Hyperspectral Image Classification Using Gated Adaptable Convolutional-Based Kolmogorov–Arnold Network

Vision transformers (ViTs) and convolutional neural networks (CNNs) have demonstrated remarkable performance in classifying complicated hyperspectral images (HSIs). However, these models require a lot of computational power and training data. Recently, the Kolmogorov-Arnold networks (KANs) have been proposed as an effective network to overcome these challenges. In addition to learning new features, KANs may optimize learned features with outstanding accuracy due to their outward similarity to multilayer perceptrons (MLPs) and internal similarity to splines. Therefore, we introduce a novel gated-adaptable convolutional-based KAN (GC-KAN) for the HSI classification (HSIC). Our method combines a gated approach that selectively focuses on significant features and adaptable convolutional modules that adjust to intricate spectral-spatial changes. Extensive studies on the Houston 2013 and Indian Pines datasets demonstrate the efficacy of GC-KAN, demonstrating its improved performance over conventional techniques. This demonstrates GC-KAN’s potential as an effective tool for more thorough spatial-spectral feature extraction and accurate interpretation for several remote sensing applications.