Quantum Enchanced Multi-Scale CNN with Bi-directional Mamba for Crop Field Analysis
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 resolu...
| Authors | Mohammad Salman Khan, Ehsan Atoofian, Saad B. Ahmed |
|---|---|
| Primary category | cs.CV |
| Published | 15 June 2026 |
| Last updated at source | 15 June 2026 |
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