An Efficient Hybrid De-Noising Method for Remote Sensing Images
01 Jan 2022-Vol. 792, pp 449-459
TL;DR: In this paper, an efficient hybrid bilateral-guided (HBG) filter has been proposed that can filter noise faithfully from the satellite digital image, which preserves edges of the original image while removing noises, thereby supporting edge detection and other image processing.
Abstract: 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
References
More filters
TL;DR: An empirical study of the optimal bilateral filter parameter selection in image denoising applications and an extension of the bilateral filter: multiresolution bilateral filter, where bilateral filtering is applied to the approximation subbands of a signal decomposed using a wavelet filter bank.
Abstract: The bilateral filter is a nonlinear filter that does spatial averaging without smoothing edges; it has shown to be an effective image denoising technique. An important issue with the application of the bilateral filter is the selection of the filter parameters, which affect the results significantly. There are two main contributions of this paper. The first contribution is an empirical study of the optimal bilateral filter parameter selection in image denoising applications. The second contribution is an extension of the bilateral filter: multiresolution bilateral filter, where bilateral filtering is applied to the approximation (low-frequency) subbands of a signal decomposed using a wavelet filter bank. The multiresolution bilateral filter is combined with wavelet thresholding to form a new image denoising framework, which turns out to be very effective in eliminating noise in real noisy images. Experimental results with both simulated and real data are provided.
457 citations
TL;DR: Four types of noise (Gaussian noise, Salt & Pepper noise, Speckle noise and Poisson noise) are used and image de-noising performed for different noise by Mean filter, Median filter and Wiener filter .
Abstract: Image processing is basically the use of computer algorithms to perform image processing on digital images. Digital image processing is a part of digital signal processing. Digital image processing has many significant advantages over analog image processing. Image processing allows a much wider range of algorithms to be applied to the input data and can avoid problems such as the build-up of noise and signal distortion during processing of images. Wavelet transforms have become a very powerful tool for de-noising an image. One of the most popular methods is wiener filter. In this work four types of noise (Gaussian noise , Salt & Pepper noise, Speckle noise and Poisson noise) is used and image de-noising performed for different noise by Mean filter, Median filter and Wiener filter . Further results have been compared for all noises.
203 citations
TL;DR: It can be concluded that the BA can be successfully applied to solve crop type classification problems using a multispectral satellite image and is compared with the existing hybrid approach such as the BA with K-means.
Abstract: Among the multiple advantages and applications of remote sensing, one of the most important uses is to solve the problem of crop classification, i.e., differentiating between various crop types. Satellite images are a reliable source for investigating the temporal changes in crop cultivated areas. In this letter, we propose a novel bat algorithm (BA)-based clustering approach for solving crop type classification problems using a multispectral satellite image. The proposed partitional clustering algorithm is used to extract information in the form of optimal cluster centers from training samples. The extracted cluster centers are then validated on test samples. A real-time multispectral satellite image and one benchmark data set from the University of California, Irvine (UCI) repository are used to demonstrate the robustness of the proposed algorithm. The performance of the BA is compared with two other nature-inspired metaheuristic techniques, namely, genetic algorithm and particle swarm optimization. The performance is also compared with the existing hybrid approach such as the BA with K-means. From the results obtained, it can be concluded that the BA can be successfully applied to solve crop type classification problems.
86 citations
TL;DR: The obtained results indicate that the postevent geometrical features (relative change of different damage levels with respect to each other) along with the ANFIS model can help to reach better results in building damage detection.
Abstract: Building damage detection after earthquake would help to rapid relief and response of disaster. In this study, an efficient method was proposed for building damage detection in urban area after earthquake using pre-event vector map and postevent pan-sharpened high spatial resolution image. At first, preprocessing was applied on the postevent satellite image. Second, results of pixel- and object-based classifications were integrated. In the following, geometric features of buildings were extracted including area, rectangular fit ( $\text {rect\_fit}$ ), and convexity. A decision-making system based on these features and an adaptive network-based fuzzy inference system (ANFIS) model was designed to attain building damage degree. A comprehensive sensitivity analysis was carried out to find proper parameters of the ANFIS model leading to accurate damage results. The proposed method was tested over earthquake data set of Bam city in Iran. The results of our method indicate that an overall accuracy of 76.36% and kappa coefficient of 0.63 were achieved to identify building damage degree. The obtained results indicate that the postevent geometrical features (relative change of different damage levels with respect to each other) along with the ANFIS model can help to reach better results in building damage detection.
66 citations
TL;DR: A very efficient method to restore image corrupted by high-density impulse noise by detecting both the number and position of the noise-free pixels in the image and iteratively executed to replace the neighbor noise pixels until convergence.
Abstract: Median filtering computation for noise removal is often used in impulse noise removal techniques, but the difficulties in removing high-density noise aspect restrict its development. In this paper, we propose a very efficient method to restore image corrupted by high-density impulse noise. First, the proposed method detects both the number and position of the noise-free pixels in the image. Next, the dilatation operation of the noise-free pixels based on morphological image processing is iteratively executed to replace the neighbor noise pixels until convergence. By doing so, the proposed method is capable to remove high-density noise and therefore reconstruct the noise-free image. Experimental results indicate that the proposed method more effectively removes high-density impulse noise in corrupted images in comparison with the other tested state-of-the-art methods. Additionally, the proposed method only requires moderate execution time to achieve optimal impulse noise removal.
31 citations