A Target Detection Method Based on Low-Rank Regularized Least Squares Model for Hyperspectral Images

2016 ◽  
Vol 13 (8) ◽  
pp. 1129-1133 ◽  
Author(s):  
Yang Xu ◽  
Zebin Wu ◽  
Fu Xiao ◽  
Tianming Zhan ◽  
Zhihui Wei
Keyword(s):  
Least Squares ◽  
Target Detection ◽  
Detection Method ◽  
Hyperspectral Images ◽  
Low Rank ◽  
Regularized Least Squares

scholarly journals Background Information Self-Learning Based Hyperspectral Target Detection

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-7
Author(s):  
Yufei Tian ◽  
Jihai Yang ◽  
Shijun Li ◽  
Wenning Xu
Keyword(s):  
Information Extraction ◽  
Hyperspectral Imaging ◽  
Target Detection ◽  
Detection Method ◽  
Experimental Results ◽  
Hyperspectral Images ◽  
Background Information ◽  
Spectral Information ◽  
Tumor Region ◽  
Self Learning

Hyperspectral imaging has been proved as an effective way to explore the useful information behind the land objects. And it can also be adopted for biologic information extraction, by which the origin information can be acquired from the image repeatedly without contamination. In this paper we proposed a target detection method based on background self-learning to extract the biologic information from the hyperspectral images. The conventional unstructured target detectors are very difficult to estimate the background statistics accurately in either a global or local way. Considering the spatial spectral information, its performance can be further improved by avoiding the above problem. It is especially designed to extract fingerprint and tumor region from hyperspectral biologic images. The experimental results show the validity and the superiority of our method on detecting the biologic information from hyperspectral images.

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