Deep learning (DL) has been widely applied to hyperspectral image (HSI) classification, owing to its promising feature learning and representation capabilities. However, limited by the spatial resolution of sensors, existing DL-based classification approaches mainly focus on pixel-level spectral and spatial information extraction through complex network architecture design while ignoring the existence of mixed pixels in actual scenarios. To tackle this difficulty, we propose a novel dual-branch subpixel-guided network for HSI classification, called DSNet, which automatically integrates subpixel information and convolutional class features by introducing a deep autoencoder unmixing architecture to enhance classification performance. DSNet is capable of fully considering physically nonlinear properties within subpixels and adaptively generating diagnostic abundances in an unsupervised manner to achieve more reliable decision boundaries for class label distributions. The subpixel fusion module is designed to ensure high-quality information fusion across pixel and subpixel features, further promoting stable joint classification. Experimental results on three benchmark datasets demonstrate the effectiveness and superiority of DSNet compared with state-of-the-art DL-based HSI classification approaches. The codes will be available at https://github.com/hanzhu97702/DSNet , contributing to the remote sensing community.

中文翻译：

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用于高光谱图像分类的双分支亚像素引导网络


深度学习（DL）因其有前景的特征学习和表示能力而被广泛应用于高光谱图像（HSI）分类。然而，受传感器空间分辨率的限制，现有的基于深度学习的分类方法主要侧重于通过复杂的网络架构设计来提取像素级的光谱和空间信息，而忽略了实际场景中混合像素的存在。为了解决这个难题，我们提出了一种用于 HSI 分类的新型双分支子像素引导网络，称为 DSNet，它通过引入深度自动编码器解混合架构来自动集成子像素信息和卷积类特征，以增强分类性能。 DSNet 能够充分考虑子像素内的物理非线性特性，并以无监督的方式自适应生成诊断丰度，从而为类标签分布实现更可靠的决策边界。亚像素融合模块旨在确保跨像素和亚像素特征的高质量信息融合，进一步促进稳定的联合分类。三个基准数据集的实验结果证明了 DSNet 与最先进的基于 DL 的 HSI 分类方法相比的有效性和优越性。这些代码将在 https://github.com/hanzhu97702/DSNet 上提供，为遥感社区做出贡献。