基于SAR图像的双时相变化检测研究综述

刘玉婷; 李世华; 何泽; 刘开通

doi:10.11834/jrs.20255072

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专辑

基于SAR图像的双时相变化检测研究综述
Review of bitemporal change detection using SAR imagery
2026年30卷第1期页码：11-41
收稿：2025-03-12，

纸质出版：2026-01-07
DOI： 10.11834/jrs.20255072
稿件说明：

移动端阅览

刘玉婷，李世华，何泽，刘开通.2026.基于SAR图像的双时相变化检测研究综述.遥感学报，30（1）： 11-41 DOI： 10.11834/jrs.20255072.

Liu Y T，Li S H，He Z and Liu K T. 2026. Review of bitemporal change detection using SAR imagery. National Remote Sensing Bulletin， 30（1）：11-41 DOI： 10.11834/jrs.20255072.

摘要

遥感变化检测作为一种重要的监测技术，广泛应用于城市规划、灾害评估、资源管理等领域。合成孔径雷达SAR（Synthetic Aperture Radar）具备全天时、全天候成像能力，克服了光学变化检测受天气和光照影响的问题。其微波穿透能力、极化特性和相干成像机制，使其在监测次地表与遮挡区域变化、地物结构与物理特性变化，以及微小变化等方面具有独特优势。目前，大多数遥感双时相变化检测综述主要围绕光学遥感图像展开，缺乏对基于SAR图像变化检测的系统且针对性的总结。此外，随着深度学习与多源融合技术的发展，基于SAR图像的同构和异构变化检测已成为前沿热点。因此，文章基于上百篇国内外经典和最新文献，结合SAR卫星成像原理，首先探讨了不同成像条件下的变化检测典型应用。然后，构建了包括数据获取、图像预处理、变化识别与后处理等环节的双时相变化检测一般流程。在此基础上，系统梳理了主流的变化检测数据和方法。数据部分涵盖基于SAR的同构与异构变化检测开源数据集，方法部分涵盖传统方法以及深度学习方法中的同构与异构变化检测研究。特别地，整理了相关数据集和模型代码链接，为后续领域研究提供重要参考。最后，本文从数据、算法和应用3个层面总结了当前领域面临的主要挑战，如数据质量与数量、模型特征表达能力与计算复杂度、变化检测应用范围等。针对这些挑战，展望了未来多模态融合、智能化与轻量化模型设计以及多元变化检测的发展方向。

Abstract

Remote sensing Change Detection (CD)

as a crucial monitoring technique

has been widely applied in fields such as urban planning

disaster assessment

and resource management. Synthetic aperture radar (SAR) possesses all-weather

all-time imaging capabilities

effectively overcoming the limitations of optical CD that are influenced by weather conditions and illumination variations. Its microwave penetration ability

polarization characteristics

and coherent imaging mechanism provide unique advantages in monitoring subsurface and occluded changes

object structure and physical property variations

and subtle changes. Currently

most existing reviews on bi-temporal CD focus on optical imagery and deep learning algorithms

lacking a systematic and targeted summary of SAR-based CD. Furthermore

with the advancement of deep learning and multisource data fusion

homogeneous and heterogeneous CD based on SAR imagery have emerged as prominent research frontiers.

On the basis of the challenges outlined above

this article draws upon classical and recent literature to first explore the diverse application scenarios and domains of CD under varying imaging conditions. SAR imaging modes mainly include Stripmap

ScanSAR

and Spotlight

each of which has distinct advantages in resolution and coverage

catering to diverse CD tasks. Polarization modes

such as single

dual

and full polarization

affect the change type and richness of information extracted from SAR data. Different frequency bands

such as L-band

C-band

and X-band

exhibit varying penetration capabilities and sensitivities. With the increasing availability of multitemporal SAR and multisource data

the applications of SAR-based CD are expanding. Multitemporal SAR leverages time-series information to enhance the detection of periodic changes

while the fusion of SAR and optical data overcomes single-sensor limitations

thereby advancing multimodal CD methods.

Then

a comprehensive SAR CD framework is constructed

which includes data acquisition

image preprocessing

change identification

and post-processing. During data acquisition

factors such as satellite coverage

imaging mode

and band selection must be considered. Image preprocessing primarily involves image co-registration

radiometric correction

geometric correction

and noise reduction. In the change identification phase

traditional homogeneous CD methods primarily depend on the generation of difference images followed by detailed difference analysis. Conventional heterogeneous CD approaches typically employ post-classification

similarity measurement

and feature space transformation to establish cross-modal correspondences. By contrast

deep learning-based methods utilize neural networks to automatically extract homogeneous or heterogeneous features and identify changes. Finally

the post-processing focuses on accuracy assessment and validation of the detection results.

Moreover

the article systematically reviews the mainstream datasets and methods for SAR-based homogeneous and heterogeneous CD. Homogeneous datasets consist of bi-temporal SAR images from the same sensor

while heterogeneous datasets typically include SAR and optical image pairs. These datasets offer preprocessed imagery and accurate binary change labels to support learning and evaluation. The reviewed methods encompass both traditional and deep learning-based approaches tailored for these two scenarios. Traditional methods offer computational efficiency but rely heavily on manually designed features. Deep learning methods

with their powerful learning capabilities

modality adaptability

and end-to-end modeling advantages

can effectively extract both unimodal and multimodal features. Additionally

curated links to datasets and model codes are provided to support future research in this field.

Finally

the main challenges in the field are summarized from the perspectives of data

algorithms

and applications

including issues such as data quality and availability

model representational capacity and computational complexity

and the limited scope of current CD applications. In response to these challenges

future research directions are proposed

focusing on multimodal data fusion

intelligent and lightweight model design

and diversified CD applications.

关键词

Keywords

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