2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information

2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information
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Paper Detail

Paper IDIVMSP-12.4
Paper Title ROBUST GRAPH AUTOENCODER FOR HYPERSPECTRAL ANOMALY DETECTION
Authors Ganghui Fan, Yong Ma, Jun Huang, Xiaoguang Mei, Jiayi Ma, Wuhan University, China
SessionIVMSP-12: Image & Video Interpretation and Understanding
LocationGather.Town
Session Time:Wednesday, 09 June, 14:00 - 14:45
Presentation Time:Wednesday, 09 June, 14:00 - 14:45
Presentation Poster
Topic Image, Video, and Multidimensional Signal Processing: [IVARS] Image & Video Analysis, Synthesis, and Retrieval
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
Abstract Autoencoder can not only extract features in an unsupervised manner, but also selects samples out that differs significantly from others. However, autoencoder is sensitive to noise and anomalies during training, and the relationships between pixels are discarded. In order to tackle these problems, we propose a robust graph autoencoder (RGAE) for hyperspectral anomaly detection. To be specific, we first redesign the objective function to encourage the network more robust to noise and anomalies. Meanwhile, a superpixel segmentation-based graph regularization term (SuperGraph) is incorporated into AE to preserve the geometric structure and spatial information simultaneously. Experiments with three real data sets are conducted to evaluate the performance, and the detection results demonstrate that our method outperforms other state-of-the-art hyperspectral anomaly detectors.