HSIMamba: Hyperpsectral Imaging Efficient Feature Learning with Bidirectional State Space for Classification

TL;DR

HSIMamba is a novel hyperspectral image classification framework that combines bidirectional CNN pathways with spatial analysis, achieving high accuracy efficiently without high computational costs.

Contribution

It introduces a bidirectional CNN-based framework with a specialized spatial block, improving spectral feature extraction and classification accuracy over existing models.

Findings

Outperforms state-of-the-art models on three benchmark datasets.

Enhances spectral feature extraction with bidirectional processing.

Reduces computational demands compared to Transformer-based methods.

Abstract

Classifying hyperspectral images is a difficult task in remote sensing, due to their complex high-dimensional data. To address this challenge, we propose HSIMamba, a novel framework that uses bidirectional reversed convolutional neural network pathways to extract spectral features more efficiently. Additionally, it incorporates a specialized block for spatial analysis. Our approach combines the operational efficiency of CNNs with the dynamic feature extraction capability of attention mechanisms found in Transformers. However, it avoids the associated high computational demands. HSIMamba is designed to process data bidirectionally, significantly enhancing the extraction of spectral features and integrating them with spatial information for comprehensive analysis. This approach improves classification accuracy beyond current benchmarks and addresses computational inefficiencies encountered with advanced models like Transformers. HSIMamba were tested against three widely recognized datasets Houston 2013, Indian Pines, and Pavia University and demonstrated exceptional performance, surpassing existing state-of-the-art models in HSI classification. This method highlights the methodological innovation of HSIMamba and its practical implications, which are particularly valuable in contexts where computational resources are limited. HSIMamba redefines the standards of efficiency and accuracy in HSI classification, thereby enhancing the capabilities of remote sensing applications. Hyperspectral imaging has become a crucial tool for environmental surveillance, agriculture, and other critical areas that require detailed analysis of the Earth surface. Please see our code in HSIMamba for more details.