Deep learning has gained popularity in recent times in the field of feature-extraction, object-identification, object-tracking, change-detection, image-classification, spatio-temporal-data analysis, and hyperspectral imaging. Most of the supervised tasks using deep learning require a large number of labeled samples, barring which the model tends to overfit and do not generalize well to the test data. Semi-supervised learning is very beneficial for hyperspectral images which contain abundant unlabeled data samples in comparison to labeled data. Furthermore, it is known that for datasets in which samples are related to each other in all three dimensions such as videos, three-dimensional biological images and hyperspectral images, the use of spatial-spectral/spatial–temporal based deep learning strategies, which can exploit the relationship between pixels in all three-dimensions, has also seen a rise in the past few years. Moreover, to date, deep feature extraction and classification has been done using euclidean distance based metrics. Foray into the field of angular feature extraction and classification, which is known to work better when samples are impacted by resolution or illumination differences, has not yet been made. We propose a novel spatial-spectral semisupervised deep learning approach based on angular distances by projecting the deep features onto the surface of an $l_2$-normalized unit hypersphere.

深度学习最近在特征提取，对象识别，对象跟踪，变化检测，图像分类，时空数据分析和高光谱成像等领域获得了普及。使用深度学习的大多数监督任务都需要大量带标签的样本，除非模型倾向于过度拟合并且不能很好地推广到测试数据。半监督学习对于与标记数据相比包含大量未标记数据样本的高光谱图像非常有益。此外，众所周知，对于其中样本在所有三个维度上都彼此相关的数据集（例如视频，三维生物图像和高光谱图像），使用基于空间光谱/时空的深度学习策略，过去几年中，可以利用所有三个维度中的像素之间关系的图像也出现了增长。此外，迄今为止，已经使用基于欧几里德距离的量度完成了深度特征的提取和分类。尚未进入角度特征提取和分类领域，众所周知，当样本受分辨率或光照差异影响时，这种方法可以更好地工作。通过将深度特征投影到物体表面，我们提出了一种基于角距离的新型空间光谱半监督深度学习方法。当样品受到分辨率或光照差异的影响时，已知这种方法效果更好。通过将深度特征投影到物体表面，我们提出了一种基于角距离的新型空间光谱半监督深度学习方法。当样品受到分辨率或光照差异的影响时，已知这种方法效果更好。通过将深度特征投影到物体表面，我们提出了一种基于角距离的新型空间光谱半监督深度学习方法。${升}_2$-归一化单位超球。