A Spatial-Spectral Prototypical Network for Hyperspectral Remote Sensing Image

Hyperspectral remote sensing image (HRSI) can provide additional spectral information of objects and have been widely used in many fields. However, due to the complex environment of the HRSI gathering area, collecting the labeled samples of HRSI is time-consuming and labor-intensive. The scarcity of labeled samples is one of the major difficulties for HRSI analysis and processing. In this letter, a spatial-spectral prototypical network (SSPN) for HRSI is proposed for solving the problem of lack of labeled samples. The contribution of this letter is threefold. First, we design a novel local pattern coding algorithm to combine the spatial and spectral information of HRSI pixels based on spatial neighborhood correlation. Then, a spatial-spectral feature extraction algorithm based on 1-D convolutional neural network (1-D-CNN) is suggested to learn the spatial-spectral metric space where HRSI pixels can be correctly classified with only a few labeled samples. Finally, a novel prototype representation for HRSI in spatial-spectral metric space is proposed to better classify the mixed pixels existing in HRSI. The experimental results on three popular HRSI data sets demonstrate that the proposed SSPN is significantly better than the traditional algorithms.