Thresholding process on the dissimilarities between probability models for change detection on remote sensing data

Abstract

Change detection comprises a very important application in environmental studies involving multitemporal data obtained by remote sensing. Developing more accurate change detection methods is an ongoing challenge. Our study presents a new, unsupervised change detection method based on the concepts of stochastic distances and thresholding. To prove the effectiveness of the method, a study was carried out involving a region in southeastern Brazil, from 1999 to 2018, which underwent a high rate of environmental degradation caused by urban, industrial, and sand mining expansion. In this investigation, images obtained by thematic mapper and operational land imager sensors aboard the Landsat-5 and-8 satellites were used. Comparisons with the change vector analysis (CVA) method are included in the analyses. Results showed that the proposed method is capable of providing more accurate results in relation to the CVA method, after adequate parameterization, providing more realistic mappings with greater precision.