A Novel Change Point Detection Approach for Analysis of Time-Ordered Satellite Imagery

Abstract

Change detection through the analysis of images is a fundamental step in the remote sensing analysis framework. It is an emerging area of research with many methods and algorithms in place. Most of the work till date is based on applied change detection methods to a pair of images in the case of satellite imagery. This paper proposes a novel change point detection methodology for a time-ordered set of images which shall enhance the efficiency within the overall image processing framework. It focuses on change detection applied to the set of time-ordered images to identify the exact pair of bi-temporal images about the change point, thereby being of great value to the image analyst in the overall image processing workflow. The paper proposes a metric to detect changes in time-ordered image series in the form of rank ordered threshold values extracted post-application of the segmentation algorithms to the bi-temporal image pair differences derived from time-ordered images. The rank of the threshold value specific to the image pair indicates the relevance and quantum of changes in the image pair among the entire time-ordered image series. A higher rank of threshold conforms to the pair of images with higher image changes. A total of four segmentation algorithms including the well-known Otsu, minimum cross-entropy method by Li et al. methods have been applied to a set of ten time-ordered satellite image sequences as part of the study. Li’s cross-entropy method is found to provide the best results for the determination of the change point. Such a change point detection methodology is applicable not only to satellite imagery but also to a general time-ordered set of images and video frames. © 2022, Indian Society of Remote Sensing.