A New Framework of Target Detection in Hyperspectral Images

Hyperspectral Image (HSI) is used widely in many areas, especially in the remote sensing field. Compared with the traditional remote sensing HSI, the large-scale and high-resolution HSI (LHHSI) which has big data and large size is high-resolution both in spatial domain and spectral domain. However, traditional methods of automatic target detection do not apply to LHHSI. Therefore, this paper proposes a novel framework of automatic target detection for LHHSI based on spatial-spectral interest point (SSIP). It contains five key steps. Firstly, bands selection of LHHSI is used to reduce spectral dimension of LHHSIs. Second, we extract candidate SSIPs from the LHHSIs. Third, we need to determine whether there exist potential target regions by using spectral curves of many selected key SSIPs. And next, the image which contains the potential target regions is divided into image blocks by using quad-tree segmentation, and then every image block is represented by a vector with BoW model based on the selected SSIPs. Finally, these image blocks are classified with SVM. During the classification, if the result is what we need, the quad-tree segmentation of the current block will be ended. The experimental results show that the proposed algorithm has a better performance than traditional algorithms.