Leveraging the rich spectral and spatial information, hyperspectral image classification (HSIC) plays a vital role in remote sensing, which is significant for land-cover mapping and environmental monitoring. However, hyperspectral images exhibit high dimensionality, significant spectral redundancy, and a limited number of annotated samples, making classification challenging. However, the original data may be complicated by more specific spectral-spatial interdependencies, which the so-called feature extraction in either standard film learning or early CNN-based models doesn’t capture. Simultaneously, recent approaches that embed attention or a transformer into the architecture suffer from high computational cost, overfitting, and scalability issues. These limitations highlight the need for a new, flexible, and computationally efficient deep-learning framework for various HSIC scenarios. We propose HSICNet, a unique dual-branch network architecture for deep learning that separately captures spectral and spatial features, followed by an attention-based feature fusion strategy to facilitate the interplay between low-, mid, and high-level feature representations. To alleviate the curse of dimensionality and redundancy, we also include a PCA-based dimensionality reduction module. The work involves optimising the proposed model for accuracy, computational efficiency, and generalisation across all classes. HSICNet is extensively evaluated by conducting experiments on three benchmark hyperspectral datasets, including Indian Pines, Pavia University, and Salinas, and its performance is superior to the current state-of-the-art algorithms. With overall accuracy up to 99.35%, it achieves statistically significant improvements in the Kappa coefficient, F1-score, and average accuracy by a large margin. Ablations verify our choice of a dual-branch design and attention fusion to improve classification scores. With a strong architecture and the advantage of HSICNet’s lightweight nature, it has potential for use in real-time, scalability-sensitive remote sensing applications such as precision agriculture, smart cities, and environmental monitoring. Its robust generalizability and interpretability also permit deployment in heterogeneous and adaptive operational settings.
Purnachand et al. (Thu,) studied this question.