自编码
高光谱成像
计算机科学
人工智能
平滑的
模式识别(心理学)
编码器
像素
离群值
正规化(语言学)
自适应采样
计算机视觉
深度学习
数学
操作系统
统计
蒙特卡罗方法
作者
Ziqiang Hua,Xiaorun Li,Qunhui Qiu,Liaoying Zhao
出处
期刊:IEEE Geoscience and Remote Sensing Letters
[Institute of Electrical and Electronics Engineers]
日期:2021-09-01
卷期号:18 (9): 1640-1644
被引量:18
标识
DOI:10.1109/lgrs.2020.3005999
摘要
Autoencoder is an efficient technique for unsupervised feature learning, which can be applied to hyperspectral unmixing. In this letter, we present an autoencoder network with adaptive abundance smoothing (AAS) to solve the challenges of previous techniques. Specifically, the proposed method uses a multilayer encoder to obtain the abundance and a single-layer decoder to reconstruct the image. The AAS algorithm tackles the outliers by exploiting the spatial-contextual information and can be adaptive for each pixel. Moreover, the softmax function is used as the encoder output function with the help of L 1/2 regularization to produce sparse output. Experimental results of the synthetic and real data reveal the superior performance of the proposed method against other competitors.
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