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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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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Summary: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 anomaly detection (HAD), primarily relying on multilayer perceptrons (MLPs) and convolutional neural networks (CNNs) to extract and represent nonlinear features. However, MLPs and CNNs often require deeper network architectures when dealing with complex high-dimensional data, resulting in a constrained generalization and limited representation of features. To address this issue, and inspired by the recent Kolmogorov-Arnold network (KAN), this article introduces a novel asymmetric convolutional autoencoder (AE) network by integrating KAN and CNN, named KACNet . Specifically, we design a spectral KAN block in the convolutional encoder and a spatial KAN block in the convolutional decoder, to simultaneously enhance the feature extraction and characterization capabilities of the network. Furthermore, to effectively utilize the limited prior information, a weight initialization mechanism based on hierarchical density-based spatial clustering of applications with noise (HDBSCAN) is developed to boost the background recovery. By combining KAN, CNN, and HDBSCAN, the proposed integration enhances the interpretability and reliability of HAD. Extensive experiments are conducted on six public datasets, demonstrating that the KAN poses remarkable performance on background reconstruction, particularly, the proposed KACNet significantly outperforms the other state-of-the-art methods.
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ISSN:0196-2892
1558-0644
DOI:10.1109/TGRS.2025.3540385