Hyperspectral Image(HSI) classification is the most vibrant field of research in the hyperspectral community, which aims to assign each pixel in the image to one certain category based on its spectral-spatial characteristics. Recently, some spectral-spatial-feature based DCNNs have been proposed and demonstrated remarkable classification performance. When facing a real HSI, however, these Networks have to deal with the pixels in the image one by one. The pixel-wise processing strategy is inefficient since there are numerous repeated calculations between adjacent pixels. In this paper, firstly, a brand new Network design mechanism TPPI (training based on pixel and prediction based on image) is proposed for HSI classification, which makes it possible to provide efficient and practical HSI classification with the restrictive conditions attached to the hyperspectral dataset. And then, according to the TPPI mechanism, TPPI-Net is derived based on the state of the art networks for HSI classification. Experimental results show that the proposed TPPI-Net can not only obtain high classification accuracy equivalent to the state of the art networks for HSI classification, but also greatly reduce the computational complexity of hyperspectral image prediction.

翻译：超光谱光谱图像(HSI)分类是超光谱界中最活跃的研究领域,其目的在于根据光谱空间特性将图像中的每像素指派给某一类,根据光谱空间特性,将图像中的每像素划归为某一类。最近,一些以光谱空间-空间-地貌DCNNS为基础的DCNNS已经提出并展示了显著的分类性能。然而,在面对真正的HSI时,这些网络必须逐个处理图像中的像素。像素处理策略效率低下,因为相邻的像素之间反复计算很多。在本文中,首先为HSI分类提议了一个品牌的新网络设计机制TPPI(基于像素和图像预测的培训)TPPI(TPI-Net)(TPPI-Net)(TPPI-Net)(TPPI-Net)(TPI-Net)不仅获得相当于HSI分类的艺术网络状态的高度分类准确性,而且还大大降低了超光谱性图像的测算。