KACNet: Kolmogorov-Arnold Convolution Network for Hyperspectral Anomaly Detection

Hyperspectral images capture numerous narrow spectral bands to provide detailed information to identify and locate targets, making them highly suitable for anomaly detection tasks. In recent years, deep learning techniques have demonstrated impressive capabilities and prospects in hyperspectral anom...

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Bibliographic Details
Published in:	IEEE transactions on geoscience and remote sensing Vol. 63; pp. 1 - 14
Main Authors:	Wu, Zhaoyue, Lu, Hailiang, Paoletti, Mercedes E., Su, Hongjun, Jing, Weipeng, Haut, Juan M.
Format:	Journal Article
Language:	English
Published:	New York IEEE 2025 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	Anomalies Anomaly detection Artificial neural networks Background noise Clustering convolutional autoencoder (AE) Deep learning Dictionaries Feature extraction hyperspectral images (HSIs) Hyperspectral imaging Image reconstruction Kernel Kolmogorov-Arnold network (KAN) Machine learning Multilayer perceptrons Neural networks Noise Spectral bands Tensors Transformers
ISSN:	0196-2892, 1558-0644
Online Access:	Get full text
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