🤖 AI Summary
This study addresses the challenges of hyperspectral image-based crop classification, which include high-dimensional spectral data, complex spatial structures, class imbalance, and limited labeled samples. To tackle these issues, the authors propose the BiSpectral Mamba framework, which uniquely integrates a bidirectional Mamba state space model with quantum-inspired learning, while also incorporating multiscale CNNs and a spectral attention mechanism to enable efficient joint spatial–spectral feature extraction. A class-weighted training strategy is further employed to enhance classification robustness under few-shot conditions. Evaluated on the UAVHSI-Crop dataset, the method achieves an overall accuracy of 84.83%, demonstrating its strong potential for agricultural remote sensing applications.
📝 Abstract
Hyperspectral image (HSI) crop analysis is essential for precision agriculture because it captures rich spectral and spatial information for accurate crop monitoring and assessment. However, HSI classification remains challenging due to high spectral dimensionality, spatial complexity, class imbalance, and limited labeled samples. To address these challenges, this paper proposes a BiSpectral Mamba-based framework that combines multi-scale convolutional feature extraction, spectral attention, bidirectional state-space modeling, and quantum-inspired learning. A multi-scale CNN backbone first extracts hierarchical spatial-spectral representations through feature fusion across multiple resolutions. A spectral attention mechanism then emphasizes informative bands while suppressing redundant and noisy channels. The refined features are processed by a BiSpectral Mamba module that captures long-range dependencies in both forward and backward directions by modeling hyperspectral feature maps as sequential tokens. In addition, class-weighted optimization and feature fusion strategies are incorporated to improve training stability and mitigate class imbalance. Experimental evaluation on the UAVHSI-Crop dataset demonstrates the effectiveness of the proposed framework, achieving an overall accuracy of 84.83%. The results show that integrating convolutional, attention-based, and state-space modeling components enables robust spatial-spectral feature learning for crop classification. The proposed framework also shows potential for broader agricultural and remote sensing applications, including crop disease detection, yield prediction, and soil moisture estimation, while highlighting the effectiveness of structured state-space and quantum-inspired architectures for hyperspectral image analysis.