Enhanced Multiscale Feature Fusion Network for HSI Classification

Deep learning-based hyperspectral image (HSI) classification methods have recently attracted significant attention. However, features captured by convolutional neural network (CNN) are always partial due to the restrictions of the respective fields and the loss of multiscale information, which lead to features being discontinuous when extracted. In a departure from existing approaches, in this article, we propose a novel Enhanced Multiscale Feature Fusion Network (EMFFN). As a deeper and wider network, EMFFN can extract sufficiently multiscale features from the parallel multipath of three stages for HSI classification purposes. There are two subnetworks for multiscale spectral and spatial information in EMFFN, respectively. First, we propose a spectral Cascaded Dilated Convolutional Network (CDCN) designed to obtain a larger respective field for long-ranged information and extract multiscale features. Subsequently, a Parallel Multipath Network (PMN) is proposed to capture large-scale, middle-scale, and small-scale spatial features in parallel during all three stages. In the next step, hierarchical features are fused successively, and shallower feature maps can achieve better learning performance when guided by deeper semantic information. As PMN deepens in different stages, more multiscale information flows into the network, enabling finer classification results. To incorporate abundant spectral and spatial features, moreover, we combine features collected from two subnetworks into EMFFN using the designed consolidated loss function. As a result, the network facilitates the learning of not only localization-preserved features, but also high-level semantic features. In our experiments, three benchmark HSIs are utilized to evaluate the performance of the proposed method. Our results demonstrate that the proposed EMFFN can outperform state-of-the-art methods.