Constrained Total Least Squares Analysis for Target Detection in Remote Sensing Image

详细信息    查看全文 | 下载全文 | 推荐本文 |

• 作者：Shin-Ya HuangHsuan Ren
• 会议时间：2011-08-01
• 关键词：Total least squares ; Sum-to-one constraint ; Non-negative constraint
• 作者单位：Shin-Ya Huang(Department of Computer Science and Information Engineering, National Central University, Jhongli, Taiwan)Hsuan Ren(Center for Space and Remote Sensing Research, National Central University, Jhongli, Taiwan)
• 母体文献：第五届海峡两岸遥感遥测会议论文集
• 会议名称：第五届海峡两岸遥感遥测会议
• 会议地点：哈尔滨
• 主办单位：中国地理学会
• 语种：chi
• 分类号：TP7;TP1

摘要

The least squares approaches are widely used in remote sensing image analysis to solve the linear mixture model. They assume the spectra of the endmemebers are known and fixed vectors for linear unmixing. But it is clearly shown from the spectral libraries that one material has various spectra. Therefore, total least square has been proposed to have the robustness to accommodate those variations and achieve minimum error. In this study, we apply two constraints on the estimated abundance in total least square: sum-to-one and nonnegative constraints. These two constraints ensure the sum of all estimated abundance is one and no abundance fraction is less than zero. The performance comparison with regular least square approaches is conducted with a hyperspectral image scene.