Land cover change detection over mining areas based on support vector machine
Abstract：A supervised change detection approach based on support vector machine （SVM） is proposed by making full use of the SVMrs good capacity for two-class separation. The technical flow of change detection based on the SVM is designed and implemented, in which the differ- ence images are generated from multi-temporal remote sensing images at first, and then all pix- els are labeled as changed and unchanged by binary SVM detector. In order to obtain the de- tailed land cover change information, samples are selected from the unchanged areas to train a SVM classifier, by which land cover of change areas are classified, and a change matrix is con- structed. Multi-temporal advanced land observing satellite （ALOS） images over a mining area are used as experimental data. By comparing the proposed SVM-based method with change vec- tor analysis and image differencing, it is concluded that the SVM-based method can obtain higher accuracy than other methods and provide detailed change type and direction information at the same time. The proposed SVM-based change detection method is a promising approach for land use/cover change monitoring over mining areas and other regions.
Keywords：change detectionsupport vector machineland cover changetextural features
Publication Date：2012-01-01
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：6( 262-267 )