Adversarial autoencoder for hyperspectral anomaly detection

被引：0
|
作者：
Du Q. [1 ]
Xie W. [2 ]
机构：
[1] Electrical and Computer Engineering, Mississippi State University, 39762, MS
[2] State Key Laboratory of Integrated Services Networks, Xidian University, Xi'an
来源：
Cehui Xuebao/Acta Geodaetica et Cartographica Sinica | 2023年 / 52卷 / 07期
基金：
中国国家自然科学基金;
关键词：
adversarial autoencoder; adversarial learning; anomaly detection; autoencoder; hyperspectral Imagery;
D O I：
10.11947/J.AGCS.2023.20220635
中图分类号：
学科分类号：
摘要：
Autoencoder (AE) is a typical generative model. It has been widely used due to its simple learning process, good ability for convergence, and unsupervised nature. To improve the performance ot AE whose objective function is merely input-output reconstruction error, adversarial autoencoder (AAE) has been proposed, which can provide variational inference to the network output. This paper reviews the use of unsupervised and semisupervised AAE in hyperspectral anomaly detection (HAD). The performance ot AAE can be improved by adding adversarial learning between the input of the encoder and the output of the decoder, in addition to the adversarial learning in the latent space in the original AAE. In this way, the network can focus more on learning data distribution rather than point-to-point data reconstruction. The idea of using these deep learning models is beyond the concept of traditional HAD methods, and can significantly improve the detection performance, as demonstrated by real data experiments. © 2023 SinoMaps Press. All rights reserved.
引用
收藏
页码：1105 / 1114
页数：9
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