Change Detection Based on IR-MAD Model for GF-5 Remote Sensing Imagery

Abstract

GF-5 is the domestic full spectrum satellite with the most spectral bands, and it can comprehensive observe earth and atmosphere, the data can effectively monitor the changes of ground objects. However, due to the high wave dimension and large data of the hyperspectral remote sensing image, which reduces the processing and operation speed, and brings great uncertainty to the accuracy. In order to improve the accuracy and processing speed of hyperspectral imagery change detection, a method of iterative weighted multivariate change detection based on IR-MAD is proposed. In this paper, the high-resolution remote sensing image of GF-5 is used as the data source. After geometric correction, removal of bad line and other pre-processing methods, the change detection results are obtained by the iterative weighted multivariate (IR-MAD) change detection method. The experiments show that: the algorithm in this paper is compared with change vector analysis (CVA) change detection, principal component change vector analysis (PCA-CVA) change detection method, and iterative weighted multivariate (IR-MAD) detection method without principal component extraction. The detection accuracy of this method is high, and the error rate and missed rate are also low.