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.