Hyperspectral image (HSI) classification is a crucial technique for remote sensing to build large-scale earth monitoring systems. HSI contains much more information than traditional visual images for identifying the categories of land covers. One recent feasible solution for HSI is to leverage CapsNets for capturing spectral-spatial information. However, these methods require high computational requirements due to the full connection architecture between stacked capsule layers. To solve this problem, a DWT-CapsNet is proposed to identify partial but important connections in CapsNet for a effective and efficient HSI classification. Specifically, we integrate a tailored attention mechanism into a Discrete Wavelet Transform (DWT)-based downsampling layer, alleviating the information loss problem of conventional downsampling operation in feature extractors. Moreover, we propose a novel multi-scale routing algorithm that prunes a large proportion of connections in CapsNet. A capsule pyramid fusion mechanism is designed to aggregate the spectral-spatial relationships in multiple levels of granularity, and then a self-attention mechanism is further conducted in a partially and locally connected architecture to emphasize the meaningful relationships. As shown in the experimental results, our method achieves state-of-the-art accuracy while keeping lower computational demand regarding running time, flops, and the number of parameters, rendering it an appealing choice for practical implementation in HSI classification.

翻译：高光谱图像（HSI）分类是遥感技术构建大规模地球监测系统的关键技术。相较于传统视觉图像，HSI包含更丰富的信息，可用于识别地表覆盖类别。近期一种可行的HSI处理方案是利用胶囊网络（CapsNets）捕获光谱-空间信息。然而，由于堆叠胶囊层之间采用全连接架构，这些方法需要较高的计算资源。为解决此问题，本文提出DWT-CapsNet，通过识别胶囊网络中的部分关键连接来实现高效的高光谱图像分类。具体而言，我们在基于离散小波变换（DWT）的下采样层中集成定制化注意力机制，缓解传统特征提取器中下采样操作的信息丢失问题。此外，我们提出一种新颖的多尺度路由算法，可大幅剪枝胶囊网络中的连接。通过设计胶囊金字塔融合机制聚合多粒度层次的光谱-空间关系，并在局部连接架构中进一步采用自注意力机制以强化有效关联。实验结果表明，本方法在保持较低计算需求（包括运行时间、浮点运算量和参数量）的同时，达到了最先进的分类精度，为高光谱图像分类的实际应用提供了有吸引力的解决方案。