IMAGE FUSION AND SPECTRAL UNMIXING OF HYPERSPECTRAL IMAGES FOR SPATIAL IMPROVEMENT OF CLASSIFICATION MAPS

被引：17
作者：
Licciardi, G. A. [1 ]
Villa, A.
Khan, M. M.
Chanussot, J. [1 ]
机构：
[1] Grenoble Inst Technol, GIPSA Lab, Grenoble, France
来源：
2012 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS) | 2012年
关键词：
Unmixing; spatial enhancement; image fusion; classification; NEURAL-NETWORK;
D O I：
10.1109/IGARSS.2012.6351978
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：
In this paper we propose a new approach for the improvement of the spatial resolution of hyperspectral image classification maps combining both spectral unmixing and pansharpening approaches. The main idea is to use a spectral unmixing algorithm based on neural networks to retrieve the abundances of the endmembers present in the scene, and then use the spatial information retrieved from the pansharpened image to find the location of each endmember within the enhanced pixel according to the endmembers abundances. The proposed approach has been applied both to real and synthetic datasets.
引用
收藏
页码：7290 / 7293
页数：4
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