IRSN: Involutional Residual Spectral Network for Hyperspectral Image Classification

Convolutional methods are commonly used for hyperspectral imaging (HSI) classification. However, HSI datasets are large due to numerous narrow-band spectra, leading to high computational costs and optimization challenges in convolution-based deep learning models. To address this, we propose the involutional residual spectral network (IRSN), using involution kernels tailored to the data for meaningful feature extraction. IRSN achieves this with fewer parameters than convolutions. By leveraging involution layers based on spectral signatures, IRSN captures spectral-spatial information. Furthermore, residual blocks within the network facilitate information preservation and overcome gradient-related challenges. Experimental studies conducted using four publicly available datasets demonstrate that the proposed IRSN model outperforms certain state-of-the-art convolutional-based networks in terms of effectiveness and efficiency.