HSIRMamba: An effective feature learning for hyperspectral image classification using residual Mamba

Abstract

Deep learning models have recently demonstrated outstanding results in classifying hyperspectral images (HSI). The Transformer model is among the various deep learning models that have received increasing interest due to its superior ability to simulate the long-term dependence of spatial-spectral information in HSI. Due to its self-attention mechanism, the Transformer exhibits quadratic computational complexity, which makes it heavier than other models and limits its application in the processing of HSI. Fortunately, the newly developed state space model Mamba exhibits excellent computing effectiveness and achieves Transformer-like modeling capabilities. Therefore, we propose a novel enhanced Mamba-based model called HSIRMamba that integrates residual operations into the Mamba architecture by combining the power of Mamba and the residual network to extract the spectral properties of HSI more effectively. It also includes a concurrent dedicated block for spatial analysis using a convolutional neural network. HSIRMamba extracts more accurate features with low computational power, making it more powerful than transformer-based models. HSIRMamba was tested on three majorly used HSI Datasets-Indian Pines, Pavia University, and Houston 2013. The experimental results demonstrate that the proposed method achieves competitive results compared to state-of-the-art methods.