Constrained weighted least squares approaches for target detection and classification in hyperspectral imagery

Hsuan Ren, Qian Du, James Jensen

研究成果: 會議貢獻類型會議論文同行評審

6 引文 斯高帕斯(Scopus)

摘要

Least squares unmixing methods are widely used to solve linear mixture problems for endmember abundance estimation in hyperspectral imagery. In this paper, a weighted least squares method is introduced as a generalization. When different weight matrix is used, a certain detector or classifier will be resulted. For accurate abundance fraction estimation, a constrained weighted least squares approach is developed by combining sum-to-one and nonnegativity constraints. The experimental results show that when a meaningful weight matrix is applied as a data pre-processing operator, the weighted least squares method will outperform ordinary least squares solution and the constrained methods will outperform unconstrained ones.

原文???core.languages.en_GB???
頁面3426-3428
頁數3
出版狀態已出版 - 2002
事件2002 IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2002) - Toronto, Ont., Canada
持續時間: 24 6月 200228 6月 2002

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???event.eventtypes.event.conference???2002 IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2002)
國家/地區Canada
城市Toronto, Ont.
期間24/06/0228/06/02

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