CVA multi-scale remote sensing image change detection combined with feature selection

doi:10.13474/j.cnki.11-2246.2020.0257

Abstract

Abstract: Aiming at the problems of the object-oriented method can not detect the subtle changes in the image, the segmentation effect, and the high false alarm rate in the pixel-oriented method in the multi-temporal change detection. In this paper, a combined with multiple characteristics change vector analysis (CVA) method based on pixel and multi-level segmentation joint discriminant method based on the object. Firstly, the spectral and texture features of different time phases are extracted, and the maximum correlation minimum redundancy (mRMR) algorithm is used to select the features and the pixel-level change detection results are obtained by CVA. Then the two images are superimposed and split, and the regional merge strategy is used to make different scale detection, obtain the test results of each scale. And finally combine the multiple test results to determine the final change test results. The experimental results show that the proposed method can effectively reduce the missed detection rate and improve the accuracy of detection.

Key words: feature fusion, feature selection, multi-scale segmentation, change vector analysis, decision-level fusion

CLC Number:

P237

CAI Fusheng, XIANG Zejun, CAI Heng, SHAN Deming. CVA multi-scale remote sensing image change detection combined with feature selection[J]. Bulletin of Surveying and Mapping, 2020, 0(8): 101-104,130.

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