  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.
