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SCIENCE CHINA Information Sciences, Volume 59, Issue 10: 102313(2016) https://doi.org/10.1007/s11432-016-5576-y

Spectral-spatial classification for hyperspectral imagery: a novel combination method \\based on affinity scoring

Zhao CHEN1,2,3, Bin WANG1,2,3,*
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  • ReceivedSep 13, 2015
  • AcceptedJan 20, 2016
  • PublishedAug 26, 2016

Abstract

Recently, a general framework for spectral-spatial classification has caught the attention of the hyperspectral imagery (HSI) society. It consists of three parts: classification, segmentation and combination of the former results to make a refined labeled map. Seeing the potentials of the last part, we derive a novel combination rule based on affinity scoring (CRAS). The core of the system is affinity score (AS), which is derived from fuzzy logic. Every AS measures the degree, i.e., the affinity, by which a pixel belongs to a class. The score is essentially decided by three factors: local spatial consistency, spectral similarity, and prior knowledge. The method is compatible with basic classification and segmentation tools, thus saving the trouble of designing complex techniques for the other parts in the framework. Experimental results show that CRAS excels several basic techniques as well as various state-of-the-art methods in the area of spectral-spatial classification.


Acknowledgment

Acknowledgments

This work was supported in part by National Natural Science Foundation of China (Grant No. 61572133), and Research Fund for State Key Laboratory of Earth Surface Processes and Resource Ecology (Grant No. 2015-KF-01).


References

[1] Fauvel M, Tarabalka Y, Benediktsson J A, et al. Advances in spectral-spatial classification of hyperspectral images. Proc IEEE, 2013, 101: 652-675 CrossRef Google Scholar

[2] Camps-Valls G, Tuia D, Bruzzone L, et al. Advances in hyperspectral image classification. IEEE Signal Process Mag, 2014, 31: 45-54 Google Scholar

[3] Khodadadzadeh M, Li J, Plaza A, et al. Spectral-spatial classification of hyperspectral data using local and global probabilities for mixed pixel characterization. IEEE Trans Geosci Remote Sens, 2014, 52: 6298-6314 CrossRef Google Scholar

[4] Pu H Y, Chen Z, Wang B, et al. A novel spatial-spectral similarity measure for dimensionality reduction and classification of hyperspectral imagery. IEEE Trans Geosci Remote Sens, 2014, 52: 7008-7022 CrossRef Google Scholar

[5] Chen Y, Nasrabadi N, Tran T. Hyperspectral image classification via kernel sparse representation. IEEE Trans Geosci Remote Sens, 2013, 51: 217-231 CrossRef Google Scholar

[6] Huang X, Zhang L P. An SVM ensemble approach combining spectral, structural, and semantic features for the classification of high-resolution remotely sensed imagery. IEEE Trans Geosci Remote Sens, 2013, 51: 257-271 CrossRef Google Scholar

[7] Pesaresi M, Benediktsson J A. A new approach for the morphological segmentation of high-resolution satellite imagery. IEEE Trans Geosci Remote Sens, 2001, 39: 309-320 CrossRef Google Scholar

[8] Fauvel M, Chanussot J, Benediktsson J A. A spatial-spectral kernel-based approach for the classification of remote-sensing images. Patt Recogn, 2012, 45: 381-392 CrossRef Google Scholar

[9] Achanta R, Shaji A, Smith K, et al. SLIC superpixels compared to state-of-the-art superpixel methods. IEEE Trans Patt Anal Mach Intell, 2012, 34: 2274-2280 CrossRef Google Scholar

[10] Yang H, Du Q, Ma B. Decision fusion on supervised and unsupervised classifiers for hyperspectral imagery. IEEE Geosci Remote Sens Lett, 2010, 7: 875-879 CrossRef Google Scholar

[11] Tarabalka Y, Benediktsson J A, Chanussot J, et al. Multiple spectral-spatial classification approach for hyperspectral data. IEEE Trans Geosci Remote Sens, 2010, 48: 4122-4132 Google Scholar

[12] Tilton J C, Tarabalka Y, Montesano P M, et al. Best merge region growing segmentation with integrated non-adjacent region object aggregation. IEEE Trans Geosci Remote Sens, 2012, 50: 4454-4467 CrossRef Google Scholar

[13] Tarabalka Y, Chanussot J, Benediktsson J A. Segmentation and classification of hyperspectral images using minimum spanning forest grown from automatically selected markers. IEEE Trans Syst Man Cybern B-Cybern, 2010, 40: 1267-1279 CrossRef Google Scholar

[14] Bernard K, Tarabalka Y, Angulo J, et al. Spectral-spatial classification of hyperspectral data based on a stochastic minimum spanning forest approach. IEEE Trans Image Process, 2012, 21: 2008-2021 CrossRef Google Scholar

[15] Kang X, Li S, Benediktsson J A. Spectral-spatial hyperspectral image classification with edge-preserving filtering. IEEE Trans Geosci Remote Sens, 2014, 52: 2666-2677 CrossRef Google Scholar

[16] Tilton J C. Image segmentation by region growing and spectral clustering with a natural convergence criterion. In: Proceedings of IEEE International Geoscience and Remote Sensing Symposium, Seattle, 1998. 4: 1766--1768. Google Scholar

[17] Beaulieu J M, Goldberg M. Hierarchy in picture segmentation: a stepwise optimization approach. IEEE Trans Patt Anal Mach Intell, 1989, 11: 150-163 CrossRef Google Scholar

[18] Plaza A J, Tilton J C. Automated selection of results in hierarchical segmentations of remotely sensed hyperspectral images. In: Proceedings of IEEE International Geoscience and Remote Sensing Symposium, Seoul, 2005. 7: 4946--4949. Google Scholar

[19] Tarabalka Y, Tilton J C, Benediktsson J A, et al. Marker-based hierarchical segmentation and classification approach for hyperspectral imagery. In: {Proceedings of International Conference Acoustics, Speech and Signal Processing (ICASSP)}, Prague, 2011. 1089--1092. Google Scholar

[20] Tarabalka Y, Tilton J C, Benediktsson J A, et al. A marker-based approach for the automated selection of a single segmentation from a hierarchical set of image segmentations. IEEE J Sel Top Appl Earth Observ Remote Sens, 2012, 5: 262-272 CrossRef Google Scholar

[21] Widayati A, Verbist B, Meijerink A. Application of combined pixel-based and spatial-based approaches for improved mixed vegetation classification using IKONOS. In: Proceedings of 23rd Asian Conference on Remote Sensing, Kathmandu, 2002. 1--8. Google Scholar

[22] Briem G, Benediktsson J A, Sveinsson J R. Multiple classifiers applied to multisource remote sensing data. IEEE Trans Geosci Remote Sens, 2002, 40: 2291-2299 CrossRef Google Scholar

[23] Kittler J, Hatef M, Duin R P W, et al. On combining classifiers. {IEEE Trans Patt Anal Mach Intell}, 1998, 20, 226--239. Google Scholar

[24] Liu B, Hu H, Wang H. Superpixel-based classification with an adaptive number of classes for polarimetric SAR images. IEEE Trans Geosci Remote Sens, 2013, 51: 907-924 CrossRef Google Scholar

[25] Jung M, Henkel K, Herold M, et al. Exploiting synergies of global land cover products for carbon cycle modeling. Remot Sens Environ, 2006, 101: 534-553 CrossRef Google Scholar

[26] Dópido I, Villa A, Plaza A, et al. A quantitative and comparative assessment of unmixing-based feature extraction techniques for hyperspectral image classification. IEEE J Sel Top Appl Earth Observ Remote Sens, 2012, 5: 421-iffalse CrossRef Google Scholar

[27] Tuia D, Pacifici F, Kanevski M, et al. Classification of very high spatial resolution imagery using mathematical morphology and support vector machines. IEEE Trans Geosci Remote Sens, 2009, 47: 3866-3879 CrossRef Google Scholar

[28] Fauvel M, Benediktsson J A, Chanussot J, et al. Spectral and spatial classification of hyperspectral data using SVMs and morphological profiles. IEEE Trans Geosci Remote Sens, 2008, 46: 3804-3814 CrossRef Google Scholar

[29] Tarabalka Y, Benediktsson J A, Chanussot J, et al. Spectral-spatial classification of hyperspectral imagery based on partitional clustering techniques. IEEE Trans Geosci Remote Sens, 2009, 47: 2973-2987 CrossRef Google Scholar

[30] Tarabalka Y, Chanussot J, Benediktsson J A. Segmentation and classification of hyperspectral images using watershed transformation. Patt Recogn, 2010, 43: 2367-2379 CrossRef Google Scholar

[31] Li J, Bioucas-Dias J M, Plaza A. Spectral-spatial classification of hyperspectral data using loopy belief propagation and active learning. IEEE Trans Geosci Remote Sens, 2013, 51: 844-856 CrossRef Google Scholar

[32] Mu\ {n}oz-Mar\'{\i} J, Tuia D, Camps-Valls G. Semisupervised classification of remote sensing images with active queries. IEEE Trans Geosci Remote Sens, 2012, 50: 3751-3763 CrossRef Google Scholar

[33] Tuia D, Mu\ {n}oz-Mar\'{\i} J, Camps-Valls G. Remote sensing image segmentation by active queries. Patt Recogn, 2012, 45: 2180-fi CrossRef Google Scholar

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