Fuzzy Control Method for Synchronous Acquisition of High Resolution Image based on Machine Learning

Aiming at the problem of low baud rate of traditional high-resolution image synchronous acquisition fuzzy control method, a high-resolution image synchronous acquisition fuzzy control method based on machine learning is designed. By detecting the fuzzy edge information of high-resolution image, the fuzzy membership function of synchronous acquisition quantity is proposed, and the gradient amplitude of synchronous acquisition quantity of high-resolution image is calculated. The unsupervised learning algorithm based on machine learning is used to cluster the fuzzy control data, so as to determine the fuzzy space of synchronous acquisition quantity of high-resolution image, and calculate the fuzzy feature similarity, the fuzzy control of synchronous acquisition quantity of high resolution image is realized. Experimental results show that the controlled wave rate in this paper solves the problem of low wave rate in 255.63 bps/h-271.33 bps/h, and significantly improves the control accuracy.

Fast Extraction Algorithm for Local Edge Features of Super-Resolution Image

Image super-resolution is getting popularity these days in diverse fields, such as medical applications and industrial applications. The accuracy is imperative on image super-resolution. The traditional approaches for local edge feature point extraction algorithms are merely based on edge points for super-resolution images. The traditional algorithms are used to calculate the geometric center of gravity of the edge line when it is near, resulting in a low feature recall rate and unreliable results. In order to overcome these problems of lower accuracy in the existing system, an attempt is made in this research work to propose a new fast extraction algorithm for local edge features of super-resolution images. This paper primarily focuses on the super-resolution image reconstruction model, which is utilized to extract the super-resolution image. The edge contour of the super-resolution image feature is extracted based on the Chamfer distance function. Then, the geometric center of gravity of the closed edge line and the nonclosed edge line are calculated. The algorithm emphasizes on polarizing the edge points with the center of gravity to determine the local extreme points of the upper edge of the amplitude-diameter curve and to determine the feature points of the edges of the super-resolution image. The experimental results show that the proposed algorithm consumes 0.02 seconds to extract the local edge features of super-resolution images with an accuracy of up to 96.3%. The experimental results show that our proposed algorithm is an efficient method for the extraction of local edge features from the super-resolution images.

EVALUATION OF EFFECTIVENESS OF PATCH BASED IMAGE CLASSIFICATION TECHNIQUE USING HIGH RESOLUTION WORLDVIEW-2 IMAGE

The aim of the current study was to evaluate the performance of patch-based classification technique in land use/land cover classification and to investigate the effect of patch size in thematic map accuracy. To reach desired goal, recently proposed ensemble learning classifiers (i.e., XGBoost and CatBoost) were utilized to classify produced image patches obtained from high-resolution WorldView-2 (WV-2) satellite image. . In order to analyse the effect of varying patch size on classification accuracy, three different window sizes (i.e., 3 × 3, 7 × 7 and 11 × 11) were applied to WV-2 imagery for extracting image patches. Constructed image patches were classified using XGBoost and CatBoost ensemble learning classifiers and thematic maps were constructed for varying patch sizes. Results showed that while XGBoost and CatBoost showed similar classification performances for varying patch size and the estimated highest overall accuracy were %68, %82 and %92 for 11x11, 7 × 7 and 11 × 11 patch sizes, respectively. These findings confirmed that defining class boundaries on the high-resolution image using smaller patches increases the accuracy of thematic maps. In addition, results of patch-based classification were compared the results of LULC maps produced by same classifiers using pixel-based classification method. Overall accuracy of pixel-by-pixel classification of WV-2 image reached to about %94. Furthermore, CatBoost showed superior classification performance in all time compared to XGBoost. All in all, pixel-based CatBoost was found to be more successful in LULC mapping of fine resolution image.

Regularized Multiframe Super-Resolution Image Reconstruction Using Linear and Nonlinear Filters

The primary goal of the multiframe super-resolution image reconstruction is to produce an image with a higher resolution by integrating information extracted from a set of corresponding images with low resolution, which is used in various fields. However, super-resolution image reconstruction approaches are typically affected by annoying restorative artifacts, including blurring, noise, and staircasing effect. Accordingly, it is always difficult to balance between smoothness and edge preservation. In this paper, we intend to enhance the efficiency of multiframe super-resolution image reconstruction in order to optimize both analysis and human interpretation processes by improving the pictorial information and enhancing the automatic machine perception. As a result, we propose new approaches that firstly rely on estimating the initial high-resolution image through preprocessing of the reference low-resolution image based on median, mean, Lucy-Richardson, and Wiener filters. This preprocessing stage is used to overcome the degradation present in the reference low-resolution image, which is a suitable kernel for producing the initial high-resolution image to be used in the reconstruction phase of the final image. Then, L2 norm is employed for the data-fidelity term to minimize the residual among the predicted high-resolution image and the observed low-resolution images. Finally, bilateral total variation prior model is utilized to restrict the minimization function to a stable state of the generated HR image. The experimental results of the synthetic data indicate that the proposed approaches have enhanced efficiency visually and quantitatively compared to other existing approaches.