Contour Structural Profiles: An Edge-Aware Feature Extractor for Hyperspectral Image Classification

Feature extraction provides an effective tool to classify hyperspectral images (HSIs). However, most hyperspectral feature extraction methods tend to yield an over-smoothed phenomenon, which leads to inconsistency between the homogeneous regions and the ground objects in the actual scene. To alleviate this problem, an edge-aware feature extractor called contour structural profiles (CSPs) is proposed to extract the discriminative features for hyperspectral images classification (HSIC). The proposed classification method comprises three components. First, the spectral dimension of the HSI is reduced with an averaging-based method. Then, an edge-aware total variation (TV) model is constructed to extract the contour structural profile, in which a learned contour probability map is served as one of the major cues in the feature extraction process. Next, multiscale structural profiles (MSSPs) are constructed using the edge-aware TV model with different parameters so as to fully characterize ground objects with different scales. Finally, the MSSPs are fused with a kernel principal component analysis (KPCA) followed by a spectral classifier to obtain the final classification map. Experimental results on several publicly available hyperspectral datasets illustrate that the proposed method obtains superior classification performance over several state-of-the-art classification approaches, especially when the number of training samples is insufficient.

Automated summary

This paper proposes an edge-aware feature extractor to address the problem of over-smoothing in hyperspectral image classification. The proposed method extracts discriminative features through contour structural profiles and multiscale structural profiles, and obtains the final classification map through fusion and a classifier.