Spectral–Spatial Classification of Few Shot Hyperspectral Image With Deep 3-D Convolutional Random Fourier Features Network

Remote sensing hyperspectral images are very useful for land cover classification because of their rich spatial and spectral information. However, hyperspectral image acquisition and pixel labeling are laborious and time-consuming, so few-shot learning methods are considered to solve this problem. Deep learning has gradually been used for few-shot hyperspectral classification, but there are some problems. The feature extraction network based on deep learning requires too many parameters to be trained, resulting in a huge network model, which is not conducive to deployment on remote sensing data acquisition equipment. Moreover, due to the lack of label samples, the algorithm based on deep learning is more prone to overfitting. To solve the above problems, considering the advanced characteristics of the kernel method in dealing with nonlinear, small sample, and high-dimensional data, we propose a small scale high precision network called 3-D convolution random Fourier features (3-DCRFF) based on the random Fourier feature (RFF) kernel approximation, which is the 3-DCRFF network. First, we combine 3-D convolution with RFF as the basic structure of the network to extract the spatial and spectral features of HSI cubes. Second, we use a classifier based on attention mechanism to classify feature vectors to obtain recognition probability. Finally, the network parameters are solved from the perspective of Bayesian optimization, and the synthetic gradient optimization method is designed and implemented to realize the fast learning of the network. A large number of experiments HSI classification experiments were performed on University of Pavia (UP), Pavia Center (PC), Indian Pines (IP), and Salinas standard remote sensing datasets, the results show that our algorithm outperforms most state-of-the-art algorithms on few-shot classification.