The emergence of convolutional neural networks (CNNS) has greatly promoted the development of hyperspectral image classification (HSIC). However, some serious problems are the lack of label samples in hyperspectral images (HSIs) and the spectral characteristics of different objects in HSIs are sometimes similar among classes. These problems hinder the improvement of hyperspectral image classification performance. To this end, in this paper, a positive feedback spatial spectral correlation network based on spectral inter-class slicing (PFSSC_SICS) is proposed. Firstly, a spectral inter-class slicing (SICS) strategy is designed, which can remove similar spectral signature between classes, and reduce the impact of similar spectral signature of different classes on HSI classification performance. Secondly, in order to solve the impact of the lack of labeled samples on hyperspectral image classification, a positive feedback (PF) mechanism and a spatial spectral correlation (SSC) module are introduced to extract deeper and more features. Finally, the experimental results show that the classification performance of the PFSSC_SICS is far exceeds than that of some state-of-the-art methods.

Hyperspectral image classification (HSIC) is one of the most important research topics in the field of remote sensing. However, it is difficult to label hyperspectral data, which limits the improvement of classification performance of hyperspectral images in the case of small samples. To alleviate this problem, in this paper, a dual-branch network which combines cross-channel dense connection and multi-scale dual aggregated attention (CDC_MDAA) is proposed. On the spatial branch, a cross-channel dense connections (CDC) module is designed. The CDC can effectively combine cross-channel convolution with dense connections to extract the deep spatial features of HSIs. Then, a spatial multi-scale dual aggregated attention module (SPA_MDAA) is constructed. The SPA_MDAA adopts dual autocorrelation for attention modeling to strengthen the differences between features and enhance the ability to pay attention to important features. On the spectral branch, a spectral multi-scale dual aggregated attention module (SPE_MDAA) is designed to capture important spectral features. Finally, the spatial spectral features are fused, and the classification results are obtained. The experimental results show that the classification performance of the proposed method is superior to some state-of-the-art methods in small samples and has good generalization.

No abstract