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Novel Image Classification technique using Particle Filter Framework optimised by Multikernel Sparse Representation BABT
N. R,Bhuvaneswari; V.G,Sivakumar.
ABSTRACT The robustness and speed of image classification is still a challenging task in satellite image processing. This paper introduces a novel image classification technique that uses the particle filter framework (PFF)-based optimisation technique for satellite image classification. The framework uses a template-matching algorithm, comprising fast marching algorithm (FMA) and level set method (LSM)-based segmentation which assists in creating the initial templates for comparison with other test images. The created templates are trained and used as inputs for the optimisation. The optimisation technique used in this proposed work is multikernel sparse representation (MKSR). The combined execution of FMA, LSM, PFF and MKSR approaches has resulted in a...
Tipo: Info:eu-repo/semantics/article Palavras-chave: Multikernel Sparse Representation; Image Classification; Sparse Learning; Level Set Method; Particle Filter Framework; Remote Sensing.
Ano: 2016 URL: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S1516-89132016000300606
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