A Band-Enhanced Tensor-Based Multiband SAR Feature Extraction Method for Land Cover Classification

Current multiband synthetic aperture radar (SAR) feature extraction methods often fail to adequately exploit intraband details and interband spectral relationships, limiting their classification performance. To address this, in this article, a novel band-enhanced tensor-based multiband SAR feature extraction method is proposed for land cover classification. It first constructs a fourth-order feature tensor by aggregating gray-level co-occurrence matrix features from spatial neighborhoods, effectively capturing multiband spatial-spectral characteristics. Subsequently, a band-enhanced tensor discriminant locality preserving projection (BTDLPP) method is proposed to project the high-dimensional feature tensor to a discriminative compact subspace. BTDLPP innovatively integrates manifold structure preservation with linear discriminant analysis by formulating an objective function that simultaneously maximizes the between-class scatter and minimizes the within-class scatter and the manifold structural distortion. For the band mode, diagonal dominance and correlation guided regularization terms are added to the optimization objective function to enhance band information through weighted fusion. This approach enables comprehensive extraction of both linear and nonlinear discriminative features while jointly exploring intra and interband features. Experiments on multiband SAR datasets in Wanning and Sheyang regions demonstrate that, compared to the conventional tensor subspace projection methods, the feature space extracted by the proposed method simultaneously exhibits better linear separability and the preservation of manifold structures, leading to higher land cover classification accuracy.