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An Efficient Hybrid De-Noising Method for Remote Sensing Images

R.Avudaiammal, S.Devipriya, A.Swarnalatha,
Published in Springer Science and Business Media Deutschland GmbH
Volume: 792
Pages: 449 - 459

In recent years, analysis of remote sensing imagery has become significant due to its good representation of data in region of interest (ROI) in the world. However, the noise-corrupted satellite image data may not be capable to recognize and analyze it accurately. It necessitates the usage of suitable image de-noising that aids in improving the clarity, sharpness of image for further processing like feature extraction and enhanced analysis of the image data. In this paper, an efficient hybrid bilateral-guided (HBG) filter has been proposed that can filter noise faithfully from the satellite digital image. The attractive feature of the HBG filter is its focus on preserving edges of the original image while removing noises, thereby supporting edge detection and other image processing in a better way. Weiner filtering, median filtering, bilateral filtering, guided filtering and HBG are implemented using MATLAB R2016a on Intel core i3 system. These filtering techniques are carried out on Gaussian noise, salt-and-pepper noise and speckle noise-corrupted satellite images. The performance measures used are mean square error (MSE) and peak signal-to-noise ratio (PSNR). PSNR of HBG is 83%, 76% and 81% more than that of conventional bilateral filter, and MSE of HBG is 49%, 50% and 49% lesser than that of conventional guided filter for 50% salt-and-pepper noise, speckle noise and Gaussian noise-corrupted image, respectively. It is also proved that PSNR of proposed HBG is 74, 71 and 69% more than that of existing hybrid median Wiener filter (HMW) and MSE of HBG is 85%, 95% and 84% lesser than that of hybrid median Wiener filter (HMW) for 50% Gaussian noise, salt-and-pepper noise and speckle noise-corrupted image, respectively. Thus, the simulation outcomes show that the HBG filtering is suitable for removal of all types of noise from satellite images

About the journal
JournalData powered by TypesetLecture Notes in Electrical Engineering
PublisherData powered by TypesetSpringer Science and Business Media Deutschland GmbH
Open AccessNo