DSSNet: A Simple Dilated Semantic Segmentation Network for Hyperspectral Imagery Classification

Deep learning-based methods have presented a promising performance in the task of hyperspectral imagery classification (HSIC). However, recent methods usually are considered HSIC as a patchwise image classification problem and addressed it by giving a single label to the patch surrounding a pixel. In this letter, we propose a new semantic segmentation network that can directly label each pixel in an end-to-end manner. Compared with patchwise models, our method can significantly improve training effectiveness and reduce some manual parameters. Another challenge in HSIC is that the spatial resolution of hyperspectral imagery is relatively low; in that case, the pooling operation may result in resolution and coverage loss. To address this issue, we introduce dilated convolution to our model and construct a dilated semantic segmentation network (DSSNet). Different from some existing works, DSSNet is specially designed for HSIC without complicated architecture, and no pretrained models are required. The joint spatial-spectral information can be extracted via an end-to-end manner and, thus, avoid various preprocessing or postprocessing operations. Experiments on two public data sets have demonstrated the effectiveness of our improvements compared with some of the latest deep learning-based HSIC models.