An efficient dual feature fusion model of feature extraction for hyperspectral images

Conventional feature extraction (FE) and spatial or context-preserving filters have been extensively studied when applying hyperspectral images (HSIs). However, there still exists some issues to resolve, such as the destruction of the complete structural information because of unfolded 2-D matrix before extracting, the unchanged or even increased number of resulting features from the additional context or spatial-preserving filters, and overreliance on experience to choose retained dimensionality that significantly improves the operating time. This article presents an efficient FE framework, i.e., a dual feature fusion model (DFFM), to address these issues. Specifically, a novel two-order feature fusion (FF) weighted on partial Shannon's entropy is proposed to hold low dimensional characteristics. Then, a valid three-order FF using constrained Tucker compression is performed on the resulting elements, containing the intact spatial structure and saving computing costs. It can also automatically select a suitable number of features to keep and is robust to noise and training sets. Comparative experiments on three benchmark HSIs were performed to verify the efficiency of DFFM in cases of different training sizes. All the experimental results show that this framework is robust and effective, outperforming several state-of-the-art techniques in classification precision and execution time.

Automated summary

This article presents an efficient feature extraction framework, called the dual feature fusion model (DFFM), to address issues in hyperspectral image applications. The framework utilizes a novel two-order feature fusion and a valid three-order feature fusion method to maintain spatial structure integrity and save computing costs. It also automatically selects a suitable number of features and is robust to noise and training sets. Experimental results demonstrate that the framework outperforms state-of-the-art techniques in classification precision and execution time across various training sizes.