Sub-pixel mapping based on artificial immune systems for remote sensing imagery

A new sub-pixel mapping strategy inspired by the clonal selection theory in artificial immune systems (AIS), namely, the clonal selection sub-pixel mapping (CSSM) framework, is proposed for the sub-pixel mapping of remote sensing imagery, to provide detailed information on the spatial distribution of land cover within a mixed pixel. In CSSM, the sub-pixel mapping problem becomes one of assigning land-cover classes to the sub-pixels while maximizing the spatial dependence by the clonal selection algorithm. Each antibody in CSSM represents a possible sub-pixel configuration of the pixel. CSSM evolves the antibody population by inheriting the biological properties of human immune systems, i.e., cloning, mutation, and memory, to build a memory cell population with a diverse set of locally optimal solutions. Based on the memory cell population, CSSM outputs the value of the memory cell and finds the optimal sub-pixel mapping result. Based on the framework of CSSM, three sub-pixel mapping algorithms with different mutation operators, namely, the clonal selection sub-pixel mapping algorithm based on Gaussian mutation (G-CSSM), Cauchy mutation (C-CSSM), and non-uniform mutation (N-CSSM), have been developed. They each have a similar sub-pixel mapping process, except for the mutation processes, which use different mutation operators. The proposed algorithms are compared with the following sub-pixel mapping algorithms: direct neighboring sub-pixel mapping (DNSM), the sub-pixel mapping algorithm based on spatial attraction models (SASM), the BP neural network sub-pixel mapping algorithm (BPSM), and the sub-pixel mapping algorithm based on a genetic algorithm (GASM), using both synthetic images (artificial and degraded synthetic images) and real remote sensing imagery. The experimental results demonstrate that the proposed approaches outperform the previous sub-pixel mapping algorithms, and hence provide an effective option for the sub-pixel mapping of remote sensing imagery.

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