Hyperspectral Anomaly Detection based on Autoencoder using Superpixel Manifold Constraint
Paper
Paper/Presentation Title | Hyperspectral Anomaly Detection based on Autoencoder using Superpixel Manifold Constraint |
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Presentation Type | Paper |
Authors | Gan, Yuquan, Liu, Wenqiang, Liu, Ying, He, Jinglu and Zhang, Ji |
Journal or Proceedings Title | Proceedings of the 2022 5th International Conference on Artificial Intelligence and Pattern Recognition (AIPR 2022) |
Journal Citation | pp. 873-879 |
Number of Pages | 7 |
Year | 2023 |
Publisher | Association for Computing Machinery (ACM) |
Place of Publication | United States |
ISBN | 9781450396899 |
Digital Object Identifier (DOI) | https://doi.org/10.1145/3573942.3574108 |
Web Address (URL) of Paper | https://dl.acm.org/doi/10.1145/3573942.3574108 |
Web Address (URL) of Conference Proceedings | https://dl.acm.org/doi/proceedings/10.1145/3573942 |
Conference/Event | 2022 5th International Conference on Artificial Intelligence and Pattern Recognition (AIPR 2022) |
Event Details | 2022 5th International Conference on Artificial Intelligence and Pattern Recognition (AIPR 2022) Parent International Conference on Artificial Intelligence and Pattern Recognition Delivery In person Event Date 23 to end of 25 Sep 2022 Event Location Xiamen China |
Abstract | In the field of hyperspectral anomaly detection, autoencoder (AE) have become a hot research topic due to their unsupervised characteristics and powerful feature extraction capability. However, autoencoders do not keep the spatial structure information of the original data well during the training process, and is affected by anomalies, resulting in poor detection performance. To address these problems, a hyperspectral anomaly detection method based on autoencoders with superpixel manifold constraints is proposed. Firstly, superpixel segmentation technique is used to obtain the superpixels of the hyperspectral image, and then the manifold learning method is used to learn the embedded manifold that based on the superpixels. Secondly, the learned manifold constraints are embedded in the autoencoder to learn the potential representation, which can maintain the consistency of the local spatial and geometric structure of the hyperspectral images (HSI). Finally, anomalies are detected by computing reconstruction errors of the autoencoder. Extensive experiments are conducted on three datasets, and the experimental results show that the proposed method has better detection performance than other hyperspectral anomaly detectors. |
Keywords | Autoencoder; Superpixel segmentation; Manifold constraints; Anomaly detection; Structure information |
ANZSRC Field of Research 2020 | 460299. Artificial intelligence not elsewhere classified |
Public Notes | Files associated with this item cannot be displayed due to copyright restrictions. |
Byline Affiliations | Xi’an University of Posts and Telecommunications, China |
University of Southern Queensland |
https://research.usq.edu.au/item/z58y9/hyperspectral-anomaly-detection-based-on-autoencoder-using-superpixel-manifold-constraint
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