Affiliation:
1. School of Geographical Sciences, Liaoning Normal University, Dalian 116029, China
2. School of Computer and Artificial Intelligence, Liaoning Normal University, Dalian 116029, China
Abstract
Recently, the methods based on the autoencoder reconstruction background have been applied to the area of hyperspectral image (HSI) anomaly detection (HSI-AD). However, the encoding mechanism of the autoencoder (AE) makes it possible to treat the anomaly and the background indistinguishably during reconstruction, which can result in a small number of anomalous pixels still being included in the acquired reconstruction background. In addition, the problem of redundant information in HSIs also exists in reconstruction errors. To this end, a fully convolutional AE hyperspectral anomaly detection (AD) network with an attention gate (AG) connection is proposed. First, the low-dimensional feature map as a product of the encoder and the fine feature map as a product of the corresponding decoding stage are simultaneously input into the AG module. The network context information is used to suppress the irrelevant regions in the input image and obtain the significant feature map. Then, the features from the AG and the deep features from upsampling are efficiently combined in the decoder stage based on the skip connection to gradually estimate the reconstructed background image. Finally, post-processing optimization based on guided filtering (GF) is carried out on the reconstruction error to eliminate the wrong anomalous pixels in the reconstruction error image and amplify the contrast between the anomaly and the background.
Funder
National Natural Science Foundation of China
Subject
General Earth and Planetary Sciences
Reference34 articles.
1. Ben Salem, M., Ettabaa, K.S., and Hamdi, M.A. (2014, January 5–7). Anomaly Detection in Hyperspectral Imagery: An Overview. Proceedings of the International Image Processing, Applications and Systems Conference, Sfax, Tunisia.
2. Racetin, I., and Krtalić, A. (2021). Systematic review of anomaly detection in hyperspectral remote sensing applications. Appl. Sci., 11.
3. Adaptive multiple-band CFAR detection of an optical pattern with unknown spectral distribution;Reed;IEEE Trans. Acoust. Speech Signal Process.,1990
4. A Density-Based Cluster Kernel RX Algorithm for Hyperspectral Anomaly Detection;Hao;Spectrosc. Spectr. Anal.,2019
5. Li, Z., and Zhang, Y. (August, January 28). Hyperspectral Anomaly Detection Based on Improved RX with CNN Framework. Proceedings of the IGARSS 2019–2019 IEEE International Geoscience and Remote Sensing Symposium, Yokohama, Japan.
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