The distribution of ground objects in hyperspectral images predominantly reveals spatial indications of both order and disorder, encapsulating a wealth of texture information. This texture information encompasses not only local structural details but also global statistical priors of an image. Nevertheless, convolutional-neural-network-based methods for hyperspectral image classification (HIC) primarily use skip connections to incorporate shallow features abundant in texture information into deeper layers. They face challenges in effectively capturing the statistical properties of texture information, and the traditional method of modeling statistical attributes struggles to seamlessly integrate into parameter learning of convolutional neural networks (CNNs). To do so, this work proposes a statistical texture awareness network (STANet) for HIC. It achieves the exploration of learnable texture features. Through multilevel quantization and quantization encoding, a statistical texture learning module (STLM) is constructed to represent texture information from low-level features in a statistical manner. As a result, it augments the discriminatory power of such features. In addition, a complete feature fusion module (CFFM) is designed to intelligently combine multiscale contextual semantic and statistical texture features, thereby bolstering the discrimination of spectral-spatial ones. Experimental results reported for three public datasets demonstrate the superior performance of the proposed network over other peers.

中文翻译：

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用于高光谱图像分类的统计纹理感知网络


高光谱图像中地面物体的分布主要揭示了有序和无序的空间指示，封装了丰富的纹理信息。该纹理信息不仅包含局部结构细节，还包含图像的全局统计先验。然而，基于卷积神经网络的高光谱图像分类（HIC）方法主要使用跳跃连​​接将纹理信息丰富的浅层特征合并到更深的层中。他们在有效捕获纹理信息的统计属性方面面临挑战，并且传统的统计属性建模方法难以无缝集成到卷积神经网络（CNN）的参数学习中。为此，这项工作提出了一种用于 HIC 的统计纹理感知网络 (STANet)。它实现了对可学习纹理特征的探索。通过多级量化和量化编码，构建统计纹理学习模块（STLM），以统计方式表示低级特征的纹理信息。结果，它增强了这些特征的歧视力。此外，还设计了完整的特征融合模块（CFFM），可以智能地结合多尺度上下文语义和统计纹理特征，从而增强光谱空间特征的辨别力。三个公共数据集报告的实验结果表明，所提出的网络比其他同行具有优越的性能。