收稿日期: 2008-12-31
修回日期: 2009-04-01
网络出版日期: 2009-05-10
基金资助
国家重点基础研究发展计划项目“陆表生态环境要素主被动遥感协同反演理论与方法”(编号:2007CB714406);中国高技术研究发展计划项目“卫星遥感SAR与光学影像自动配准与融合技术系统研究”(编号:2007AA12Z157);中国科学院知识创新工程青年人才领域前沿专项项目“多种地表覆盖条件下遥感邻近效应测量与校正方法研究”(编号:08S01100CX)资助.
State of the art on remotely sensed data classification based on support vector machines
Received date: 2008-12-31
Revised date: 2009-04-01
Online published: 2009-05-10
张睿 , 马建文 . 支持向量机在遥感数据分类中的应用新进展[J]. 地球科学进展, 2009 , 24(5) : 555 -562 . DOI: 10.11867/j.issn.1001-8166.2009.05.0555
Support Vector Machine (SVM) is a state-of-the-art machine learning algorithm based on statistical learning theory. It tries to find the optimal classification hyperplane in high dimensional feature space to handle complicated classification and regression problems by solving optimization problems. With the development of the theory and its applications, SVM has been used in remote sensing community successfully. SVM has been applied to land cover/land use classification for remotely sensed data, change detection for multi-temporal remote sensing data, and information fusion for multiple source data. Moreover, it has become a standard technique for hyperspectral data process. In this paper, the applications of SVM in remote sensing are reviewed. First, we introduced the basic theory of the SVM briefly. Then we reviewed the state of the art in different remote sensing applications. At last, we stated the development of several new SVM algorithms, which were derived from the SVM theory, and applications in remote sensing community.
Key words: Support vector; Classification for remotely sensed data; Review.
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