Adversarial autoencoder for hyperspectral anomaly detection

doi:10.11947/j.AGCS.2023.20220635

Abstract

Abstract: 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 of 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 of 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.

Key words: hyperspectral Imagery, anomaly detection, autoencoder, adversarial autoencoder, adversarial learning

CLC Number:

P237

DU Qian, XIE Weiying. Adversarial autoencoder for hyperspectral anomaly detection[J]. Acta Geodaetica et Cartographica Sinica, 2023, 52(7): 1105-1114.

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