Super-Resolution Reconstruction of Remote Sensing Images Using Multifractal Analysis

Satellite remote sensing (RS) is an important contributor to Earth observation, providing various kinds of imagery every day, but low spatial resolution remains a critical bottleneck in a lot of applications, restricting higher spatial resolution analysis (e.g., intra-urban). In this study, a multifractal-based super-resolution reconstruction method is proposed to alleviate this problem. The multifractal characteristic is common in Nature. The self-similarity or self-affinity presented in the image is useful to estimate details at larger and smaller scales than the original. We first look for the presence of multifractal characteristics in the images. Then we estimate parameters of the information transfer function and noise of the low resolution image. Finally, a noise-free, spatial resolution-enhanced image is generated by a fractal coding-based denoising and downscaling method. The empirical case shows that the reconstructed super-resolution image performs well in detail enhancement. This method is not only useful for remote sensing in investigating Earth, but also for other images with multifractal characteristics

An Anisotropic Diffusion Adaptive Filter for Image Denoising and Restoration Applied on Satellite Remote Sensing Images

This paper proposes an operating approach based on the anisotropic diffusion method to restore and denoise Satellite Remote Sensing Images (SRSIs). The contents of the approach are the motion by mean curvature to detect the noise direction for each degraded pixel and preserve the original edges of the image, and the gradient in the Gaussian kernel which restores the degraded pixel locally, assuring the estimation of its original value and saving the contrast of the image. The algorithm, concluded by our proposed system, treats noised SRSIs regardless of noise type, so better restoration is achieved. Experiments of the proposed system and of other approaches were conducted in MATLAB in order to demonstrate the efficiency of the proposed approach and its performance was confirmed through evaluation with PSNR and SSIM

Microstructures And Nanomechanical Properties Of The Bakken Shale

The focus of this thesis is to study the pore structures along with mechanical properties of the shale rocks from the Bakken Formation. The pore structures that are obtained from the SEM image analysis method showed that total surface porosity of the studied samples is less than 12% and that organic porosity is not the main contributor to total porosity for the samples analyzed. Clay minerals and feldspar have a positive influence on porosity while quartz, pyrite, and TOC has a negative impact. The results from the multifractal theory and lacunarity methods based on the segmented SEM images indicated that pores distribution and size in Bakken shale are heterogeneous. Regarding gas adsorption analysis, the results showed that all range of pore sizes: micro (\u3c2 nm), meso (2-50 nm) and macro-pores (\u3e50 nm) exist in the Bakken shale samples. Meso-pores and macro-pores are the main contributors to the porosity for these samples. In comparison with the Middle Bakken, samples from the Upper and Lower Bakken exhibited more micro pore volumes. The deconvolution of the pore distribution function from the combination of N2 and CO2 adsorption results proved that five typical pore size families exist in the Bakken shale samples: one micro-pore, one macro-pore and three meso-pore size clusters. In order to analyze the heterogeneity of the pore structures from gas adsorption, multifractal method was applied to analyze adsorption isotherms (CO2 and N2). The results explained that the generalized dimensions derived from CO2 and the N2 adsorption isotherms decrease as q increases, demonstrating a multifractal behavior. Samples from the Middle Bakken demonstrated the smallest average H value and largest average α10-- α10+ for micropores while samples from the Upper Bakken depicted the highest average α10-- α10+ for the meso-macropores. This indicated that the Middle Bakken and the Upper Bakken have the largest micropore and meso-macropore heterogeneity, respectively. The impact of rock composition on pore structures showed that organic matter could increase the micropore connectivity and reduce micropore heterogeneity. This study was followed by mechanical analysis of shale samples from the Bakken. Statistical grid nanoindentation method was applied to analyze mechanical properties of the Bakken. Then the Mori-Tanaka scheme was carried out to homogenize the elastic properties of the samples and upscale the nanoindentation data to the macroscale. The discrepancy between the macro-mechanical modulus from the homogenization and unconfined compression test was less than 15% which was found acceptable. The creep analysis of the samples describes that minerals with various mechanical properties exhibit different creep behavior. Under the same constant load and time conditions, the creep displacement of hard minerals would be smaller than the soft ones. On the contrary, the changes in mechanical properties (storage modulus, loss modulus, complex modulus, and hardness) of hard minerals are larger than soft minerals. The results from curve fitting led us to conclude that the changes in creep displacement, storage modulus, complex modulus and hardness with respect to the creep time would follow a logarithmic function

Super-Resolution Reconstruction of Remote Sensing Images Using Multifractal Analysis

Satellite remote sensing (RS) is an important contributor to Earth observation, providing various kinds of imagery every day, but low spatial resolution remains a critical bottleneck in a lot of applications, restricting higher spatial resolution analysis (e.g., intraurban). In this study, a multifractal-based super-resolution reconstruction method is proposed to alleviate this problem. The multifractal characteristic is common in Nature. The self-similarity or self-affinity presented in the image is useful to estimate details at larger and smaller scales than the original. We first look for the presence of multifractal characteristics in the images. Then we estimate parameters of the information transfer function and noise of the low resolution image. Finally, a noise-free, spatial resolutionenhanced image is generated by a fractal coding-based denoising and downscaling method. The empirical case shows that the reconstructed super-resolution image performs well indetail enhancement. This method is not only useful for remote sensing in investigating Earth, but also for other images with multifractal characteristics

Super-resolution mapping

Super-resolution mapping is becoming an increasing important technique in remote sensing for land cover mapping at a sub-pixel scale from coarse spatial resolution imagery. The potential of this technique could increase the value of the low cost coarse spatial resolution imagery. Among many types of land cover patches that can be represented by the super-resolution mapping, the prediction of patches smaller than an image pixel is one of the most difficult. This is because of the lack of information on the existence and spatial extend of the small land cover patches. Another difficult problem is to represent the location of small patches accurately. This thesis focuses on the potential of super-resolution mapping for accurate land cover mapping, with particular emphasis on the mapping of small patches. Popular super-resolution mapping techniques such as pixel swapping and the Hopfield neural network are used as well as a new method proposed. Using a Hopfield neural network (HNN) for super-resolution mapping, the best parameters and configuration to represent land cover patches of different sizes, shapes and mosaics are investigated. In addition, it also shown how a fusion of time series coarse spatial resolution imagery, such as daily MODIS 250 m images, can aid the determination of small land cover patch locations, thus reducing the spatial variability of the representation of such patches. Results of the improved HNN using a time series images are evaluated in a series of assessments, and demonstrated to be superior in terms of mapping accuracy than that of the standard techniques. A novel super-resolution mapping technique based on halftoning concept is presented as an alternative solution for the super-resolution mapping. This new technique is able to represent more land cover patches than the standard techniques

Super-resolution mapping

Super-resolution mapping is becoming an increasing important technique in remote sensing for land cover mapping at a sub-pixel scale from coarse spatial resolution imagery. The potential of this technique could increase the value of the low cost coarse spatial resolution imagery. Among many types of land cover patches that can be represented by the super-resolution mapping, the prediction of patches smaller than an image pixel is one of the most difficult. This is because of the lack of information on the existence and spatial extend of the small land cover patches. Another difficult problem is to represent the location of small patches accurately. This thesis focuses on the potential of super-resolution mapping for accurate land cover mapping, with particular emphasis on the mapping of small patches. Popular super-resolution mapping techniques such as pixel swapping and the Hopfield neural network are used as well as a new method proposed. Using a Hopfield neural network (HNN) for super-resolution mapping, the best parameters and configuration to represent land cover patches of different sizes, shapes and mosaics are investigated. In addition, it also shown how a fusion of time series coarse spatial resolution imagery, such as daily MODIS 250 m images, can aid the determination of small land cover patch locations, thus reducing the spatial variability of the representation of such patches. Results of the improved HNN using a time series images are evaluated in a series of assessments, and demonstrated to be superior in terms of mapping accuracy than that of the standard techniques. A novel super-resolution mapping technique based on halftoning concept is presented as an alternative solution for the super-resolution mapping. This new technique is able to represent more land cover patches than the standard techniques