高光谱成像
公制(单位)
离群值
正规化(语言学)
数学
符号
规范(哲学)
约束(计算机辅助设计)
算法
计算机科学
人工智能
模式识别(心理学)
算术
经济
运营管理
法学
政治学
几何学
作者
Chunzhi Li,Xiaohua Chen
出处
期刊:IEEE Geoscience and Remote Sensing Letters
[Institute of Electrical and Electronics Engineers]
日期:2020-02-01
卷期号:17 (2): 282-286
被引量:6
标识
DOI:10.1109/lgrs.2019.2920687
摘要
In this letter, a sparse-constrained hyperspectral unmixing method via reconstruction error approximation is proposed. In the presented method, all the noises and the outliers are treated as diverse interferences and addressed to minimize the regularization error. Several techniques are involved in our presented approach: 1) to attenuate the interference of noise, an auxiliary variable is introduced; 2) based on the relative noiseless hyperspectral image, a sparse constraint is employed to achieve the sparsity; and 3) besides, the correntropy-induced metric (CIM), instead of the $L_{2}$ - or $L_{2,1}$ -norm loss function, is utilized to measure the quality of the unmixing model approximation. A series of experiments on the synthetic and real hyperspectral images is conducted, and all the experiment results show the efficacy of the proposed approach.
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