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Bilateral filtering (BF), which is an edge-preserving filtering (EPF) method, has been widely recognized as a simple and efficient approach for hyperspectral image (HSI) feature extraction. However, due to the limitation of spatial resolution and the influence of the complexity of land feature distribution in HSIs, updating the target pixel through weighted averaging of neighbouring pixels is prone to generating mixed pixels, i.e., the updated target pixel is mixed with the feature of other land objects in addition to that of the target object, decreasing the quality of the image feature extraction. To address this problem, in this study, we propose a superpixel-based BF algorithm, SuperBF. This algorithm divides a HSI into many homogeneous regions via superpixel segmentation and then separately filters each homogenous region via BF; this approach ensures that the pixel structure in the template after BF is similar to that in the filtering process, reduces the probability of generating mixed pixels, and thus improves the quality of the image feature extraction. To verify the effectiveness of this proposed method, a support vector machine (SVM) classifier is used to classify the extracted SuperBF features. Experiments on three commonly employed HSI datasets demonstrated that SuperBF is significantly superior to the traditional BF-based hyperspectral feature extraction method and some new feature extraction methods.

SuperBF: Superpixel-Based Bilateral Filtering Algorithm and Its Application in Feature Extraction of Hyperspectral Images