TPPI-Net: Towards Efficient and Practical Hyperspectral Image Classification
This work addresses a practical bottleneck for researchers and practitioners in remote sensing by making hyperspectral image classification more efficient, though it is incremental as it builds on existing networks.
The paper tackles the inefficiency of pixel-wise processing in hyperspectral image classification by proposing TPPI-Net, a new network design mechanism that reduces computational complexity while maintaining high classification accuracy equivalent to state-of-the-art methods.
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.