SSC-SFN: spectral-spatial non-local segment federated network for hyperspectral image classification with limited labeled samples

Abstract

ABSTRACT Hyperspectral image (HSI) classification methods based on deep learning (DL) have performed well in numerous investigations. Although many modified superpixel-wise neural networks are utilized to enhance spatial information, their ability to mine spectral information in graph structures is insufficient. Moreover, single classifier approaches are unable to extract adequate spatial and spectral information simultaneously. For the classification of large-scale research areas, many works have relied on the use of a large number of labeled samples, leading to low efficiency and weak generalization. To address these issues, an effective spectral-spatial HSI classification approach based on spectral-spatial non-local segment federated network (SSC-SFN) was developed in this study. In this framework, deconvolution is employed to recover the data size, while the lost spatial information is replaced by up-pooling. The spectral dimensional features are updated through the generation of non-Euclidean graph structures and the non-local segment smoothing technique. The convolutional neural network and graph convolutional network techniques are coupled to exploit the available spectral and spatial structure information fully. Extensive experimental results obtained using four public benchmark datasets show that the classification accuracy of SSC-SFN can exceed 90% for large-scale HSIs with limited samples.