With their combined spectral depth and geometric resolution, hyperspectral remote sensing images embed a wealth of complex, non-linear information that challenges traditional computer vision techniques. Yet, deep learning methods known for their representation learning capabilities prove more suitable for handling such complexities. Unlike applications that focus on single-label, pixel-level classification methods for hyperspectral remote sensing images, we propose a multi-label, patch-level classification method based on a two-component deep-learning network. We use patches of reduced spatial dimension and a complete spectral depth extracted from the remote sensing images. Additionally, we investigate three training schemes for our network: Iterative, Joint, and Cascade. Experiments suggest that the Joint scheme is the best-performing scheme; however, its application requires an expensive search for the best weight combination of the loss constituents. The Iterative scheme enables the sharing of features between the two parts of the network at the early stages of training. It performs better on complex data with multi-labels. Further experiments showed that methods designed with different architectures performed well when trained on patches extracted and labeled according to our sampling method.

翻译：高光谱遥感图像凭借其光谱深度与几何分辨率的结合，嵌入了丰富的复杂非线性信息，这对传统计算机视觉技术构成了挑战。然而，以表征学习能力著称的深度学习方法更适合处理此类复杂性。与面向高光谱遥感图像的单标签像素级分类方法不同，我们提出了一种基于双组件深度学习网络的多标签图像块级分类方法。我们采用降低空间维度的图像块以及从遥感图像中提取的完整光谱深度。此外，我们研究了该网络的三种训练方案：迭代式、联合式和级联式。实验表明，联合式方案性能最优，但其应用需要对损失函数各组成部分的权重组合进行昂贵的搜索。迭代式方案能够在网络训练早期实现两个组件间的特征共享，在处理含有多标签的复杂数据时表现更优。进一步实验显示，根据我们的采样方法提取并标注的图像块，在其上训练的不同架构设计方法均表现良好。