Abstract
In the present work,an image extraction technique has been designed to fix the center spot of the tropical cyclone. The cyclone prediction is processed within the CBIR system and hence it established a well-effective output. A raw cyclone image is being processed under the image processing techniques such as Adaptive Gaussian Notch Filter (Pre-processing), Adaptive Thresholding (segmentation) and Feature-extraction. The extracted features are collectively used as an input to the CNN-HMO classifier which produces a relevant classified output. Relevance feedback is a key factor which passes these corresponding outputs to the user. User selects the required cyclone image after then, the image extraction process of PSO based density matrix takes place. The extractor has variance, gradient and density matrix (DM) to predit the center spot. So that the brightness temperature is computed for each pixel coordinates in both zonal and meridional axial direction. After then DM point converges over every pixel in an image and a PSO is followed towards the pixel orientation of each convergence line to determine the best optimal solution (i.e.,) the center of the Tropical Cyclone. With this optimizing criterion, the center of the Tropical Cyclone is generated.
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Communicated by: H. Babaie
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Mubeen, S.M.M., Priya, M.S. & Vijayaraj, M. A novel approach for predicting the tc center of remotely sensed images using pso based density matrix. Earth Sci Inform 15, 197–209 (2022). https://doi.org/10.1007/s12145-021-00711-5
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DOI: https://doi.org/10.1007/s12145-021-00711-5