SUBPIXEL MAPPING OF HYPERSPECTRAL IMAGES BASED ON COLLABORATIVE REPRESENTATION

被引:2
作者
Xue, Xiaoqin [1 ]
Zhang, Yifan [1 ]
Zhao, Tuo [1 ]
He, Mingyi [1 ]
机构
[1] Northwestern Polytech Univ, Sch Elect & Informat, Shaanxi Key Lab Informat Acquisit & Proc, Xian 710129, Peoples R China
来源
2016 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS) | 2016年
基金
中国国家自然科学基金;
关键词
Classification; collaborative representation; hyperspectral; subpixel mapping; CLASSIFICATION;
D O I
10.1109/IGARSS.2016.7729853
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
Subpixel mapping with a low resolution hyperspectral image as the only input is widely applicable due to the fact that auxiliary image with high spatial resolution is not always available in practice. In this paper, to extract spatial information without auxiliary image, the upscaled low resolution hyperspectral image is classified using collaborative representation-based classifier. Another subpixel scale classification map is available by the combination of collaborative representation-based classification, spectral unmixing and subpixel spatial attraction model. To achieve better classification performance, decision fusion is employed to elect approximate class label from these two initial classification maps for each subpixel by the voting of the neighboring subpixels. Experimental results illustrate that the proposed approach is more promising in extracting and utilizing spatial information compared with some state-of-the-art subpixel mapping approaches.
引用
收藏
页码:3298 / 3301
页数:4
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