Multi-view fusion optimization method via low-rank tensor decomposition for remote sensing image classification

Extracting effective scene features is important for remote sensing image classification. Generally, the multi-view features contain information of consistency and complementarity, and efficient integration of them is helpful to enhance the performance of remote sensing image classification. Although some recent methods are able to achieve promising results, they lack analysis of the inherent relevance of multiple-view features. Thus, we present a multi-view fusion optimization method via low-rank tensor decomposition. First, the Laplacian matrix is constructed by utilizing K-nearest neighbors to generate a set of low-dimensional eigenvalues. Second, a third-order tensor is built by combining the multiple-view Laplacian features, which are factorized into many components with rank 1 using the canonical polyadic decomposition. Finally, the alternating optimization model is reconstructed by utilizing the relationship among fibers and slices of a tensor to generate optimal low-dimensional embedding features. Experiments of classification on three remote sensing image data sets AID, WHU, and UCMerced are constructed. The results of the experiments show that the new proposed method achieves better performance than others.