Multiobjective Memetic Spatiotemporal Subpixel Mapping for Remote Sensing Imagery

Subpixel mapping (SPM) technology is an effective way to account for the distribution of the component objects within the mixed pixels and can alleviate the mixed pixel problem to some degree. However, the traditional SPM methods rely on only a single coarse-resolution image, and the limited information source can lead to great uncertainty. The rapid development of Earth observation systems has resulted in many fine spatial resolution remote sensing images now being accessible. Spatiotemporal SPM approaches utilize the finer spatial distribution of a historical thematic map to provide temporal prior information for the mapping process. However, spatiotemporal SPM is essentially a constrained optimization problem that aims to predict the optimal class distribution map subject to the abundance, spatial, and temporal constraints. It is a challenging task to properly model and optimize the multiple constraints. In this article, a multiobjective memetic spatiotemporal SPM (MOMSPM) framework is proposed. This model transforms the data fidelity term, spatial prior term, and temporal prior term into a multiobjective optimization problem (MOP) to discard the sensitive regularization parameters. The multiobjective model realizes the fusion of abundance, spatial, and temporal information. To optimize the three objective functions simultaneously, MOMSPM provides a multiobjective memetic algorithm framework in which the global multiobjective search method [multiobjective evolutionary algorithm based on decomposition (MOEA/D)] combines two commonly employed single-objective local search operators (geospatial distribution preference (GSDP) local search and maximum a posteriori (MAP)-based local search). The GSDP and MAP operators are employed to refine the solution and achieve an improved outcome. The hybridized method provides powerful search ability and achieves a good balance between the three objective functions. Experiments on synthetic and real datasets prove that the proposed method is superior to the state-of-the-art spatiotemporal SPM methods.