Spectral-Spatial Hyperspectral Image Classification Based on Mathematical Morphology Post-Processing

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Procedia Computer Science 00 (2018) 000–000 Procedia Computer Science 00(2018) (2018)93–97 000–000 Procedia Computer Science 129

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2017 International Conference on Identification, Information and Knowledge in the Internet of 2017 International Conference on Identification, Information and Knowledge in the Internet of Things Things

Spectral-Spatial Spectral-Spatial Hyperspectral Hyperspectral Image Image Classification Classification Based Based on on Mathematical Morphology Post-Processing Mathematical Morphology Post-Processing a School a School b

Lishuan Hua,b , Chengming Qib,∗, Qun Wanga Lishuan Hua,b , Chengming Qib,∗, Qun Wanga

of Information Engineering, China University of Geosciences (Beijing), Beijing 100083, China of Information Engineering, China University of Geosciences (Beijing), Beijing 100083, China College of Urban Rail Transit and Logistics, Beijing Union University, Beijing 100101, China b College of Urban Rail Transit and Logistics, Beijing Union University, Beijing 100101, China

Abstract Abstract Hyperspectral remote sensing sensors can provide plenty of valuable information. Fusion of spectral and spatial information plays a Hyperspectral sensorsImage can provide of valuable information. Fusion spectral and spatial information plays a key role in the remote field of sensing HyperSpectral (HSI) plenty classification. In this paper, a novel twoofstages spectral-spatial HSI classification key role in the field of HyperSpectral Image (HSI) classification. In this paper, a novel two stages spectral-spatial HSI classification method based on Mathematical Morphology (MM) post-processing is proposed. In first stage, Support Vector Machine (SVM) is method based on Mathematical Morphology (MM) proposed. In first Supportnoise, Vector Machine is adopted to obtain the initial classification results. Inpost-processing second stage, inisorder to remove saltstage, and pepper MM is used(SVM) to refine adopted to obtain theofinitial results. are In second stage, order to remove salt andThe pepper noise, MM is used refine the obtained results aboveclassification stage. Experiments conducted oninthe Indian Pines dataset. evaluation results showtothat the the obtained resultsachieves of abovebetter stage.accuracy Experiments are conducted the Indian Pines dataset.HSI Theclassification evaluation results show that the proposed approach than several recentlyon proposed post-processing methods. proposed approach achieves better accuracy than several recently proposed post-processing HSI classification methods. c 2018 Copyright  2018 Elsevier Elsevier Ltd. Ltd. All All rights rights reserved. reserved. Copyright © c 2018 Copyrightand  Elsevierunder Ltd. All rights reserved. Selection peer-review responsibility Selection and peer-review under responsibility of of the the scientific scientific committee committee of of the the 2017 2017 International International Conference Conference on on Identification, Identification, Selection andand peer-review under responsibility of the (IIKI2017). scientific committee of the 2017 International Conference on Identification, Knowledge in the Internet of Things Information (IIKI2017). Information and Knowledge in the Internet of Things (IIKI2017). Keywords: Mathematical morphology; Noise reduction; Spectral-spatial hyperspectral image classification; Support vector machines. Keywords: Mathematical morphology; Noise reduction; Spectral-spatial hyperspectral image classification; Support vector machines.

1. Introduction 1. Introduction HyperSpectral Image (HSI) analysis has been an emerging research topic in recent years. Hyperspectral sensors HyperSpectral Image (HSI) analysis been anofemerging topic can in recent years. sensors divide the electromagnetic spectrum intohas hundreds spectral research bands, which provide the Hyperspectral potential and detailed divide the electromagnetic spectrum into hundreds of spectral bands, which can provide the potential and detailed land-cover distinction and identification [1]. In the first beginning, many algorithms are designed to classify each land-cover anditsidentification [1].These In the first beginning, algorithms are designed classify each pixel of the distinction HSI based on spectrum only. method are knownmany as pixel-wise methods. Supportto vector machine pixel of the HSI based on its spectrum only. These method are known as pixel-wise methods. Support vector machine (SVM)[2] depends on the principle of structural risk minimization [3], has a promising generalization performance (SVM)[2] principlethe ofHSI structural risk minimization has a promising generalization performance when beingdepends applied on for the supporting classification [4, 5, 6]. The[3], pixel-wise classification map obtained from the when being applied for supporting the HSI classification [4, 5, 6]. The pixel-wise classification map obtained from the SVM classifier usually contains salt-and-pepper noise because the spectrum of the pixel is volatile and vulnerable to SVM classifier usually contains salt-and-pepper noise because the spectrum of the pixel is volatile and vulnerable to the effects of environmental noise. In recent years, it is found that the integration of spectral and spatial information the effects of environmental noise. In recent years, it is found that the integration of spectral and spatial information in the image analysis can eliminate salt-and-pepper noise on classification map and can markedly improve imagein the image analysis can eliminate salt-and-pepper noise on classification map and can markedly improve image∗ ∗

Corresponding author. Corresponding E-mail address:author. [email protected] E-mail address: [email protected] c 2018 Elsevier Ltd. All rights reserved. 1877-0509 Copyright  c 2018 1877-0509and Copyright  Elsevier Ltd. Allof rights scientific reserved. committee of the 2017 International Conference on Identification, Information and Selection peer-review under responsibility 1877-0509 Copyright © 2018 Elsevier Ltd. Allthe rights reserved. Selection and responsibility of the scientific committee of the 2017 International Conference on Identification, Information and Knowledge in peer-review the Internet under of Things (IIKI2017). Selection and peer-review under responsibility of the scientific committee of the 2017 International Conference on Identification, Information Knowledge in theinInternet of Things (IIKI2017). and Knowledge the Internet of Things (IIKI2017). 10.1016/j.procs.2018.03.054

Lishuan Hu et al. / Procedia Computer Science 129 (2018) 93–97 Author name / Procedia Computer Science 00 (2018) 000–000

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classification accuracy. Morphological filters [7] and other types of local filtering approaches have been investigated to develop novel spatial feature extraction and classification methods. In this paper, a novel spectral-spatial hyperspectral image classification method is presented based on Mathematical Morphology (MM) post-processing. The major advantage of the proposed SVM+MM method is that it can make full use of the spatial information of the HSI while does not need to computer the spatial features and it only need use set operators to deal with binary images. Experiments performed on the Indian Pines dataset demonstrate the effectiveness of the proposed method. The remainder of this paper is organized as follows. In Section 2, we formulate the proposed framework of SVM+MM. We present the experimental results in Section 3, and conclude our work in Section 4. 2. PROPOSED METHOD During the last decade, many post-processing techniques (e.g., domain analysis, clustering, filtering) were proposed to refine the results of classification. But these techniques used all class numbers could result in the final class number of a pixel being affected by its neighborhood pixels. In this paper, we propose an improved post-processing method that can avoid these shortcomings effectively. This section includes three steps. First, we briefly review SVM. Second, we introduce mathematical morphology. Finally, we present a novel method by combining SVM and MM for solving classification problems. 2.1. SVM SVM, introduced by Vapnik [3], is one of the most successful kernel methods. Compared to its counterparts, SVM has two major advantages: (1) it requires only a few samples (i.e., support vectors) to locate the optimal separating hyperplane; and (2) a Gaussian kernel SVM can manage infinite-dimension classification problems and is thus robust to the spectral dimension of a hyperspectral image. Generally, a cross-validation procedure is applied to choose the most appropriate kernel function k(·, ·), and parameters among a set of kernel functions on a separate validation set, which is different from the training set. 2.2. MM Mathematical morphology is a technique of image processing based on set theory, lattice theory, topology, and random function [8]. MM aims to analyze spatial relationships between pixels using a set of known shape and size(e.g., disk of radius 3 pixels), called the structuring elements (SE)[9]. The four fundamental MM operators are erosion, dilation, opening and closing. Let E be a Euclidean space or an integer grid, and A a binary image in E. The erosion of the binary image A by the structuring element B is defined by: A  B = {z ∈ E|Bz ⊆ A}

where Bz is the translation of B by the vector z, i.e., Bz = {b + z|b ∈ B}, ∀z ∈ E. The dilation of A by the structuring element B is defined by:  Ab A⊕B=

(1)

(2)

b∈B

The opening of A by B is obtained by the erosion of A by B, followed by dilation of the resulting image by B: A ◦ B = (A  B) ⊕ B

(3)

A • B = (A ⊕ B)  B

(4)

(A ◦ B) • B = {[(A  B) ⊕ B] ⊕ B}  B

(5)

The closing of A by B is obtained by the dilation of A by B, followed by erosion of the resulting structure by B: Noises filtering for binary images A can be obtained by the opening of A ,followed by closing of A.



Lishuan Hu et al. / Procedia Computer Science 129 (2018) 93–97 Author name / Procedia Computer Science 00 (2018) 000–000

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Fig. 1. (a)the original binary images; (b)the structuring elements (SE);(c-d)the opening operator;(e-f)the closing operator

Fig. 1 shows after an opening and closing operation, a binary image can remove noise inside or outside it. We can use this idea to remove the salt and pepper noise of result maps of HSI classification. 2.3. SVM+MM Morphologic operations are especially suited to the processing of binary images. In our proposed method, we decompose SVM multi-class classification map into multiple single-class classification binary maps first, where pixels belonging to that class are assigned value 1 (or 0 otherwise). Then, the noise of these binary maps is removed respectively by morphologic basic operations. Finally, these refined maps are fused into a single classification map. The SVM+MM approach is shown in Algorithm 1. Algorithm 1 SVM+MM Require: the classification map of SVM Ensure: the final classification map 1: for i = 1, · · · , N do 2: initialize Bi binary map 3: for j = 1, · · · , M do 4: if (bi, j = calssNumi ) then bi, j = 1 5: elsebi, j = 0 6: end if 7: end for. 8: call bwmorph(Bi ,’open’) //opening operator 9: call bwmorph(Bi ,’close’)//closing operator 10: end for. 11: for j = 1, · · · , M do 12: p j = argi maxbij 13: end for. In Algorithm 1, the algorithm of SVM+MM relied on assignment operation and set operation. The function ”bwmorph” of matlab is the opening and closing function of morphologic basic operations. The opening and closing function can run fast for it is only set operator. 3. Experimental results In this section, we implement several experiments to verify the performance of our method and compare with other noise reduction methods for HSI classification. 3.1. Data Set Description The India Pines dataset was collected by the AVIRIS sensor over the Indian Pines region, Northwestern Indian, USA, in 1992, which is widely used to verify the performance of classification algorithms. The Indian Pines data set comprises 220 bands with the spatial size of 145 × 145 pixels in the wavelength range from 0.4 to 2.5µm. Removing the noisy bands, 200 bands remained. The ground truth has 10,062 labeled pixels which consists of 16 land cover classes. Figure 3(a) shows a false color composite (bands 17, 27, and 50 for RGB) and (b) the ground truth.

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Fig. 2. the first line:the original 9 binary images for each class ; the second line: the opening result images for every original images; the third line: the closing result images for the second line images respectively.

(a)

(b)

(c)

(d)

Fig. 3. Classification maps of the AVIRIS Indian Pine dataset: (a) false color composite (bands 17, 27, and 50 for RGB), (b) ground truth,(c) classification map for the SVM method and (d) classification map for the proposed method SVM+MM.

In Indian Pines data set, after removing some classes with small sample size, only 9 classes are considered. As table 1 shows, about 20% of available labeled samples are randomly selected for training, and the remaining for test in each run. 3.2. Experimental Result For SVM, we adopt the popular LIBSVM toolbox [10] with the Gaussian radial basis function (RBF) kernel as solver. The optimal parameters C and γ are optimized by five-fold cross validation. Figure 2 are 3 × 9 binary images, and the first line 9 binary images are obtained by SVM for each class. The second and the third line binary images are opening/closing process results of their above binary images respectively. We can easily see that the noise of these original binary images are reduced significantly with the MM method. Figure 3 (c) and (d) are the classification maps for the SVM method and SVM+MM method respectively. We can see that SVM+MM can reduce the salt and pepper noise greatly compared with SVM. It is clear that our proposed method can obtain the better performance and be more closer to the material spatial distribution than SVM. In order to verify our proposed method, we compare with the classification results of two other postprocessing methods. One of methods uses the construction of a minimum spanning forest from the SVMderived markers(SVM+MSF)[12] , and the other uses majority voting within neighborhoods defined by HSEG segmentation(HSEG+MV)[13]. Three evaluation metrics, overall accuracy (OA), average accuracies (AA), and Kappa coefficient, are used to measure the statistical significance for hyperspectral image classification [11]. Table 1 summarizes global and class-specific accuracies of the pixel-wise SVM classification, SVM+MSF, HSEG+MV and the proposed SVM+MM method for the Indian Pines dataset. It shows that our method presents higher performances. It is noticeable that SVM get the lowest classification accuracy without post-processing. Compare with the other two post-processing methods, our proposed method obtains the highest OA above 93%, Kappa and AA.



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Table 1. Class names, number of training and testing samples, global and class-specific classification accuracies in percentage for the Indian Pines Image. Indian Pines

Training

Test

SVM

SVM+MSF

HSEG+MV

SVM+MM

Corn-notill Corn-mintill Grass/Pasture Grass/Trees Hay-windrowed Soybeans-notill Soybeans-mintill Soybeans-clean Woods

304 157 91 137 91 187 511 106 235

1124 673 392 593 387 785 1944 487 1030

79.09 73.85 95.66 96.80 99.48 70.45 85.80 80.49 97.96

90.97 69.52 94.63 92.40 99.77 98.04 81.97 85.99 97.59

90.46 83.04 94.41 97.56 99.54 92.06 84.04 95.39 98.63

92.17 85.29 96.68 99.33 100 84.33 95.37 91.58 99.42

Kappa OA AA

-

-

0.8295 0.8553 0.8662

0.8612 0.8841 0.9110

0.8847 0.9086 0.9279

0.9263 0.9374 0.9380

4. Conclusion In this paper, a mathematical morphology post-processing has been applied to hyperspectral remote sensing image classification. Our approach mixes the spectral and the spatial information in a better way. Therefore, we obtain more accurate results than the SVM, SVM+MSF and HSEG+MV. Experimental results show that, compared with previously proposed algorithms, our algorithm is a promising method on HSI classification. 5. References References [1] C.I. Chang, Hyperspectral Data Exploitation: Theory and Applications, Hoboken, NJ, Wiley, USA, 2007. [2] L.Bruzzone, C. Persello, A novel context-sensitive semisupervised SVM classifier robust to mislabeled training samples, IEEE Trans. Geosci. Remote Sens. 47 (2009) 2142-2154. [3] V.N. Vapnik, Statistical Learning Theory, John Wiley & Sons, New York, 1998. [4] E. Pasolli, F.Melgani, D. Tuia, F. Pacifici, W.J. Emery, SVM active learning approach for image classification using spatial information, IEEE Trans. Geosci. Remote Sens. 52 (2014) 2217-2233. [5] J. Shawe-Taylor, N. Cristianini, Kernel Methods for Pattern Analysis, Cambridge University Press, New York, 2004. [6] L. Gao, J. Li, M. Khodadadzadeh, A. Plaza, B. Zhang, Z. He, H. Yan, Subspace-Based Support Vector Machines for Hyperspectral Image Classification, IEEE Geosci. Remote Sens. Lett. 12 (2015) 349-353. [7] Benediktsson, J. A. Pesaresi, Classification and feature extraction for remote sensing images from urban areas based on morpological transfromations, IEEE Transactions on Geoscience and Remote Sensing. 41(2003)1940-1949. [8] J. Serra, Image Analysis and Mathematical Morphology. London, U.K.:Academic,1988. [9] P. Soille, Morphological Image Analysis, Principles and Applications, 2nd ed. New York: Springer-Verlag, 2003. [10] C.C. Chang, C.J. Lin, LIBSVM: A library for support vector machines, ACM Trans. Intell. Syst. Technol. 2 (2011) 27 pages. [11] R.G. Congalton, R.G. Oderwald, R.A. Mead, Assessing Landsat classification accuracy using discrete multivariate analysis statistical techniques, Photogramm. Eng. Remote Sens. 49 (1983) 1671-1678. [12] Y. Tarabalka, J. Chanussot, Segmentation and classification of hyperspectral images using minimum spanning forest grown from automatically selected markers, IEEE Trans. Systems, Man, and Cybernetics. 40(2010)1267-1279. [13] Y. Tarabalka, J. A. Benediktsson, Multiple spectral-spatial classification approach for hyperspectral data, IEEE Trans. on Geoscience and Remote Sensing. 48(2010)4122-41132.