Fully Convolutional Network-based Nonlocal Dependent Learning for Hyperspectral Image Classification

Deep convolutional neural networks play an important role in hyperspectral Images (HSIs) classification tasks through hierarchical learning. Recent work based on deep learning has made great progress in exploring contextual features, with more of these approaches focusing on nonlocal contextual information. Nevertheless, the contextual information obtained by these methods still have room for improvement as they only consider semantic level. Moreover, they ignore the importance of contextual features in the spectral domain, an important component of contextual features, especially in HSI. This paper proposes a novel HSI classification method, the nonlocal dependent learning fully convolutional network (FCN). The network fully focuses on HSI spatial and spectral non-local contextual features by combining a context-aware module and global convolutional long short-term memory neural network (ConvLSTM) learning from shallow (spatial level) to deep (semantic level) layers. Specifically, the proposed context-aware module perceives local joint features through local and surrounding learning, and refines features under a global context through global learning. To further enhance the long-range dependencies of spectral and spatial dimensions at different phases, global ConvLSTM learning is proposed to obtain multiscale fused features from spatial to semantic. Experiments on datasets with different scenes reveal that the proposed method obtains better classification performance than state-of-the-art methods.