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??? |
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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 2002 → 28 6月 2002 |
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???event.eventtypes.event.conference??? | 2002 IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2002) |
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國家/地區 | Canada |
城市 | Toronto, Ont. |
期間 | 24/06/02 → 28/06/02 |