Recent Advances in Remote Sensing Change Detection – A Review

Abstract

Change detection is a key methodology in remote sensing. Despite numerous important papers dedicated to this topic no comprehensive review of the subject currently exists. Most existing reviews are limited to certain fields of application or are simply no longer current. Here, we provide an overview of the most important algorithms used for change detection and their development and refinement through time. Change detection cannot be considered as a mere algorithm. We thus describe all of the steps necessary to perform change detection as part of a process chain. We also show how such a change detection process chain may be adapted to specific individual requirements. The labeling of changes is recognized as a basic part of this process. Three change labeling categories are introduced: (1) pre-change extraction labeling, (2) concurrent labeling, and (3) post-change extraction labeling. Methods developed specifically for use with synthetic aperture radar (SAR) data are a focus of this review. SAR data provide information compatible with data produced by optical sensors, but also often require specialized processing techniques and are useful within a unique field of application. An examination of time series analysis methods is also included in this review. To date, these techniques have not been considered in reviews, but the increasing availability of remote sensing data as well as recent advances in remote sensing change detection make it essential that they are included here. Although not exhaustive, this review is intended to provide a comprehensive overview of well established change detection methods as well as recent advances in this field.