A New Framework of Target Detection in Hyperspectral Images

被引:0
作者
Li, Yanshan [1 ,2 ]
Xu, Jianjie [1 ]
Chen, Yayuan [2 ,3 ]
Kong, Zhoufan
Huang, Qinghua [2 ,3 ,4 ,5 ]
机构
[1] Shenzhen Univ, ATR Natl Key Lab Def Technol, Shenzhen, Peoples R China
[2] Shenzhen Univ, Coll Informat Engn, Shenzhen 518060, Peoples R China
[3] South China Univ Technol, Sch Elect & Informat Engn, Guangzhou 510641, Guangdong, Peoples R China
[4] Northwestern Polytech Univ, Sch Elect & Informat, Xian 710072, Shaanxi, Peoples R China
[5] Northwestern Polytech Univ, Ctr OPT IMagery Anal & Learning OPTIMAL, Xian 710072, Shaanxi, Peoples R China
来源
2017 2ND INTERNATIONAL CONFERENCE ON ADVANCED ROBOTICS AND MECHATRONICS (ICARM) | 2017年
基金
中国国家自然科学基金;
关键词
Large-scale; High-resolution; Hyperspectral images; Target detection; Spatial-spectral interest points; CLASSIFICATION; SCALE;
D O I
暂无
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
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.
引用
收藏
页码:144 / 148
页数:5
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