高光谱成像
像素
能量(信号处理)
探测器
人工智能
能量最小化
缩小
模式识别(心理学)
计算机科学
领域(数学分析)
数学
计算机视觉
算法
统计
物理
数学优化
量子力学
电信
数学分析
作者
Xiaobin Zhao,Zengfu Hou,Xin Wu,Wei Li,Pengge Ma,Ran Tao
出处
期刊:International journal of applied earth observation and geoinformation
日期:2021-08-04
卷期号:103: 102461-102461
被引量:25
标识
DOI:10.1016/j.jag.2021.102461
摘要
Traditional hyperspectral target detection methods use spectral domain information for target recognition. Although it can effectively retain intrinsic characteristics of substances, targets in homogeneous regions still cannot be effectively recognized. By projecting the spectral domain features on the transform domain to increase the separability of background and target, fractional domain-based revised constrained energy minimization detector is proposed. Firstly, the fractional Fourier transform is adopted to project the original spectral information into the fractional domain for improving the separability of background and target. Then, a newly revised constrained energy minimization detector is performed, where sliding double window strategy is used to make the best of the local spatial statistical characteristics of testing pixel. In order to make the best of inner window information, the mean value of Pearson correlation coefficient is measured between prior target pixel and testing pixel associated with its four neighborhood pixels. Extensive experiments for four real hyperspectral scenes indicate that the performance of the proposed algorithm is excellent when compared with other related detectors.
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