高光谱影像变化检测研究进展  

Advance in Hyperspectral Images Change Detection

作　　者：宋若曦 冯一宁 程伟 王相海[2,3] SONG Ruo-xi;FENG Yi-ning;CHENG Wei;WANG Xiang-hai(Aerospace Information Research Institute,Chinese Academy of Sciences,Beijing 100101,China;School of Computer and Information Technology,Liaoning Normal University,Dalian 116081,China;School of Geography,Liaoning Normal University,Dalian 116029,China)

机构地区：[1]中国科学院空天信息创新研究院,北京100101 [2]辽宁师范大学计算机与信息技术学院,辽宁大连116081 [3]辽宁师范大学地理科学学院,辽宁大连116029

出　　处：《光谱学与光谱分析》2023年第8期2354-2362,共9页Spectroscopy and Spectral Analysis

基　　金：国家自然科学基金项目(41971388);辽宁省高等学校教育厅创新团队支持计划项目(LT2017013)资助。

摘　　要：随着现代遥感技术的快速发展,遥感影像变换检测技术受到重视并被应用到地理国情检测、土地调查、生态系统监测、食品安全保障和军事侦察等领域。高光谱影像所具有的更精细的光谱分辨率以及多时相高光谱影像所提供的更加丰富和更为详细的光谱变化信息为进一步精细判断地表的变化提供了可能。然而高光谱影像高复杂度的数据结构、高维度的数据特征、高冗余信息,以及不同时相光谱信息对环境的敏感性极大地增加了多时相高光谱变化检测的难度。文章以变化检测过程中所涉及的技术手段为主线,首先从六个方面对多时相高光谱影像变化检测的研究动态及现状进行分析,包括:(1)基于高光谱影像间广义相似度度量的传统高光谱影像变化检测方法,该类方法主要沿用了高光谱影像出现之前多光谱变化检测的技术路线;(2)基于降维的高光谱影像变化检测方法,该类方法主要为克服高光谱影像所具有的高维度、高冗余等特性给变化检测带来的不良影响而展开;(3)基于统计建模的高光谱影像变化检测方法,该类方法通过对高光谱影像的统计特性和多维度相关性进行挖掘和建模来确定各像元的变化属性;(4)基于分类方法的高光谱影像变化检测方法,该类方法将图像的分类策略引入到变化检测过程中为获得“from-to”类型的变化信息提供保障;(5)基于光谱解混的高光谱影像变化检测方法,该类方法主要面对高光谱影像低空间分辨率所带来的混合像元问题如何提取精细的变化信息而展开;(6)基于深度学习的高光谱影像变化检测方法,该类方法通过将深度学习技术应用于多时相高光谱变化检测中而产生的一类新兴而具有发展前景的变化检测技术。进一步,对目前多时相高光谱影像变化检测中面临的挑战性问题进行了提炼和分析展望。With the rapid development of modern remote sensing techniques,remote sensing image change detection has become one of the most important means of the land-cover monitoring process.It has been widely used in application areas such as Geographic Situation Detection,Land Survey,Ecosystem Monitoring,Disaster Monitoring and Assessment,Food Security Insurance and military reconnaissance.The fine spectral resolution of the hyperspectral(HS)image and the detailed spectral change information of the multitemporal HS images brings the possibility for detecting the subtle changes associated with the dynamic land-cover transition.However,the high complexity data structure,high dimensional data features,and high redundancy information of the HS images makes HS change detection extremely challenging.This paper reviews the research advance of multitemporal HS image change detection,including:(1)Traditional HS image change detection approach based on the generalized similarity measurement of the HS images,which mainly follows the modeling process of multispectral change detection methods;(2)Dimensionality reduction based HS image change detection approaches,which are designed to overcome the adverse effects of the high dimensionality,high redundancy properties of the HS images;(3)Statistical modeling based HS image change detection approaches,which determines the change detection results by modeling of the statistical properties and multi-dimensional correlations of the HS images;(4)Classification based HS image change detection approaches,which introduces the image classification strategy into the change detection process to provide guarantee for obtaining the“from-to”type change information;(5)Unmixing based HS image change detection approaches,which are mainly developed to solve the mixed pixel phenomenon caused by the low spatial resolution of HS images;(6)Deep learning based HS image change detection approaches,which applied the deep learning methods into the HS image change detection tasks.Finally,the three major chall

关 键 词：遥感 高光谱影像 变化检测 多时相 光谱变化 研究进展 

分 类 号：TP751.1[自动化与计算机技术—检测技术与自动化装置]