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Neural Computing and Applications
Erschienen in: Neural Computing and Applications 2/2016

01.02.2016 | Original Article

A spectral–textural kernel-based classification method of remotely sensed images

verfasst von: Jianqiang Gao, Lizhong Xu, Fengchen Huang

Erschienen in: Neural Computing and Applications | Ausgabe 2/2016

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Abstract

Most studies have been based on the original computation mode of semivariogram and discrete semivariance values. In this paper, a set of texture features are described to improve the accuracy of object-oriented classification in remotely sensed images. So, we proposed a classification method support vector machine (SVM) with spectral information and texture features (ST-SVM), which incorporates texture features in remotely sensed images into SVM. Using kernel methods, the spectral information and texture features are jointly used for the classification by a SVM formulation. Then, the texture features were calculated based on segmented block matrix image objects using the panchromatic band. A comparison of classification results on real-world data sets demonstrates that the texture features in this paper are useful supplement information for the spectral object-oriented classification, and proposed ST-SVM classification accuracy than the traditional SVM method with only spectral information.
Vorheriger Artikel Object detection in video sequences by a temporal modular self-adaptive SOM
Nächster Artikel The stabilization of BAM neural networks with time-varying delays in the leakage terms via sampled-data control

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Literatur
1.
Murray H, Lucieer A, Williams R (2010) Texture-based classification of sub-antarctic vegetation communities on Heard Island. Int J Appl Earth Obs Geoinf 12:138–187CrossRef
2.
Yan H, Anzhi Y, Su W, Daoliang L, Ming L, Yijun J, Chao Z (2008) Texture feature extraction for land-cover classification of remote sensing data in land consolidation district using semi-variogram analysis. WSEAS Trans Comput 7:857–923
3.
Farrokhnia F, Jain AK (1991) A multi-channel filtering approach to texture segmentation. In: Proceedings of IEEE computer society conference on computer vision and pattern recognition. IEEE Computer Society Press, Maui, pp 364–434
4.
Fogel I, Sagi D (1989) Gabor filters as texture discriminator. Biol Cybern 61:103–116CrossRef
5.
Manjunath BS, Ma WY (1996) Texture features for browsing and retrieval of image data. IEEE Trans Pattern Anal Mach Intell 18:837–879CrossRef
6.
Tuceryan M, Jain AK (1998) Texture analysis. In: Chen CH, Pau LF, Wagn PSP (eds) Handbook of pattern recognition and computer vision. World Scientific Publishing Company, Hackensack, pp 207–255
7.
Tuceryan M, Ahuja N (1990) Extraction of early perceptual structure in dot patterns: integrating region, boundary, and component gestalt. Comput Vis Graph Image Process 49:279–359CrossRef
8.
Cesmeli E, Wang DL (2001) Texture segmentation using Gaussian-Markov random fields and neural oscillator networks. IEEE Trans Neural Netw 12:394–404CrossRef
9.
Chaudhuri BB, Sarkar N (1995) Texture segmentation using fractal dimension. IEEE Trans Pattern Anal Mach Intell 17:72–79CrossRef
10.
Maillard P (2003) Comparing texture analysis methods through classification. Photogramm Eng Remote Sens 69:357–424CrossRef
11.
Su W, Zhang C, Yang J, Wu H, Deng L, Yue A, Chen M (2012) Analysis of wavelet packet and statistical textures for object oriented classification of forest-agriculture ecotones using SPOT 5 imagery. Int J Remote Sens 33:3557–3636CrossRef
12.
Atkinson PM, Lewis P (2000) Geostatistical classification for remote sensing: an introduction. Comput Geosci 26:361–432CrossRef
13.
Chica-Olmo M, Abarca-Hernandez F (2000) Computing geostatistical image texture for remotely sensed data classification. Comput Geosci 26:373–456CrossRef
14.
Miranda FP, Fonseca LEN, Carr JR (1998) Semivariogram textural classification of JERS-1 (Fuyo-1) SAR data obtained over a flooded area of the amazon rainforest. Int J Remote Sens 19:549–605CrossRef
15.
Yue A, Zhang C, Yang J, Su W, Yun W, Zhu D (2013) Texture extraction for object-oriented classification of high spatial resolution remotely sensed images using a semivariogram. Int J Remote Sens 34(11):3736–3759. doi:10.​1080/​01431161.​2012.​759298 CrossRef
16.
Ohanian PP, Dubes RC (1992) Perfor mance evaluation for four classes of textural features. Pattern Recognit 25:819–852CrossRef
17.
Lloyd CD, Berberoglu S, Curran PJ, Atkinson PM (2004) A comparison of texture measures for the per-field classification of mediterranean land cover. Int J Remote Sens 25:3943–4008CrossRef
18.
Su W, Li J, Chen YH, Liu ZG, Zhang JS, Low TM, Suppiah I, Hashim SAM (2008) Textural and local spatial statistics for the object-oriented classification of urban areas using high resolution imagery. Int J Remote Sens 29:3105–3122CrossRef
19.
Cortes C, Vapnik VN (1995) Support vector networks. Mach Learn 20:273–297MATH
20.
21.
Vapnik VN (1999) An overview of statistical learning theory. IEEE Trans Neural Netw 10:988–999CrossRef
22.
Varshney PK, Arora MK (2004) Advanced image processing techniques for remotely sensed hyperspectral data. Springer, BerlinCrossRef
23.
Osuna E, Freund R, Girosi F (1997) Training support vector machines: an application to face detection. In: Proceedings of computer vision and pattern recognition, pp 130–136
24.
Deniz O, Castrillon M, Hernandez M (2003) Face recognition using independent component analysis and support vector machines. Pattern Recognit Lett 24(13):2153–2157CrossRef
25.
Chapelle O, Haffner P, Vapnik VN (1999) Support vector machines for histogram-based image classification. IEEE Trans Neural Netw 10(5):1055–1064CrossRef
26.
Guo G, Li S (2003) Content-based audio classification and retrieval by support vector machines. IEEE Trans Neural Netw 14(1):209–215CrossRef
27.
Mukherjee S, Osuna E, Girosi F (1997) Nonlinear prediction of chaotic time series using support vector machines. In: Proceedings of the IEEE workshop on neural networks for signal processing, Amelia Island, pp 511–520
28.
Zhu G, Blumberg DG (2002) Classification using ASTER data and SVM algorithms: the case study of Beer Sheva, Israel. Remote Sens Environ 80(2):233–240CrossRef
29.
Nemmour H, Chibani Y (2006) Multiple support vector machines for land cover change detection: an application for mapping urban extensions. ISPRS J Photogramm Remote Sens 61(2):125–133CrossRef
30.
Melgani F, Bruzzone L (2004) Classification of hyperspectral remote sensing images with support vector machines. IEEE Trans Geosci Remote Sens 42(8):1778–1790CrossRef
31.
Pal M, Mather PM (2005) Support vector machines for classification in remote sensing. Int J Remote Sens 26(5):1007–1011CrossRef
32.
Inglada J (2007) Automatic recognition of man-made objects in high resolution optical remote sensing images by SVM classification of geometric image features. ISPRS J Photogramm Remote Sens 62(3):236–248CrossRef
33.
Gao J, Fan L (2011) Kernel-based weighted discriminant analysis with QR decomposition and its application face recognition. WSEAS Trans Math 10(10):358–367MathSciNet
34.
Gao J, Li L, Fan L, Xu L (2013) An application of weighted kernel fuzzy discriminant analysis. Adv Comput Math Appl 2(4):329–338MathSciNet
35.
Gao J, Fan L, Li L, Xu L (2013) A practical application of kernel-based fuzzy discriminant analysis. Int J Appl Math Comput Sci 23(4):887–903MathSciNetCrossRefMATH
36.
Camps-Valls G, Bruzzone L (2005) Kernel-based methods for hyperspectral image classification. IEEE Trans Geosci Remote Sens 43(6):1351–1362CrossRef
37.
Fauvel M, Chanussot J, Benediktsson JA (2012) A spatial–spectral kernel-based approach for the classification of remote-sensing images. Pattern Recognit 45(1):381–392CrossRef
38.
Fauvel M et al (2013) Advances in spectral–spatial classification of hyperspectral images. Proc IEEE 101(3):652–675CrossRef
39.
Guo B, Gunn S, Damper R, Nelson J (2008) Customizing kernel functions for SVM-based hyperspectral image classification. IEEE Trans Image Process 17(4):622–629MathSciNetCrossRef
40.
Camps-Valls G, Gomez-Chova L, Munoz-Mari J, Vila-Frances J, Calpe-Maravilla J (2006) Composite kernels for hyperspectral image classification. IEEE Geosci Remote Sens Lett 3(1):93–97CrossRef
41.
Mercier G, Girard-Ardhuin F (2006) Partially supervised oil-slick detection by SAR imagery using kernel expansion. IEEE Trans Geosci Remote Sens 44(10):2839–2846CrossRef
42.
Gao J, Xu L, Shi A, Huang F (2014) A kernel-based block matrix decomposition approach for the classification of remotely sensed images. Appl Math Comput 228:531–545MathSciNetCrossRef
43.
Song B, Li J, Mura MD, Li P, Plaza A, Bioucas-Dias JM, Benediktsson JA, Chanussot J (2013) Remotely sensed image classification using sparse representations of morphological attribute profiles. IEEE Trans Geosci Remote Sens. doi:10.​1109/​TGRS.​2013.​2286953
44.
Chen Y, Nasrabadi N, Tran T (2011) Hyperspectral image classification using dictionary-based sparse representation. IEEE Trans Geosci Remote Sens 49(10):3973–3985CrossRef
45.
Plaza A, Benediktsson JA, Boardman JW et al (2009) Recent advances in techniques for hyperspectral image processing. Remote Sens Environ 113:S110–S122CrossRef
46.
Cover TM (1965) Geometrical and statistical properties of systems of linear inequalities with application in pattern recognition. IEEE Trans Electron Comput 14(3):326–334CrossRefMATH
47.
Schölkopf B, Smola A (2002) Learning with kernels-support vector machines, regularization, optimization and beyond. MIT Press, Cambridge
48.
Zhang R, Ma J (2008) An improved SVM method P-SVM for classification of remotely sensed data. Int J Remote Sens 29(20):6029–6036CrossRef
49.
Ulaby FT, Kouyate F, Brisco B et al (1986) Textural infornation in SAR images. IEEE Trans Geosci Remote Sens 24(2):235–245CrossRef
50.
Haralick RM, Shanmugam K, Dinstein IH (1973) Textural features for image classification. IEEE Trans Syst Man Cybern 3(6):610–621CrossRef
51.
52.
53.
Park CH, Park H (2008) A comparison of generalized linear discriminant analysis algorithms. Pattern Recognit 41:1083–1097CrossRefMATH
Metadaten
Titel
A spectral–textural kernel-based classification method of remotely sensed images
verfasst von
Jianqiang Gao
Lizhong Xu
Fengchen Huang
Publikationsdatum
01.02.2016
Verlag
Springer London
Erschienen in
Neural Computing and Applications / Ausgabe 2/2016
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-015-1862-7

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