Satellite Multispectral and Hyperspectral Image De-Noising with Enhanced Adaptive Generalized Gaussian Distribution Threshold in the Wavelet Domain

Abstract

The presence of noise in remote sensing satellite images may cause limitations in analysis and object recognition. Noise suppression based on thresholding neural network (TNN) and optimization algorithms perform well in de-noising. However, there are some problems that need to be addressed. Furthermore, finding the optimal threshold value is a challenging task for learning algorithms. Moreover, in an optimization-based noise removal technique, we must utilize the optimization algorithm to overcome the problem. These methods are effective at reducing noise but may blur some parts of an image, and they are time-consuming. This flaw motivated the authors to develop an efficient de-noising method to discard un-wanted noises from these images. This study presents a new enhanced adaptive generalized Gaussian distribution (AGGD) threshold for satellite and hyperspectral image (HSI) de-noising. This function is data-driven, non-linear, and it can be fitted to any image. Applying this function provides us with an optimum threshold value without using any least mean square (LMS) learning or optimization algorithms. Thus, it is possible to save the processing time as well. The proposed function contains two main parts. There is an AGGD threshold in the interval [−σn, σn], and a new non-linear function behind the interval. These combined functions can tune the wavelet coefficients properly. We applied the proposed technique to various satellite remote sensing images. We also used hyperspectral remote sensing images from AVIRIS, HYDICE, and ROSIS sensors for our experimental analysis and validation process. We applied peak signal-to-noise ratio (PSNR) and Mean Structural Similarity Index (MSSIM) to measure and evaluate the performance analysis of different de-noising techniques. Finally, this study shows the superiority of the developed method as compared with the previous TNN and optimization-based noise suppression methods. Moreover, as the results indicate, the proposed method improves PSNR values and visual inspection significantly when compared with various image de-noising methods.

Data-Driven Urban Traffic Accident Analysis and Prediction Using Logit and Machine Learning-Based Pattern Recognition Models

“…(3) According to the logit model results, the variables of accident time (12)(13)(14)(15)(16)(17)(18) and (18)(19)(20)(21)(22)(23)(24), rainy weather, the accident reason (lack of attention), head-on collision, and KIA Pride vehicle increased the severity of accidents with a positive coefficient. In other words, the significant role of KIA Pride vehicle in the occurrence of accidents, especially at (18-24) accident time (night peak hours' traffic), is noticeable.…”

“…Generally, metaheuristic algorithms and machine learning techniques have been widely used in different engineering studies, especially in transportation problems, which they are in desperate need of complex and accurate solutions to provide more accurate prediction models than statistical methods due to their capability of handlig more complex functions and classification problems [10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25]. Pattern recognition tools and their accurate analysis using optimized prediction tasks are a trendy topic in the two recent years [26][27][28][29][30].…”

Application of Multi-Scale Convolution Neural Network Optimization Image Defogging Algorithm in Image Processing

“…Golilarz NA et al designed a neural network algorithm incorporating adaptive Gaussian distribution to solve the noise problem in remote sensing satellite images. The test results show that, compared with the common denoising algorithm, the denoised image signal-to-noise ratio of this algorithm is significantly higher [6].…”

Approach to Predict Land Subsidence in Old Goafs considering the Influence of Engineering Noise

“…Wavelet noise reduction has been successfully applied in many fields [18][19][20][21][22][23][24][25][26][27][28]; however, its application in the field of surface subsidence prediction in old goafs is relatively rare. Consequently, this study proposes a combined prediction model of wavelet decomposition and noise reduction considering engineering noise (hereinafter referred to as the combined model LSTM-AR).…”