Multisource Geospatial Data Fusion via Local Joint Sparse Representation

In this paper, we propose an adaptive locality weighted multisource joint sparse representation classification (ALWMJ-SRC) model for the classification of multisource remote sensing data. Although the notion of multitask joint sparsity has been recently developed for data fusion and has shown to be effective for various applications, in this paper, we suggest that there are important limitations stemming from the assumptions in such a framework. We propose a formulation that is inspired by this approach yet addresses some of the key shortcomings (e.g., uniform weights and unstable estimation of coefficients), resulting in a more robust formulation for data fusion. Specifically, we impose an adaptive locality weight to constrain the sparse coefficients, which not only considers the locality information between the test sample and the atoms in the dictionary but also helps ensure that the coefficients are adaptively penalized, reducing estimation bias. The adaptive locality weight is calculated for each source, which ensures that complementary information is employed from different sources for fusion. The optimization problem is solved using an alternating-direction-methods-of-multipliers formulation. In addition, the proposed algorithm is extended to the kernel space. The efficacy of the proposed algorithm is validated via experiments for two fusion scenarios-spectral-spatial classification and hyperspectral-LiDAR sensor fusion. The experimental results demonstrate that ALWMJ-SRC consistently performs better than state-of-the-art classification approaches.