CLAReSNet: When Convolution Meets Latent Attention for Hyperspectral Image Classification

2025-11-15

AI Summary

Hyperspectral image classification faces challenges including high-dimensional spectral features, complex spectral-spatial correlations, and small-sample class imbalance. To address these, we propose a hybrid convolutional–latent-attention network. Our method introduces an adaptive latent bottleneck mechanism and multi-scale spectral latent attention, reducing computational complexity to $O(T log T cdot D)$. Additionally, we design a spectral-domain encoding layer that integrates multi-scale convolutional residual blocks, bidirectional RNNs, and an enhanced convolutional attention module to enable hierarchical cross-modal feature fusion. Evaluated on the Indian Pines and Salinas datasets, our model achieves overall accuracies of 99.71% and 99.96%, respectively—substantially outperforming state-of-the-art methods including HybridSN, SSRN, and SpectralFormer. These results demonstrate the proposed model’s superior accuracy, computational efficiency, and robustness under limited training samples and imbalanced class distributions.

Technology Category

• Computer Vision: Multi-modal Vision
• Machine Learning: Multi-class/Multi-label Learning & Extreme Classification
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding

Application Category

• Search and Retrieval-Augmented AI: Vertical and domain-specific search
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for heterogeneous, signed, attributed, multi-relational, temporal, higher-order, and annotated Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

Hyperspectral image (HSI) classification faces critical challenges, including high spectral dimensionality, complex spectral-spatial correlations, and limited training samples with severe class imbalance. While CNNs excel at local feature extraction and transformers capture long-range dependencies, their isolated application yields suboptimal results due to quadratic complexity and insufficient inductive biases. We propose CLAReSNet (Convolutional Latent Attention Residual Spectral Network), a hybrid architecture that integrates multi-scale convolutional extraction with transformer-style attention via an adaptive latent bottleneck. The model employs a multi-scale convolutional stem with deep residual blocks and an enhanced Convolutional Block Attention Module for hierarchical spatial features, followed by spectral encoder layers combining bidirectional RNNs (LSTM/GRU) with Multi-Scale Spectral Latent Attention (MSLA). MSLA reduces complexity from $mathcal{O}(T^2D)$ to $mathcal{O}(Tlog(T)D)$ by adaptive latent token allocation (8-64 tokens) that scales logarithmically with the sequence length. Hierarchical cross-attention fusion dynamically aggregates multi-level representations for robust classification. Experiments conducted on the Indian Pines and Salinas datasets show state-of-the-art performance, achieving overall accuracies of 99.71% and 99.96%, significantly surpassing HybridSN, SSRN, and SpectralFormer. The learned embeddings exhibit superior inter-class separability and compact intra-class clustering, validating CLAReSNet's effectiveness under limited samples and severe class imbalance.

Problem

• Addresses hyperspectral image classification challenges with high dimensionality
• Solves complex spectral-spatial correlations and limited training samples
• Overcomes class imbalance and quadratic complexity in existing methods

Innovation

• Hybrid CNN-transformer architecture with adaptive latent bottleneck
• Multi-scale spectral latent attention reduces quadratic complexity
• Hierarchical cross-attention fusion integrates multi-level representations