The estimation of noise covariance matrix in hyperspectral remotely sensed images

Chien Wen Chen, Hsuan Ren

研究成果: 書貢獻/報告類型會議論文篇章同行評審

3 引文 斯高帕斯(Scopus)


Target detection algorithms for hyperspectral remote sensing have been studied for decades. The Least Square (LS) approach is one of the most widely used algorithms. It has been proved that the Noise Whitened Least Square (NWLS) can outperform the original version. But in order to have good results, the estimation of the noise covariance matrix is very important and still remains a great challenge. Many estimation methods have been proposed in the past, including spatial and frequency domain high-pass filter, neighborhood pixel subtraction, etc. In this paper, we further adopt the Fully Constrained Least Square (FCLS), which combine sum-to-one and non-negative constraints, with the NWLS and we also conduct a quantitative comparison with computer simulation of material spectrum from AVIRIS data base on the detection performance and the difference from the designed noise covariance matrix. We will also compare the results with real AVIRIS image scene.

主出版物標題Imaging Spectrometry XI
出版狀態已出版 - 2006
事件Imaging Spectrometry XI - San Diego, CA, United States
持續時間: 14 8月 200616 8月 2006


名字Proceedings of SPIE - The International Society for Optical Engineering


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國家/地區United States
城市San Diego, CA


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