reflectance (\100) %

DN Reflectance (16 bits)

a

In the case of PROBA/CHRIS dataset, the spectral signatures (dense scrub, sparse scrub, weathered rock, moist sand, clear/deep water and turbid/shallow water) are used as reference library. Each pixel in the dataset (totally 139,128) is considered as a

4000 3000 2000

0

1000 0

50

100

150

200

400

Band Number

Avicennia Rhizopora Paddy

900

1400

1900

2400

Wavelength (in nm)

Groundnut Mudflat Sand

Clear /Deep Water Turbid/Shallow Water

Fig. 5. Illustration of the reference spectra of landcover types in Pichavaram region obtained from Hyperion image. (a) Before and (b) after application of FLAASH module.

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0.4

0.45 0.4

0.35

0.35

RSDPB value

RSDPB value

0.3 0.25 0.2 0.15 0.1 0.05

0.3 0.25 0.2 0.15 0.1 0.05

0

0 DS-SS

DS-WR

DS-MS

DS-CW

DS-TW

DS-SH

SS-DS

0.4

0.4

0.35

0.35

0.3

0.3

RSDPB value

RSDPB value

SS-MS

SS-CW

SS-TW

SS-SH

MS-TW

MS-SH

TW-CW

TW-SH

Target-Reference

Target-Reference

0.25 0.2 0.15 0.1

0.25 0.2 0.15 0.1 0.05

0.05

0

0 WR-DS

WR-SS

WR-MS

WR-CW

WR-TW

MS-DS

WR-SH

Target-Reference

MS-SS

MS-WR

MS-CW

Target-Reference

0.35

0.4

0.3

0.35 0.3

0.25

RSDPB value

RSDPB value

SS-WR

0.2 0.15 0.1 0.05

0.25 0.2 0.15 0.1 0.05 0

0 CW-DS

CW-SS

CW-WR

CW-MS

CW-TW

CW-SH

TW-DS

TW-SS

TW-WR

TW-MS

Target-Reference

Target-Reference 0.35

RSDPB value

0.3 0.25 0.2

SAM

JM-SAM (TAN)

JM

JM-SAM (SIN)

DS SS WR MS CW TW SH

0.15 0.1 0.05 0 SH-DS

SH-SW

SH-WR

SH-MS

SH-CW

- Dense Scrub -Sparse Scrub -Weathered Rock -Moist Sand -Clear Water -Turbid Water -Shadow

SH-TW

Target-Reference Fig. 6. Variation of RSDPB values for the spectral matching measures applied to PROBA/CHRIS image.

S. Padma, S. Sanjeevi / International Journal of Applied Earth Observation and Geoinformation 32 (2014) 138–151

0.45

0.4

0.4

0.35

0.35

RSDPB value

RSDPB value

0.45

0.3 0.25 0.2 0.15 0.1 0.05

0.3 0.25 0.2 0.15 0.1 0.05

0

0 A-R

A-P

A-G

A-M

A-S

A-C

A-T

R-A

R-P

R-G

R-M

R-S

R-C

R-T

G-C

G-T

S-C

S-T

T-S

T-C

Target-Reference

Target-Reference 0.45

0.45

0.4

0.4

0.35

0.35

RSDPB value

RSDPB value

145

0.3 0.25 0.2 0.15 0.1 0.05

0.3 0.25 0.2 0.15 0.1 0.05

0

0 P-A

P-R

P-G

P-M

P-S

P-C

P-T

G-A

G-R

Target-Reference

G-P

G-M

G-S

Target-Reference 0.2

0.25

0.18 0.16

RSDPB value

RSDPB value

0.2

0.15

0.1

0.14 0.12 0.1 0.08 0.06 0.04

0.05

0.02 0

0 M-A

M-R

M-P

M-G

M-S

M-C

M-T

S-A

S-R

S-P

S-G

S-M

Target-Reference

0.3

0.3

0.25

0.25

RSDPB value

RSDPB value

Target-Reference

0.2 0.15 0.1 0.05

0.2 0.15 0.1 0.05

0

0 C-A

C-R

C-P

C-G

C-M

C-S

C-T

JM-SAM (TAN)

JM

JM-SAM (SIN)

T-R

T-P

T-G

T-M

Target-Reference

Target-Reference SAM

T-A

A - Avicennia M - Mudflat

R - Rhizopora P - Paddy G - Groundnut S-Sand C - Clear Water T - Turbid Water

Fig. 7. Variation of RSDPB values for the spectral matching measures applied to Hyperion image.

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Table 3 Results for the implementation of spectral matching approaches in PROBA/CHRIS dataset. Jeffries-Matusita (JM) approach

Spectral Angle Mapper (SAM) approach

Dense scrub Sparse scrub Weathered rock Moist sand Clear water Turbid water Shadow JM-SAM (SIN) approach

Dense scrub Sparse scrub Weathered rock Moist sand Clear water Sparse scrub Shadow

Dense scrub

Sparse scrub

Weathered rock

Moist sand

Clear water

Turbid water

Shadow

0 0.0776 0.1424 0.2091 0.5209 0.5152 0.375

0.1475 0 0.0814 0.1372 0.4608 0.4536 0.3172

0.2613 0.156 0 0.0832 0.4634 0.4529 0.331

0.3901 0.2567 0.1576 0 0.398 0.3845 0.2778

0.837 0.7201 0.7552 0.6465 0 0.0379 0.1541

0.8232 0.7023 0.7295 0.6119 0.0768 0 0.1586

0.6995 0.5868 0.6341 0.5441 0.1477 0.174 0

JM-SAM (TAN) approach Dense scrub

Sparse scrub

Weathered rock

Moist sand

Clear water

Turbid water

Shadow

0 0.0114 0.0368 0.0795 0.3869 0.3778 0.2414

0.0115 0 0.0127 0.0348 0.3039 0.293 0.1756

0.0381 0.0128 0 0.0131 0.3176 0.3019 0.1961

0.086 0.036 0.0132 0 0.2397 0.2209 0.1438

0.5777 0.4042 0.4362 0.3004 0 0.0029 0.0227

0.5557 0.3838 0.4049 0.2698 0.0029 0 0.0274

0.3155 0.2109 0.2434 0.1681 0.0229 0.0279 0

Note: The values represent the degree of match in a scale of 0–1. JM and JM-SAM (SIN) matching values are represented in italics. Table 4 Results for the implementation of spectral matching approaches in Hyperion dataset. Jeffries-Matusita (JM) approach

Spectral Angle Mapper (SAM) approach

Avicennia Rhizopora Paddy Groundnut Mudflat Sand Clear water Turbid water JM-SAM (SIN) approach

Avicennia Rhizopora Paddy Groundnut Mudflat Sand Clear water Turbid water

Avicennia

Rhizopora

Paddy

Groundnut

Mudflat

Sand

Clear water

Turbid water

0 0.0387 0.0417 0.0567 0.1147 0.1594 0.1894 0.2232

0.0739 0 0.0708 0.0808 0.099 0.1581 0.1704 0.2024

0.0789 0.1327 0 0.0315 0.1225 0.1424 0.1965 0.2329

0.0963 0.1415 0.0551 0 0.1415 0.1583 0.216 0.2527

0.2401 0.2158 0.2428 0.2759 0 0.098 0.0864 0.1163

0.3316 0.3320 0.2918 0.3222 0.1926 0 0.121 0.1536

0.3903 0.8521 0.3944 0.4289 0.1755 0.2427 0 0.046

0.4587 0.4236 0.4687 0.5036 0.2365 0.3102 0.0946 0

JM-SAM (TAN) approach Avicennia

Rhizopora

Paddy

Groundnut

Mudflat

Sand

Clear water

Turbid water

0 0.00286 0.0033 0.0055 0.0273 0.0519 0.0721 0.0988

0.0029 0 0.0094 0.0114 0.0212 0.0515 0.0599 0.0832

0.0033 0.009442 0 0.0017 0.0295 0.041 0.0755 0.1052

0.0055 0.01151 0.0017 0 0.0386 0.0501 0.0898 0.1219

0.0281 0.021699 0.0303 0.0401 0 0.0188 0.0151 0.0273

0.0549 0.054509 0.0428 0.0529 0.0191 0 0.0291 0.0469

0.0779 0.063961 0.0818 0.0988 0.0153 0.03 0 0.0043

0.1102 0.091258 0.1179 0.1392 0.028 0.0492 0.0044 0

Note: The values represent the degree of match in a scale of 0–1. JM and JM-SAM (SIN) matching values are represented in italics.

3

SAM

JM

JM-SAM (TAN)

JM-SAM (SIN)

2.5

RSDE value

2

1.5

1

0.5

0 Dense Scrub

Sparse Scrub

Weathered Rock

Moist Sand

Clear Water

Turbid Water

Shadow

Target classes Fig. 8. RSDE plots for spectral matching measures applied to PROBA/CHRIS image.

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3

JM

SAM

JM-SAM (TAN)

147

JM-SAM (SIN)

2.5

RSDE value

2

1.5

1

0.5

0 Avicennia Rhizopora

Paddy

Groundnut Mudflat

Target-Reference Target classes

Sand

Clear Water

Turbid Water

Fig. 9. RSDE plots for spectral matching measures applied to Hyperion image.

target. Similarly in the case of Hyperion dataset, the spectral signatures (Rhizopora, paddy, groundnut, mudflat, sand, clear/deep water and turbid/shallow water) form the reference library, while each pixel in the scene (totally 52,206) is the target. In this matching-based classification, each target spectra is matched with the spectra in the reference library. Based on the least matching value, the target is labeled as the respective reference class. SAM, JM and the combined JM-SAM are used to assess this least matching value in the framework of supervised classification. The accuracy of the JM-SAM classified outputs and the SAM and JM classified outputs are compared. Further, the

performance of this matching based classification is compared with the per-pixel based minimum-distance classifier output (Figs. 10 and 11). 8.4.2.1. Accuracy estimation. 160 pixels in the classified image are selected through a stratified random process for accuracy estimation. For PROBA/CHRIS dataset, the landcover samples for validation are selected using Google Earth (Geo-Eye-I) image. In the case of Hyperion, the samples for validation are extracted from the large-scale (1:5000) wetland map (Gnanappazham and Selvam, 2008). The accuracy statistics are reported in Tables 5 and 6.

Fig. 10. Results of classification of PROBA/CHRIS image (Lake Argyle region).

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Fig. 11. Results of classification of Hyperion image (Pichavaram mangrove ecosystem).

Table 5 Accuracy matrix for classification of PROBA/CHRIS image. Class

Dense scrub Sparse scrub Weathered rock Moist sand Clear water Turbid water Shadow Overall accuracy Overall kappa

Percentage classified accurately MIN

SAM

JM

JM-SAM (TAN)

JM-SAM (SIN)

88.2 80.3 20.8 70.1 100 100 52.5 73.13 0.6833

90.1 70.5 63.5 70.5 100 90.8 70.5 79.41 0.75

90.6 81.1 81.4 91.1 92.1 100 63.5 85.69 0.8167

91.5 100 91.5 92.3 100 92.5 82.5 92.9 0.90

100 90.2 91.5 93.1 100 100 65.5 91.47 0.88

Table 6 Accuracy matrix for classification of Hyperion image. Class

Avicennia Rhizopora Paddy Groundnut Mudflat Sand Clear water/deep water Turbid water/shallow water Overall accuracy Overall kappa

Percentage classified accurately MIN

SAM

JM

JM-SAM (TAN)

JM-SAM (SIN)

56.2 78.1 40.5 40.8 70.5 81.5 80 98 67.50 0.6286

71 73.2 80.5 32.5 62.5 90.3 91.5 83 71.25 0.6714

71.1 82.1 42.5 60.5 93.5 76.4 100 100 76.25 0.7286

90.5 75.5 92.5 52.5 100 100 90.5 100 86.25 0.8429

91.5 78.5 78.5 68.5 100 100 90.8 100 85 0.8286

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8.5. Observations, results and discussion 8.5.1. PROBA/CHRIS dataset It is observed that the diagonal values of all the spectral matching measures listed in Table 3 are the least. The order of matching values is as follows: SAM > JM > JM-SAM. A lower matching value indicates a better performance of the algorithm. The proposed JM-SAM algorithm improves the capability of JM and SAM in discriminating spectrally similar and dissimilar targets. Though SAM and JM measures are capable of discriminating dissimilar spectra, the difficulty to match similar spectra is evident from Table 3. For example, spectrally similar dense scrub and sparse scrub are matched with the values of SAM (0.148) followed by JM (0.078), while the JM-SAM algorithm shows an increased similarity of matching with a least value of JM-SAM (TAN) (0.0115) and JMSAM (SIN) (0.0114). The least matching value for JM-SAM, and hence its best performance, is due to the least separable distance between spectral vectors at each band along with least spectral angle. The RSDPB plots presented in Fig. 6 represent the discriminatory capability of the spectral matching algorithms (Du et al., 2004). From the plots, it is inferred that the closeness of the target with the reference is due to a smaller value of RSDPB, while the distinct nature of the target and reference spectra are due to a higher value of RSDPB. Thus, in all cases, the proposed JM-SAM measures produce a smallest value and highest value, respectively, for similar and dissimilar matches. In matching the dense scrub with the sparse scrub, the JM-SAM (TAN) and JM-SAM (SIN) measures outperform the SAM approach with a difference of 0.039 and 0.037 in the range of 0–0.2. Similarly, the JM-SAM has an increased discriminability than JM at a difference of 0.035 and 0.032 in a range of 0–0.2. Discriminability is also seen in the classification results, where the JM-SAM (TAN) and JM-SAM (SIN) display an increased accuracy of (91.5%, 100%) and (100%, 90.2%) in classifying dense and sparse scrub, than SAM (90.1%, 70.5%) and JM (90.6%, 81.1%) measures. The JM-SAM algorithm performs better due to the least separable distance between the spectral vectors of dense scrub and sparse scrub at each of the 62 bands of PROBA/CHRIS, along with the least spectral angle between them. The RSDE plot shown in Fig. 8 indicates that the lowest value of entropy is produced by the JM-SAM measures depicting the increased chance of identifying the targets (dense scrub, sparse scrub, weathered rock, moist sand, clear water, turbid water and shadow) from the spectral library. The least average entropy measure, which indicates higher chance of identifying the seven target

signatures, is obtained through the JM-SAM (TAN) measure as 2.01, followed by a value of 2.06 of JM-SAM (SIN). On the contrary, the individual measures of SAM and JM produced a value of 2.35 and 2.34, respectively. Further, it may be mentioned here that the JMSAM measure efficiently utilizes the band-wise self-information, and hence results in lesser uncertainty (RSDE) in determining the match than the JM and SAM algorithms. A similar observation for SID-SAM mixed measure was made by Du et al. (2004) while working with AVIRIS and HYDICE images for material identification. In relating the entropy measure and classification accuracy (Fig. 12), it is inferred that for all the landcover classes, the least range of RSDE leads to a higher accuracy. For example, in the case of moist sand, the JM-SAM measure leads to a higher accuracy of 93.1% at a RSDE of 2.17, while SAM provides an accuracy of 70.5% at 2.44. Hence for all the seven classes, JM-SAM algorithm provides higher classification accuracy than the individual measures. This enhanced performance is related to its lesser uncertainty in identifying the correct match for the target. From Table 5, it is to be noted that the JM-SAM matching approach results an increased accuracy in classification compared to the minimum distance approach. The order of classification accuracy is as follows: JM-SAM > JM > SAM > Minimum Distance approach. Thus, from the RSDPB and RSDE measures, it can be inferred that the JM-SAM approach shows improved target matching than the SAM and JM approaches. Besides, the consistently improved performance of JM-SAM algorithm is observed from the increased accuracy in classification. 8.5.2. EO-1 Hyperion dataset Improved performance of JM-SAM is further confirmed with the spectral matching values of the eight classes extracted from the Hyperion image (Table 4). The order of match is the same as that of PROBA/CHRIS with SAM presenting a higher value and JM-SAM (TAN) the least value. The degree of match for the Hyperion image can be assessed by the nature of discrimination done for the spectrally similar targets: Avicennia, Rhizopora, paddy and groundnut. In assessing the spectral similarity of Avicennia with Rhizopora, paddy and groundnut, the matching scores produced by the JM-SAM combined measures are 0.0029, 0.0033 and 0.0055, respectively. For the same scenario, SAM and JM produce similarity values of 0.0739, 0.0789, 0.0963 and 0.0387, 0.0417 and 0.0567, respectively. JM-SAM low values of similarity compared to the individual SAM and JM measures indicate the increased capability of the combined algorithm to discriminate even closely matching spectra. This is due to a combination of the

100

Classification Accuracy (in %)

95 90 Dense Scrub

85

Sparse Scrub Weathered Rock

80

Moist Sand

75

Clear Water Turbid Water Shadow

70 65

Increasing values of entropy

60 1.7

1.8

1.9

2

2.1

2.2

149

2.3

2.4

2.5

2.6

RSDE Values Fig. 12. Trendlines depicting the relationship between the RSDE and classification accuracy for the landcover types in PROBA/CHRIS image.

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Classification Accuracy (in %)

100 90 Avicennia

80

Rhizopora

70

Paddy Groundnut

60

Sand

50

TurbidWater

Mudflat Clear Water

Increasing values of entropy

40 2

2.1

2.2

2.3

2.4

2.5

2.6

2.7

2.8

RSDE Values Fig. 13. Trendlines depicting the relationship between the RSDE and classification accuracy for the landcover types in Hyperion image.

least separable distance and spectral angle between the reference and target vector. Better performance of the JM-SAM algorithm compared to the individual measures of SAM and JM can be seen from the RSDPB plots (Fig. 7). Similarity and dissimilarity between the target and reference spectra are captured efficiently by the JM-SAM. At a difference of 0.034 and 0.033 in the range of 0–0.3, the JM-SAM (TAN) and JM-SAM (SIN) measures shows increased performance than the SAM approach in discriminating Avicennia from Rhizopora. Similarly, a difference of 0.037 and 0.036 in the range of 0–0.3 indicates the improved performance of JM-SAM (TAN) and JM-SAM (SIN) measures than the JM approach. The improved performance of JMSAM algorithm is attributed to its nature of combining the least separable distance between the Avicennia and Rhizopora spectra at each band, along with the spectral angle between them. Such discrimination is reflected in the classification results where the JM-SAM (TAN) and JM-SAM (SIN) resulted in accuracy of (90.5%, 75.5%) and (91.5%, 78.5%) in labeling Avicennia and Rhizopora. The RSDE plots shown in Fig. 9, for the eight spectral classes also indicate the discriminatory ability of JM-SAM, which has lowest relative entropy compared to the SAM and JM measures. The least average entropy measure, which indicates the higher chance of target matching, is obtained through JM-SAM (TAN) at a value of 2.39, followed by 2.37 of JM-SAM (SIN). The SAM approach has the highest average entropy value of 2.61 followed by 2.65 of JM. The lower uncertainty of JM-SAM algorithm in identifying the correct match is the reason to its better performance. It is seen that the classification accuracy increases with decreasing range of RSDE measure (Fig. 13). For instance, the JMSAM (TAN) measure classifies paddy at an accuracy of 92.5% at a RSDE of 2.07, while JM measures classify at 42.5 at a RSDE of 2.53. This improved performance of JM-SAM measure is inferred for all the eight classes at a lower range of entropy measure. Besides, the JM-SAM spectral matching approach presented here provides an improved accuracy than the minimum distance approach (Table 6). The order of classification accuracy is as follows: JM-SAM > JM > SAM > Minimum Distance approach.

9. Conclusions This paper has presented a novel spectral matching measure named the ‘JM-SAM approach’ which combines the capabilities of the Spectral Angle Mapper (SAM) and the Jeffries-Matusita measure. From the experiments conducted using PROBA/CHRIS and Hyperion images, it is seen that the JM-SAM approach outperformed the individual measures of SAM and JM with the least

average entropy in spectral matching. The JM-SAM (TAN) and JMSAM (SIN) approaches, with an average RSDE of 2.01 and 2.06, produced better match for the PROBA/CHRIS image, than the SAM and JM approaches. For the Hyperion image, the JM-SAM (TAN) and JM-SAM (SIN) methods showed improved spectral matching than the SAM and JM approaches with an average RSDE of 2.39 and 2.37. When used in image classification, the combined measures of JM-SAM (TAN) and JM-SAM (SIN) yielded an increased accuracy of (92.9%, 91.47%) and (87.69%, 88.48%), respectively, for the PROBA/CHRIS and Hyperion datasets. The compatibility of the proposed JM-SAM algorithm in matching the real (nonsynthetic) narrow-band spectra, has also been demonstrated using the PROBA/CHRIS and Hyperion images. The increased utilization of band-wise information content through JM, coupled with the geometrical matching by SAM has resulted in the efficient JM-SAM spectral matching algorithm. Besides, the utility of the proposed algorithm in extracting landcover information for a complex mangrove ecosystem has also been proved in this study. Acknowledgements The authors thank late Professor Mike Barnsley and Mrs. Vidhya Lakshmi Sivakumar of Swansea University, United Kingdom for the PROBA/CHRIS image and the United States Geological Survey (USGS) for the Hyperion image used in this study. References Andreoli, G., Bulgarelli, B., Hosgood, B., Tarchi, D., 2007. Hyperspectral Analysis of Oil and Oil-impacted Soils for Remote Sensing Purposes. EUR 22739 EN – DF Joint Research Centre. Office for Official Publications of the European Communities, Scientific and Technical Research series, Luxembourg, 34 pp. Ajithkumar, T.T., Thangaradjou, T., Kannan, L., 2008. Spectral reflectance properties of mangrove species of the Muthupettai mangrove environment, Tamil Nadu. J. Environ. Biol. 29, 785–788. Bruzzone, L., Roli, F., Serpico, S.B., 1995. An extension of the Jeffreys–Matusita distance to multiclass cases for feature selection. IEEE Trans. Geosci. Remote Sens. 33, 1318–1321. Cantero, M.C., Perez, R., Martínez, P., Aguilar, P.L., Plaza, J., Plaza, A., 2004. Analysis of the behavior of a neural network model in the identification and quantification of hyperspectral signatures applied to the determination of water quality. In: SPIE Optics East Conference, Chemical and Biological Standoff Detection, Philadelphia, PA. Chang, C.I. (Ed.), 2003. Hyperspectral Imaging: Techniques for Spectral Detection and Classification. Kluwer Academic, New York. Drake, N.A., Mackin, S., Settle, J.J., 1999. Mapping vegetation, soils, and geology in semiarid shrublands using spectral matching and mixture modeling of SWIR AVIRIS imagery. Remote Sens. Environ. 68, 12–25. Du, Y., Chang, C.I., Ren, H., Chang, C.C., Jensen, J.O., D’Amico, F.M., 2004. New hyperspectral discrimination measure for spectral characterization. Opt. Eng. 43, 1777–1786.

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