Semisupervised SAR Image Change Detection Using a Cluster-Neighborhood Kernel
作者： Lu Jia , Ming Li , Yan Wu , Peng Zhang , Hongmeng Chen
DOI: 10.1109/LGRS.2013.2295216
关键词: Cluster analysis 、 Kernel (image processing) 、 Artificial intelligence 、 Kernel method 、 Computer vision 、 Pattern recognition 、 Mathematics 、 Radial basis function kernel 、 Contextual image classification 、 Change detection 、 Synthetic aperture radar 、 Support vector machine
摘要: Change detection can be performed in a supervised manner. However, methods for synthetic aperture radar (SAR) image change may suffer from lack of training samples. Therefore, this letter, semisupervised support vector machine classifier based on cluster-neighborhood (CN) kernel is proposed SAR detection. In the method, samples are categorized into two neighborhoods with k-means clustering algorithm. addition, CN constructed composite-ratio using neighborhood-based statistical features. When few labeled available, explores information unlabeled to enhance its discriminative ability and robustness against speckles. Experimental results real demonstrate effectiveness method when available.
harvard.edu
sci-hub.se 参考文章(12)
F. Bovolo, G. Camps-Valls, L. Bruzzone, A support vector domain method for change detection in multitemporal images Pattern Recognition Letters. ,vol. 31, pp. 1148- 1154 ,(2010) , 10.1016/J.PATREC.2009.07.002
Sandro Martinis, André Twele, Stefan Voigt, Unsupervised Extraction of Flood-Induced Backscatter Changes in SAR Data Using Markov Image Modeling on Irregular Graphs IEEE Transactions on Geoscience and Remote Sensing. ,vol. 49, pp. 251- 263 ,(2011) , 10.1109/TGRS.2010.2052816
Michele Volpi, Devis Tuia, Gustavo Camps-Valls, Mikhail Kanevski, Unsupervised Change Detection With Kernels IEEE Geoscience and Remote Sensing Letters. ,vol. 9, pp. 1026- 1030 ,(2012) , 10.1109/LGRS.2012.2189092
Jingjing Ma, Maoguo Gong, Zhiqiang Zhou, Wavelet Fusion on Ratio Images for Change Detection in SAR Images IEEE Geoscience and Remote Sensing Letters. ,vol. 9, pp. 1122- 1126 ,(2012) , 10.1109/LGRS.2012.2191387
G. Camps-Valls, L. Bruzzone, Kernel-based methods for hyperspectral image classification IEEE Transactions on Geoscience and Remote Sensing. ,vol. 43, pp. 1351- 1362 ,(2005) , 10.1109/TGRS.2005.846154
D. Tuia, G. Camps-Valls, Semisupervised Remote Sensing Image Classification With Cluster Kernels IEEE Geoscience and Remote Sensing Letters. ,vol. 6, pp. 224- 228 ,(2009) , 10.1109/LGRS.2008.2010275
Y. Bazi, L. Bruzzone, F. Melgani, An unsupervised approach based on the generalized Gaussian model to automatic change detection in multitemporal SAR images IEEE Transactions on Geoscience and Remote Sensing. ,vol. 43, pp. 874- 887 ,(2005) , 10.1109/TGRS.2004.842441
G. Camps-Valls, L. Gomez-Chova, J. Munoz-Mari, J.L. Rojo-Alvarez, M. Martinez-Ramon, Kernel-Based Framework for Multitemporal and Multisource Remote Sensing Data Classification and Change Detection IEEE Transactions on Geoscience and Remote Sensing. ,vol. 46, pp. 1822- 1835 ,(2008) , 10.1109/TGRS.2008.916201
L. Gomez-Chova, G. Camps-Valls, L. Bruzzone, J. Calpe-Maravilla, Mean Map Kernel Methods for Semisupervised Cloud Classification IEEE Transactions on Geoscience and Remote Sensing. ,vol. 48, pp. 207- 220 ,(2010) , 10.1109/TGRS.2009.2026425
J. Weston, C. Leslie, E. Ie, D. Zhou, A. Elisseeff, W. S. Noble, Semi-supervised protein classification using cluster kernels Bioinformatics. ,vol. 21, pp. 3241- 3247 ,(2005) , 10.1093/BIOINFORMATICS/BTI497