Change Detection Methods for Remote Sensing in the Last Decade: A Comprehensive Review

TL;DR

This comprehensive review surveys the last decade of change detection methods in remote sensing, highlighting advancements, challenges, datasets, and future directions, with a focus on deep learning techniques and algorithm taxonomy.

Contribution

It provides a systematic taxonomy of change detection algorithms, summarizes state-of-the-art performance, and discusses future research directions in remote sensing change detection.

Findings

Deep learning has become a dominant tool in change detection.

Existing algorithms show varied strengths and limitations across datasets.

The survey identifies promising future research directions.

Abstract

Change detection is an essential and widely utilized task in remote sensing that aims to detect and analyze changes occurring in the same geographical area over time, which has broad applications in urban development, agricultural surveys, and land cover monitoring. Detecting changes in remote sensing images is a complex challenge due to various factors, including variations in image quality, noise, registration errors, illumination changes, complex landscapes, and spatial heterogeneity. In recent years, deep learning has emerged as a powerful tool for feature extraction and addressing these challenges. Its versatility has resulted in its widespread adoption for numerous image-processing tasks. This paper presents a comprehensive survey of significant advancements in change detection for remote sensing images over the past decade. We first introduce some preliminary knowledge for the change detection task, such as problem definition, datasets, evaluation metrics, and transformer basics, as well as provide a detailed taxonomy of existing algorithms from three different perspectives: algorithm granularity, supervision modes, and learning frameworks in the methodology section. This survey enables readers to gain systematic knowledge of change detection tasks from various angles. We then summarize the state-of-the-art performance on several dominant change detection datasets, providing insights into the strengths and limitations of existing algorithms. Based on our survey, some future research directions for change detection in remote sensing are well identified. This survey paper will shed some light on the community and inspire further research efforts in the change detection task.