One Unsupervised Feature Selection Method for the Classical Linear Classifier in Land Coverage Classification With PolSAR Imagery

Land coverage mapping and classification is one of the critical information-based tools for sustainable agricultural development, enabling relevant departments to carry out agricultural resource adjustments, yield predictions, and other tasks in advance. As a vital means of acquiring land cover and usage information, SAR sensors have become an important research direction due to their all-weather and all-day working capabilities. Nevertheless, traditional classification methods in PolSAR image classification often input a combination of various scattering features, i.e., high-dimensional feature combination, into classifiers, leading to mutual interference among different features and consequently degrading classification performance, especially for linear classifiers such as NRS and SVM. To mitigate this interference, this paper proposed an unsupervised feature selection based on spectral clustering (FSSC) that constructs a targeted approach by leveraging the linear expression capabilities of high-dimensional features. In this method, the linear relationships between different features are first analyzed, and the linear similarity between features can be quantitatively expressed using Pearson correlation coefficients, forming a feature similarity matrix. Subsequently, the similarity matrix undergoes unsupervised similarity partitioning through spectral clustering, dividing the features into distinct combinations. Features within clustering subsets can be considered as combinations with high linear similarity. Therefore, KL divergence is applied to select the most representative features within each cluster, and the resulting representative feature combinations from different clustering subsets are combined to form an optimal feature set, achieving the purpose of feature selection. This method maps high-dimensional feature combinations into low-dimensional ones while preserving the essential attributes of the original data, thereby retaining the valuable feature information and enhancing classification performance. Experimental outcomes conclusively show that the proposed method enhances the overall accuracy (OA) of SVM by 4.51% and the OA of NRS by 2.34% in the Flevoland Dataset, underscoring its efficacy in PolSAR image classification, especially for linear classifiers.