Hyperspectral Image Classification using Patch-based Attention and Restricted Isometric Regularization
Abstract
Hyperspectral images provide rich spatial-spectral information, enabling detailed analysis of remotely sensed images. This article presents a novel ultra-lightweight deep neural net model for hyperspectral image (HSI) classification by introducing a patch-based attention mechanism along with a Restricted Isometric Regularization (RIR). The patch attention mechanism is embedded onto a 3D convolutional neural network (CNN) and operates on fixed spatial neighborhoods to dynamically weigh the local spatial-spectral features, thereby enhancing the model’s ability to capture intricate patterns across spectral bands. Due to the presence of a large number of bands in HSI images, preservation of intrinsic structure is a challenge. The present article formulates a novel regularizer, RIR, which facilitates stable feature projection in high-dimensional spaces and effectively addresses the curse of dimensionality while preserving the overall structure of the image. The proposed method achieves 99.79% accuracy on Pavia University dataset, setting a new benchmark in HSI classification. It also performs excellently on other benchmark datasets, e.g. 98.63% OA and 98.44% Kappa on Indian Pines, 99.94% OA and 99.93% Kappa on Salinas, and 99.54% OA and 99.50% Kappa on Botswana. In addition, ablation studies highlight the importance of each mechanism. With only 33,337 parameters, our framework significantly outperforms state-of-the-art methods by a considerable margin.
Authors: Abhiroop Chatterjee, Susmita Ghosh, Ashish Kumar Ghosh
Published in: IEEE International Geoscience and Remote Sensing Symposium (IGARSS) (2025)