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.

Remote sensing image scene classification refers to assigning semantic labels according to the content of the remote sensing scenes. Most machine learning-based scene classification methods assume that training and testing data share the same distributions. However, in real application scenarios, this assumption is difficult to guarantee. Domain adaptation (DA) is a promising approach to address this problem by aligning the feature distribution of training and testing data. Inspired by the idea DA, in this article, we propose a correlation subspace dynamic distribution alignment (CS-DDA) method for remote sensing image scene classification. Aiming at the characteristics of remote sensing scenes, we introduce two strategies to balance the effects of source and target domains: subspace correlation maximization (SCM) and dynamic statistical distribution alignment (DSDA). On the one hand, SCM tries to avoid mapping source domain data into irrelevant subspace to preserve the representation information of the source domain. On the other hand, DSDA is proposed to reduce the data distribution discrepancy between aligned source and target domains. Specifically, DSDA is a dynamic adjustment process where an adaptive factor is learned to balance the interclass and intraclass distribution between domains. Moreover, we integrate SCM and DSDA into a uniform optimization framework, and the optimal solution can be converted to the generalized eigendecomposition problem by derivation. The experimental results indicate that the proposed method can generate better results when compared with other feature distribution alignment methods.

Recently, for the task of hyperspectral image classification, deep-learning-based methods have revealed promising performance. However, the complex network structure and the time-consuming training process have restricted their applications. In this letter, we construct a much simpler network, i.e., the nonlinear spectral-spatial network (NSSNet), for hyperspectral image classification. NSSNet is developed from the basic structure of a principal component analysis network. Nonlinear information is included in NSSNet, to generate a more discriminative feature expression. Moreover, spectral and spatial features are combined to further improve the classification accuracy. Experimental results indicate that our method achieves better performance than state-of-the-art deep-learning-based methods.