Clever eye algorithm for target detection of remote sensing imagery

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• 作者：Xiurui Geng^a ; ^{gengxr@sina.com.cn" class="auth_mail" title="E-mail the corresponding author} ; Luyan Ji^b ; ^{jily@mail.ustc.edu.cn" class="auth_mail" title="E-mail the corresponding author} ; Kang Sun^c ; ^{sunkang-1234@163.com" class="auth_mail" title="E-mail the corresponding author}
• 关键词：Hyperspectral data ; Target detection ; Matched filter ; Constrained energy minimization
• 刊名：ISPRS Journal of Photogrammetry and Remote Sensing
• 出版年：2016
• 出版时间：April 2016
• 年：2016
• 卷：114
• 期：Complete
• 页码：32-39
• 全文大小：2521 K

文摘

Target detection algorithms for hyperspectral remote sensing imagery, such as the two most commonly used remote sensing detection algorithms, the constrained energy minimization (CEM) and matched filter (MF), can usually be attributed to the inner product between a weight filter (or detector) and a pixel vector. CEM and MF have the same expression except that MF requires data centralization first. However, this difference leads to a difference in the target detection results. That is to say, the selection of the data origin could directly affect the performance of the detector. Therefore, does there exist another data origin other than the zero and mean-vector points for a better target detection performance? This is a very meaningful issue in the field of target detection, but it has not been paid enough attention yet. In this study, we propose a novel objective function by introducing the data origin as another variable, and the solution of the function is corresponding to the data origin with the minimal output energy. The process of finding the optimal solution can be vividly regarded as a clever eye automatically searching the best observing position and direction in the feature space, which corresponds to the largest separation between the target and background. Therefore, this new algorithm is referred to as the clever eye algorithm (CE). Based on the Sherman–Morrison formula and the gradient ascent method, CE could derive the optimal target detection result in terms of energy. Experiments with both synthetic and real hyperspectral data have verified the effectiveness of our method.