Feature Extraction via Joint Adaptive Structure Density for Hyperspectral Imagery Classification

Abstract

Feature extraction is known to be an effective way in both reducing computational complexity and increasing accuracy in hyperspectral imagery (HSI) classification. In this article, we propose a novel HSI feature extraction based on joint adaptive structure density (JASD), which can make full use of the texture feature of feature level and effectively utilize the spatial structure feature of pixel level. Specifically, the proposed JASD method considers two weak assumptions when exploiting the structure information contained in the HSI: 1) close pixels in spectral space have the same label and 2) all the pixels in a subregion belong to the same class. The JASD method consists of the following steps. First, a principal component analysis method is applied to the original high-dimensional HSI to obtain low-dimensional features in order to promote the separability of pixels between different classes and improve the execution efficiency of the proposed method. Then, the local density peak-assisted $k$ nearest neighbor idea is introduced into oversegmentation-based regions to define the structural spatial information of the HSI and overcome the aforementioned weak assumptions. Meanwhile, recursive filtering in the transform domain is applied to the above low-dimensional HSI to preserve global edge details. Next, the global edge details and the local spatial structure feature are fused based on the stacking method. Finally, the composite hyperspectral feature is fed into the supervised classifier to obtain the classification results. Experimental results on three widely used real HSI data sets demonstrated that the proposed JASD method is superior to several comparative classification methods in terms of classification accuracy and computational burden.