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 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.