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- W3207427914 abstract "This paper investigates parallel particle swarm optimization (parallel PSO) and mathematical morphology (MM) for classification of very high resolution (VHR) remote sensing images. The proper extraction of spatial features depends on the shape and size of the structuring element (SE) used to probe the image. However, finding the best shape and size of the SE for classification of urban remote sensing imagery is a challenging research topic, because the formats of the features may become more and more complex. For this purpose, this paper proposes a new adaptive algorithm based on the combination of parallel PSO with MM for the construction of adaptive SEs. Such that the shapes and sizes of the constructed SEs change adaptively according to the spatial features of the image and the considered classification task. As a result, the application of parallel PSO reduces the execution time compared to serial PSO, and makes the proposed algorithm can adopt several SEs simultaneously for image classification without complicating the main PSO algorithm. Finally, an experimental example is conducted to show the effectiveness of the proposed algorithms." @default.
- W3207427914 created "2021-10-25" @default.
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- W3207427914 date "2021-07-11" @default.
- W3207427914 modified "2023-10-17" @default.
- W3207427914 title "Parallel Particle Swarm Optimization Algorithm for Classification of Very High Resolution Images Based on Mathematical Morphology" @default.
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- W3207427914 doi "https://doi.org/10.1109/igarss47720.2021.9554150" @default.
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