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Supervised Nonlinear Hyperspectral Unmixing with Automatic Shadow Compensation using Multi-swarm Particle Swarm Optimization

The presence of shadows has always been a troublesome problem in image processing and can also affect spectral unmixing with hyperspectral remote sensing images. Traditional unmixing algorithms regard shadows as a special type of ground cover, so they can only estimate real materials’ sunlit abundances, and materials with low reflectance may be wrongly recognized as shadows. Without regarding shadows as ground cover, we propose a supervised nonlinear unmixing method to accurately estimate real materials’ total abundances inside and outside shadow areas. First, sunlit and shadowed constituents in every pixel of an image are modeled explicitly and integrated by a tractable bilinear mixing mechanism. Second, based on the constructed model, the strong sparsity of shadow spatial distribution and the spatial correlation among neighboring material abundances are exploited to produce a constrained optimization problem for nonlinear unmixing. Third, an existing unmixing framework based on particle swarm optimization is extended to calculate unknown variables of the optimization problem. Three alternatingly updated swarms using improved dimensional division strategies are designed to accordingly address the unmixing subproblems with respect to variables to be estimated during the solution search. This process has the potential to be generalized to solve complex nonlinear unmixing optimization problems. Finally, model-based simulated data, virtual citrus orchard data, and real hyperspectral remote sensing images are used in experiments to evaluate the proposed method and compare it with traditional and state-of-the-art nonlinear unmixing algorithms. Experimental results verify that the proposed method can achieve acceptable unmixing performance when managing nonlinear mixing effects and shadows.