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Generally, most existing super-resolution (SR) methods do not consider noise, which treats SR reconstruction and denoising as two separate problems and performs separately. However, noise is inevitably introduced in the imaging process. Based on analysis of the degraded model, in this paper, the problems of interpolation and denoising are modeled to estimate the noiseless and missing images under the same framework. By applying local fractal dimension (LFD) into image local feature analysis, a noisy single-image SR method is proposed. For each noisy image, we first construct a rational fractal interpolation model containing scaling factors, which can effectively maintain the inherent properties of the data. Furthermore, the original image structure can be well preserved by applying the interpolation model. Considering the local characteristics of the image, scaling factors are calculated on the basis of the LFDs. Then, through further local feature analysis of the interpolated image, a denoising method based on LFD is proposed for recovering a noiseless image. Finally, a high-quality high-resolution image is obtained. Experimental results demonstrate that our method outperforms the state-of-the-art methods both quantitatively and qualitatively.

Solving non-linear equation is perhaps one of the most difficult problems in all of numerical computations, especially in a diverse range of engineering applications. The convergence and performance characteristics can be highly sensitive to the initial guess of the solution for most numerical methods such as Newton’s method. However, it is very difficult to select reasonable initial guess of the solution for most systems of non-linear equations. Besides, the computational efficiency is not high enough. Taking this into account, based on variational iteration technique, we develop some new iterative algorithms for solving one-dimensional non-linear equations. The convergence criteria of these iterative algorithms has also been discussed. The superiority of the proposed iterative algorithms is illustrated by solving some test examples and comparing them with other well-known existing iterative algorithms in literature. In the end, the graphical comparison of the proposed iterative algorithms with other well-known iterative algorithms have been made by means of polynomiographs of different complex polynomials which reflect the fractal behavior and dynamical aspects of the proposed iterative algorithms.

In recent times, there has been a great increase in the demand of flexible micro-supercapacitors for use in many commercial applications. Flexible capacitors have the potential to replace conventional batteries as they can provide competitive energy storage while being much smaller in size and having greater lifetime than average lithium-ion batteries. The most used design for in-plane flexible micro-supercapacitors is the interdigitated design. However, from findings of various recent studies, it is believed that the capacitance of in-plane EDLC micro-supercapacitors can further be improved by changing electrode designs. Different fractal designs have shown great promise to enhance the electrochemical performance of in-plane micro-supercapacitors by maximizing the active surface area and minimizing the energy lost during ion-transport. In this work, we successfully fabricate graphene-based, laser-scribed flexible micro-supercapacitors and study the effects of change in geometry parameters on total capacitance. Four different geometries for electrode were designed i.e., (1) interdigitated design, (2) fractal F1 (Hilbert) design, (3) fractal F2 (Peano) design, and (4) fractal F3 (Moore) design, and were patterned onto the thin layer of graphene oxide using laser scribing technique. Detailed analysis was presented for the change in capacitance using cyclic voltammetry, galvanic charge-discharge, electrochemical impedance spectroscopy and electrostatic simulation using COMSOL Multiphysics. It was found that fractal design micro-supercapacitors give better performance with higher capacitance and energy density values in comparison to conventional design interdigitated micro-supercapacitors. Among all, Fractal F3 design showed the highest capacitance and energy density value in comparison to other devices. It was found that there were two factors affecting the performance of devices, i.e., (1) a higher active surface area for fractal designs as compared to interdigitated designs with the same unit area, and (2) in addition to electric double layer capacitance, there was also electric field being generated on the edges of electrodes, defined as edging effect. Among both factors, the latter was the major enhancer of capacitance in the fractal design devices.

The human brain is a highly complex structure, which can be only partially described by conventional metrics derived from magnetic resonance imaging (MRI), such as volume, cortical thickness, and gyrification index. In the last years, the fractal dimension (FD) – a useful quantitative index of fractal geometry – has proven to well express the morphological complexity of the cerebral cortex. However, this complexity is likely higher than that we can observe using MRI scanners with 1.5 T or 3 T field strength. Ultrahigh-field MRI (UHF-MRI) improves imaging of smaller anatomical brain structures by exploring down to a submillimetric spatial resolution with higher signal-to-noise and contrast-to-noise ratios. Accordingly, we hypothesized that UHF-MRI might reveal a higher level of the structural complexity of the cerebral cortex. In this study, using an improved box-counting algorithm, we estimated the FD of the cerebral cortex in six public or private T1-weighted MRI datasets of young healthy subjects (for a total of 87 subjects), acquired at different field strengths (1.5 T, 3 T, and 7 T). Our results showed, for the first time, that MRI-derived FD values of the cerebral cortex imaged at 7 T were significantly higher than those observed at lower field strengths. UHF-MRI provides an anatomical definition not achievable at lower field strengths and can improve unveiling the real structural complexity of the human brain.

Terahertz (THz) technology can be widely used in radar, remote sensing, homeland security and counter-terrorism, high-secret data communication and transmission, atmospheric and environmental monitoring, real-time biological information extraction, and medical diagnosis. It is the frontier of current research. However, most of highly sensitive THz detectors work in a relatively narrow frequency range. It attracts great attention to design a high-gain terahertz antenna which operates in a wide spectrum range. In this paper we propose a new structure of fractal butterfly antenna with high gain which can be used for broadband THz detection. This fractal butterfly antenna is constructed by independent fractal unit groups on butterfly antenna, which simplifies the antenna manufacturing process and reduces the manufacturing cost. The antenna can operate in the 0.1-10 THz frequency band, with impedance bandwidth of 85% resonated at 4.9 THz, return loss of as small as −33.59 dB, and maximum gain of 16.95 dB. Among the antenna with 3 to 4 fractal unit groups, the best operation band of the antenna is 6.2-6.4 THz. The proposed fractal butterfly antenna can effectively improve the performance of terahertz detectors, and provide the possibility for detection research in higher frequency bands.

International audience; This study investigated the performance of a Mosaic Frequency Selective Surface (MFSS) structure for two different applications: optical transparency and absorber. The MFSS for optical transparency application is comprised of a polycarbonate substrate with permittivity, epsilon(r) of 2.9, and the MFSS for absorber application utilized a Polyethylene Terephthalate (PET) substrate with epsilon(r) of 2.7. The MFSS unit cell is composed of a conductive metallic element design that integrates the Koch fractal and the double hexagonal loop for the optical transparency application. Meanwhile, the resistive MFSS unit cell with sheet resistivity of 100 Omega/sq is utilized for the absorber application. A Computer Simulation Technology (CST) Microwave Studio software is employed to carry out the calculation and frequency response analysis for both applications. Based on the results, it was concluded that the transparent MFSS yielded a wideband stopband and passband responses (fractional bandwidth (FBW) > 50%) with a low cross-polarization (-37 dB), and a wideband absorptivity response was achieved with thin MFSS absorber. In addition, the simulated and measured responses of the transparent MFSS achieved well-fitted correlations. The findings indicated that the proposed MFSS unit cell able to provide wideband response for both applications.

In the current scenario, the research perspective on esophageal cancer becomes severe, high-prognosis malignancy; poor prognosis is primarily attributed to the fact that most tumors remain asymptomatic and unrelated before it grows through the esophagus. Significant decreases in mortality from esophageal cancer may require effective approaches to detect and nurse more patients at early, curable stages. A new Improved Empirical Wavelet Transform (IEWT) dependent on feature extraction approach and a consistent homology for the diagnosis of early esophageal endoscopic cancer have been proposed in this article. The approach is to convert an input endoscope image into CIE colored spaces L * x * y, and the x* and y* components to create a fusion image for analysis. Further, the two kinds of wavelets are obtained by adding the two forms to the fusion signal. Another is the lower-frequency component provided by the improved empirical wavelet transformation of the wave, and the other is the high- components generated from the Deep Learning-based Complex Empirical Wavelet Transformation (DL-CEWT). The fractal sizes are determined using the box interpolation method for each small block, and the abnormal regions are defined for the basis of their fractal sizes. Binary pictures are obtained by the complex threshold in each frequency variable and then divided into small blocks in every binary image. Using the homology of every block to obtain the new features in the entry image. The extraction strategies for this application are comprehensive and preliminary findings indicate that the method is effective for the early detection of esophageal cancer in an image.

Various failure mechanisms of rolling bearing under different working conditions involve important nonlinear dynamic characteristics. And the incipient fault detection of the complex and non-stationary rolling bearing signal is difficult, especially with multiple interference source components. To address this issue, a novel fault feature extraction method which integrates chaos theory and mathematical morphology is proposed. Firstly, the smooth multi-scale morphological filtering (SMMF) is adopted to reduce the noise in chaotic rolling bearing signals without destroying its original nonlinear dynamic structure. In addition, a method of generalized estimation of morphological fractal dimension called composite multi-scale morphological fractal dimension (CMMFD) is proposed, and applied to quantify the complexity of those orbits reconstructed from rolling bearing signals. The CMMFD improves the identification ability of the original morphological fractal dimension (MFD). Then, CMMFD features are extracted from SMMF denoised signal. Different conditions of rolling bearing signals are distinguished in this feature space. Finally, the CMMFD features are input into a Hidden Markov Model (HMM) training and diagnosis to distinguish different faults of rolling bearing signals. The new diagnosis approach based on SMMF and CMMFD is compared with some existing methods in analysis of simulated signals and experimental rolling bearing signals. Accuracy and efficiency of different approaches are contrasted by analyzing the results of simulation and experiments. Comparative results demonstrate that the proposed diagnosis approach based on SMMF and CMMFD can reliably recognize different fault types at the early stage and have more accurate results.

Cloud extraction is a vital step in remote sensing image processing. Although many advanced cloud extraction methods have been proposed and confirmed to be effective in recent years, there are still difficulties in cloud extraction in areas of high brightness reflectivity covered. High brightness reflectivity cover can have similar spectral characteristics as clouds, and thus, it is easily confused with clouds in cloud extraction schemes. This work presents a novel scheme designed to extract clouds in satellite imagery with high brightness reflectivity covered. The fractal summation method and spatial analysis are used to extract the clouds in the Landsat 8 Operational Land Imager (OLI) images containing high brightness reflectivity covered. The scheme consists of three main steps: cloud extraction based on pixel values, Anselin Local Moran's I value, and anisotropy. Pixel values were applied to extract the clouds associated with anomalies, and the last two steps were conducted to eliminate false anomalies. The findings showed that the cloud-associated anomaly pixel-values well approximate a power-law function, but both the real and fake anomaly patches (e.g., snow/ice, desert, etc.) routinely coexist within the same (fractal) scaleless segments, and that the latter seems to be more significant than the former. Consequently, these results indicate that the diagnostic difference between true and false anomalies must lie in their spatial distribution patterns. Furthermore, experiments confirmed that the fractal dimension and spatial distribution (i.e. Anselin Local Moran's I index and anisotropy) difference between the real and false anomalies displayed a certain universality. The proposed scheme effectively reduces the confusion and misclassification caused by cloud, snow and the highlighted underlying surface. It is of great significance for cloud restoration processing, image analysis, image matching, target detection and extraction, and effective extraction and utilization of remote sensing data.

In this article, the fractal behavior in generalized alternated system is discussed. Take the classical fractal set as an example, the anti-synchronization of the Julia set between two different generalized alternated systems is discussed using the adaptive control method in this article. The anti-synchronization of the Julia set between different generalized alternated systems is also accomplished by synchronizing their iterative tracks. The simulations illustrate the effectiveness and correctness of this control method.

Complex fractals achieved the highest popularity in the last ten years. Researchers used the fixed point iterations to visualize the fractals and compared the image generation times to check the efficiencies of iterations. This article explores the behavior of Jungck-M iteration in the generation of anti-Mandelbrot sets. We define the orbit of Jungck-M iteration and prove its escape criteria. We establish the algorithm for anti-Mandelbrot set and visualize some graphs via proposed iteration. We calculate the image generation times for the generation of anti-Mandelbrot sets in proposed orbit and present the comparison with Jungck-CR iteration. We also discuss the variations in images at different values of the input parameters. Moreover, we present the graphs to show the escape time depends on input parameters.

The purpose of this paper is to introduce the Mandelbrot and Julia sets by using Picard-Mann iteration procedure. Escape criteria is established which plays an important role to generate Mandelbrot and Julia sets. Also, numerous graphical pictures of these sets have been visualized and certain examples have been recognized. Presented results shows that fractal images generated by Picard-Mann iteration procedure are entirely different from those generated in Mann orbit.

In this paper, a novel antenna design comprising planar monopole with fractal slot defected ground structure for circularly polarized radiation is proposed. In the proposed antenna geometry, circular polarization radiation characteristics are obtained by defecting the ground structure with fractal slot. In this approach, CP characteristics are achieved by intensifying the level of cross polar component of the linearly polarized antenna upto a certain level by means of fractal slot inserted in ground structure. Antenna prototype with third order iterative tuning fork shaped fractal slot is fabricated and tested for the validation of simulated results. Parametric studies are also performed to exemplify the process of CP radiations. From the simulated and measured results, it is confirmed that the proposed antenna can be proficiently produced 10 dB return loss bandwidth (BW) nearly ≈37.4%, while 3-dB AR bandwidth (ARBW) is 15.5%, respectively. The proposed antenna can be a credible applicant for LTE Band 13/cellular/PCS /ASW/WiMAX and Wi-Fi applications.

This article first proposes two types of three-dimensional (3D) fractal reference matrices. Then, a new reversible (2, 3) secret image sharing scheme based on a fractal matrix is presented. With the help of the fractal reference matrix, the secret data can be embedded in an original image to generate three meaningful shadow images. It is worth mentioning that these two types of fractal matrices are designed for different hiding capacity, meanwhile the characteristics of the fractal matrix can guarantee good visual quality of the generated shadow images. Moreover, we provide two authentication mechanisms according to different situations, one of the authentication mechanisms can achieve 100% ability cheat detection. When the authentication is passed, we only need two of the three shadow images to extract the correct secret data and perfectly restore the original image. Experimental results show that our proposed scheme provides excellent authentication ability and good visual quality.

Contact resistance of the main circuit is an important parameter for measuring the performance and reliability of relay products. The contact resistance value interacts with the stress and temperature of the contact surface during the relay's operation, and it will change with the ambient temperature and load current. Therefore, it is difficult to calculate the contact resistance when the relay is in the working environment. However, the finite element model used to predict contact resistance in previous studies does not consider the effect of current and temperature on the contact surface stress at the microscopic level. In this paper, based on the fractal theory, we established three-dimensional electrical contact finite element models in micrometer-scale and nano-scale to solve the contradiction between the computational efficiency and accuracy of the finite element model containing fractal surfaces. In the microscopic electrical contact model, the multi-physics coupling process of electric-temperature-stress at the conductive spot is analyzed, to improve the accuracy of the calculation of the local contact resistance at the conductive spot. Through the data transfer between the macroscopic finite element temperature rise model of the relay and the microscopic electrical contact model, the temperature rise and contact resistance of the relay under different load currents are calculated more accurately. The feasibility of the simulation method is proved by comparison with experimental measurement results.

In this study, a novel method based on deep learning was developed for partial discharge (PD) pattern recognition. Traditional PD recognition methods are crucial for extracting features from PD patterns. The method of extracting crucial features is the key to PD pattern identification. The fractal theory is commonly used to determine the features of discharge patterns. The feature distribution of different defect types can be determined according to the fractal dimensions and lacunarity. However, finding fractal features is a complicated process. In this study, a PD image was entered as an input into a deep learning system to reduce the complexity of finding features. First, four defect type of gas-insulated switchgear (GIS) experimental models are established. Then, an LDP-5 inductive sensor (L-sensor) was used to measure the ground line signals caused by PD phenomenon. Second, these electrical signals were transformed into the most representative 3D ($n$ -$q$ -$\varphi$ ) PD patterns. Finally, a convolutional neural network was employed for PD image pattern recognition. A total of 160 sets of PD patterns were measured using a 15-kV GIS. The results obtained with the proposed method were compared with those obtained with the fractal method. The results revealed that the proposed method is easy to use and can easily distinguish various defect types. The proposed approach can determine the GIS insulation status and provide information to construction units for maintenance.

Grey Wolf Optimizer (GWO) simulates the grey wolves’ nature in leadership and hunting manners. GWO showed a good performance in the literature as a meta-heuristic algorithm for feature selection problems, however, it shows low precision and slow convergence. This paper proposes a Modified Binary GWO (MbGWO) based on Stochastic Fractal Search (SFS) to identify the main features by achieving the exploration and exploitation balance. First, the modified GWO is developed by applying an exponential form for the number of iterations of the original GWO to increase the search space accordingly exploitation and the crossover/mutation operations to increase the diversity of the population to enhance exploitation capability. Then, the diffusion procedure of SFS is applied for the best solution of the modified GWO by using the Gaussian distribution method for random walk in a growth process. The continuous values of the proposed algorithm are then converted into binary values so that it can be used for the problem of feature selection. To ensure the stability and robustness of the proposed MbGWO-SFS algorithm, nineteen datasets from the UCI machine learning repository are tested. The K-Nearest Neighbor (KNN) is used for classification tasks to measure the quality of the selected subset of features. The results, compared to binary versions of the-state-of-the-art optimization techniques such as the original GWO, SFS, Particle Swarm Optimization (PSO), hybrid of PSO and GWO, Satin Bowerbird Optimizer (SBO), Whale Optimization Algorithm (WOA), Multiverse Optimization (MVO), Firefly Algorithm (FA), and Genetic Algorithm (GA), show the superiority of the proposed algorithm. The statistical analysis by Wilcoxon’s rank-sum test is done at the 0.05 significance level to verify that the proposed algorithm can work significantly better than its competitors in a statistical way.

A dual-band dual-linear polarization reflectarray configuration is developed for future 5G cellular applications. A single layer unit cell including two pairs of miniaturized fractal patches is designed to operate at two distinct frequencies within the Ka-band (27/32 GHz), in a dual-polarization mode. An in-depth analysis of the unit cell behavior is carried out, to demonstrate the total independence between the designed frequency bands and polarizations. The proposed configuration offers a very simply and thin structure, small unit cell sizes, and low losses, while leading to an independent optimization of the phase at each frequency and polarization. A dual-band/dual-polarized reflectarray prototype is designed and tested, thus demonstrating the unit cell flexibility to offer arbitrary beam directions/shapes at each frequency, for both polarizations.

The comparison of traditional fractal anti-counterfeit labels is based mainly on manual inspection. However, such labels have rich details and complex structures, making the entire identification process labor-intensive. Thus, manual inspections are highly susceptible to low identification accuracy, which produces unreliable results. To best address these disadvantages, an automatic comparison method for fractal anti-counterfeit labels is proposed. The method can effectively extract the color features, texture features, and shape features of anti-counterfeit labels, and perform cluster analysis and comparison. First, a color volume histogram is used to extract the color and pixel space information features from the fractal anti-counterfeit labels. To compensate for the deficiency of using a single feature, texture and shape features were also extracted based on the median robust extended local binary patterns (MRELBP) and Hu moments. Next, based on feature extraction, k-means clustering is performed to ensure that as many of the same types of labels as possible can be divided into the same clusters and that the comparison can only be performed on one or a few clusters. The experimental results show that the speed and quality of fractal anti-counterfeit label comparison effectively improved after the clustering analysis. Furthermore, based on visual identification and unreliable comparison results, the proposed method is expected to help consumers quickly identify low-quality, counterfeit products.

Due to extensive research on complex networks, fractal analysis with scale invariance is applied to measure the topological structure and self-similarity of complex networks. Fractal dimension can be used to quantify the fractal properties of the complex networks. However, in the existing box covering algorithms, accurately calculating the fractal dimension of complex networks is still an NP-hard problem. Therefore, in this paper, an improved overlapping box covering algorithm is proposed to explore a more accurate and effective method to calculate the fractal dimension of complex networks. Moreover, in order to verify the effectiveness of the algorithm, the improved algorithm is applied to six complex networks, and compared with other algorithms. Finally, the experimental results demonstrate that the improved overlapping box covering algorithm can cover the whole networks with fewer boxes. In addition, the improved overlapping box covering algorithm is a high accuracy and low time complexity method for calculating the fractal dimension of complex networks.

In this paper, we constructed a new rational fractal interpolation model with scale factors and shape parameters. The model obtains different expressions by changing the scale factor, according with the diversity of image features. On the basis of the constructed model, this paper presents a novel adaptive rational fractal magnification (ARFM) algorithm based on local fractal feature analysis. Firstly, the image is divided into different regions according to an adaptive threshold determined by a calculated fractal dimension, and the scaling factor is calculated based on the relationship between global fractal dimension (GFD) and local fractal dimension (LFD). Secondly, for different regions, different interpolation forms are selected according to regional characteristics. Thirdly, parameter optimization and sub-block selection are studied to further enhance the quality of the magnified image. The experimental results illustrate that the performance of the proposed ARFM algorithm is very competitive compared with the latest magnification algorithms.

This paper presents an analysis of sea surface roughness from visible images based on fractals features for the first time. We measure fractal dimensions of sea surface images by the methods of box counting, fractional Brownian motion, and area measurement. The empirical values of sea surface roughness are found from measured wind speed and an empirical relation. The correlations between wind speeds (or sea surface roughness) and fractal dimensions are evaluated based on the data from a field experiment. Further, four types of noises with different parameters are introduced to the sea surface images. Then noise suppression performances of six methods are evaluated. Our experiments have demonstrated that our fractal methods and empirical relation between wind speeds and fractal dimensions are effective for analyzing roughness changes of sea surface from visible images.

International audience; Multi-objective optimization problems (MOPs) have been widely studied during the last decades. In this paper, we present a new intrinsically parallel approach based on Fractal decomposition (FDA) to solve MOPs. The key contribution of the proposed approach is to divide recursively the decisionspace using hyperspheres. Two different methods were investigated: the first one is based on scalarization that has been distributed on a parallel multi-node architecture virtual environments and taking profit from the FDA’s properties, while the second method is based on Pareto dominance sorting. A comparison with state of the art algorithms on different well known benchmarks shows the efficiency and the robustness of the proposed decomposition approaches.

International audience; In this work, an Artificial Magnetic Conductor (AMC) metasurface composed by a Sierpinski Carpet fractal unit cell is analyzed using the Generalized Boundary Conditions (GBCs). Since the fractal structure cannot be represented using analytical expressions, the detailed GBCs model is presented to analyze angular and polarization stabilities of the obtained metasurface. The angular stability of the fractal geometry is compared with the square loop and Jerusalem Cross geometries, from the stable bandwidth and frequency deviation. Finally, the computational effort and the accuracy of the proposed method is compared to conventional methods through performance parameters, thus validating GBCs as an alternative method to characterize AMCs.

The fractal shapes in the nature, their characteristics, also, opinions about fractal dimension are given in this article. The phenomena of geophysical origin of fractal geometric shapes and potential useful tools for describing complex shapes are illustrated. Despite, the fractals are widely used in geographical areas; the opinions which cause inconsistent results from different fractal computational algorithms have been expressed.Fractal dimension was firstly introduced as the coefficient which describes geometrically complex shapes, the details are considered more important than a completely drawen picture. The theoretical fractal dimension for sets which describes simple geometric shapes is equal to the usual Euclidean or topological dimension. The theoretical fractal dimension for sets which describe points, is equal to 0, the fractal dimension for sets which describe straight line with only length, is equal to 1, the fractal dimension for sets which describe surface, is equal to 2, and the fractal dimension for sets which describe volume, is equal to 3.

Ultrasound cavitation has been studied by different authors for understanding the mechanism of producing the cavitation bubbles and the signals that appear in the cavitation field in different liquids. Although it has been proved that the properties of the signals depend on the liquid, their characteristics are not entirely known. In this context, our study aims at determining the fractal dimension of a signal collected in water and analyze the existence of a fractal pattern. It is shown that the signal presents a multifractal behavior.

In order to better collect UHF signal of partial discharge of transformer, the fourth-order Hilbert fractal antenna model is improved. HFSS is used to optimize the parallel wire, short circuit terminal and additional wire in Hilbert fractal antenna, and the optimal values of three kinds of wire length, width, dielectric substrate thickness and dielectric constant are obtained. After the laboratory test, there are four resonant frequency points in the 0.3GHz-3GHz UHF range. The bandwidth of 0.3GHz-1GHz is broadened to 500MHz, greatly improves the gain parameters and has good directivity. The antenna is small in size and can be installed in the transformer box. It is suitable for collecting UHF partial discharge signals of oil immersed transformer.

Satellite images comprise very complex spatial features that challenge the traditional image processing and classification techniques. In the last decade, fractal compression of satellite images had emerged to ease and improve the classification strength of the machine learning classifiers. Image compression has become the most essential step to deal with the high-dimensional data produced by remote sensing applications. The paper represents the survey study focussing on analyzing the effect of fractal features on satellite image classification using various machine learning techniques. In the process, the recently published research papers, conference articles, authenticated writings published in Google Scholar, Elsevier, Springer, and other websites have been considered. The work also summarised the generalized steps followed by various literature cited satellite image classification works. The study also summarises the best classifier among the group of classifiers used by researchers to guide future researchers. The comparative analysis based on the class predicted using different classifiers had shown that with time fractal-based satellite image classification had gained much popularity over the past decade.