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Morphological operations, one of the techniques for processing the shapes of objects within images, are primarily categorized into erosion and dilation operations. Erosion operation entails shaving off the periphery of objects, thereby eliminating noise-like artifacts within the image and potentially enhancing object boundaries or separating touching objects. Conversely, dilation operation involves expanding the periphery of objects, enabling the filling of small internal void spaces. When morphological operations are implemented in hardware, their execution time is significantly reduced compared to software implementations. This paper proposes a circuit design aimed at implementing morphological operations for binary images using FPGA. The proposed circuit architecture is designed to minimize the relatively expensive RAM resources, while maximizing computational efficiency. The performance of the proposed circuitry is evaluated by assuming implementation on AMD's FPGA platform and is compared against the results obtained from morphological operation circuits implemented using the AMD's Vitis vision library, confirming its effectiveness. [ABSTRACT FROM AUTHOR]

In this paper, we propose a method of embedding images into a speech signal. An image is embedded in the high frequency band above 16 kHz of the speech signal to be difficult to perceive for humans. In the proposed method, the image is embedded after being binarized by the error diffusion method. When the image is embedded, we apply an iterative phase restoration method to avoid noise generation. We also investigate a compensation method for an extracted image whose quality degrades due to transmission channel characteristics. Simulation and real‐environmental experiments showed that the proposed method significantly improved the quality of the extracted image on the receiver side. [ABSTRACT FROM AUTHOR]

Thresholding techniques are key pillars of image processing, especially for distinguishing objects in complex environments. This paper examines four types of thresholding strategies, each based on different theories, practical, popular, and advanced. Through a thorough literature review, the paper explains the thresholding techniques, thresholding operations, evaluation metrics, image processing techniques, and Python code for ROI of binary images in an understandable manner . The findings underscore the significance of thresholding in various applications, from object recognition to medical imaging, and highlight the importance of selecting appropriate thresholding methods based on image characteristics. [ABSTRACT FROM AUTHOR]

In this paper, we present three different smart mapping schemes that improve on the quickness of dissimilarity detection between images. We call the mapping schemes smart because the mapping order is setup intelligently to detect dissimilarity quickly by concentrating its search near the center of the images, which is usually the region of interest in a given scene. Thus, smart mapping is well suited for images when the differences between them are expected to be concentrated near the center of the image. We construct a mapping vector (MV) that contains an ordered list of point mappings which is employed to map points between images in an efficient manner. The focus in this paper is on applying the three different smart mapping schemes to binary images. Furthermore, we test three different mapping densities with each smart mapping scheme and analyze the results. Tests are conducted on two image sets and dissimilarity detection results are compared to results obtained via random mapping, which had been shown to be extremely fast, as predicted by the probabilistic matching model for binary images (PMMBI). We show that by employing smart mapping a great improvement in dissimilarity detection quickness is possible when dissimilarity between images is concentrated near the center of the scene. [ABSTRACT FROM AUTHOR]

The diffusive transport through a porous electrode is studied here, based on scanning electron microscope (SEM) images of the porous layer, made by electrodeposition of Nickel oxide over nanostructured carbon film. Lattice Boltzmann model (LBM) is employed to simulate the concentration profile in the pore network considering bounceback at the solid nodes of scanning electron images (SEI). The direct use of a grayscale image with probabilistic bounceback as per the pixel-wise intensities is explored vis-à-vis the binarization of images by Otsu's thresholding with total bounceback at the solid grids. The diffusion pathways are delineated, and the isolated voids are identified from the concentration changes across the pore network. The fraction of isolated voids, the overall diffusivity, and the average concentration in voids are studied for 300 SEIs. The distributions of these three parameters across the SEIs show the heterogeneity in the pore structure. [ABSTRACT FROM AUTHOR]

Copyright of Przegląd Elektrotechniczny is the property of Przeglad Elektrotechniczny and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Fractal and multifractal analysis of porous images allow the description of porous media through a scale-invariant understanding. There have been numerous works that have used these analysis techniques for the description of a great variety of real porous media. However, these studies are usually comparative, being difficult to discern the role played by the pore size and pore distribution in the results of fractal and multifractal analysis. This works develops an in-depth study of different synthetic porous media from a fractal and multifractal approach, in which both the pore size and its distribution in the medium are parameterized. Thus, a set of synthetic binary images have been generated obtaining deterministic and random structures with different fixed pore sizes and also with different rates of pore sizes. Lacunarity is also calculated in order to complete the aforementioned analysis. Results evinces that fractal dimension increases with pore size and that it is higher when the pore distribution obeys a random distribution versus a deterministic one. However, when the pore size is very large, fractal dimension is similar regardless of the pore distribution. From a multifractal approach, pore size is negatively correlated with the degree of multifractality. In fact, in images with mixtures of different pore sizes it is also found that the greater the ratio of small pores, the greater degree of multifractality. By contrast, when the ratio of large pores is relevant, the degree of multifractality also increases due to the merging of macro-pores. [ABSTRACT FROM AUTHOR]

Since, in many routine analytical laboratories, a stereomicroscope coupled with a digital camera is not equipped with advanced software enabling automatic detection of features of observed objects, in the present study, a procedure of feature detection using open-source software was proposed and validated. Within the framework of applying microscopic expertise coupled with image analysis, a set of digital images of microplastic (MP) items identified in organs of fish was used to determine shape descriptors (such as length, width, item area, etc.). The edge points required to compute shape characteristics were set manually in digital images acquired by the camera coupled with a binocular, and respective values were computed via the use of built-in MotiConnect software. As an alternative, a new approach consisting of digital image thresholding, binarization, the use of connected-component labeling, and the computation of shape descriptors on a pixel level via using the functions available in an OpenCV library or self-written in C++ was proposed. Overall, 74.4% of the images were suitable for thresholding without any additional pretreatment. A significant correlation was obtained between the shape descriptors computed by the software and computed using the proposed approach. The range of correlation coefficients at a very high level of significance, according to the pair of correlated measures, was higher than 0.69. The length of fibers can be satisfactorily approximated using a value of half the length of the outer perimeter (r higher than 0.75). Compactness and circularity significantly differ for particles and fibers. [ABSTRACT FROM AUTHOR]

Videos are one of the most substantial evidence that can be used to detect incidents. However, videos can be altered easily using current technologies. Alterations can be made for malicious purposes. Therefore, it is essential to determine the integrity of the videos that will be used as evidence or to give people the right idea. Alterations on the videos that have not previously added control data are within the scope of passive fraud detection. This study proposes an effective solution for detecting frame duplication attacks, which is one of the passive forgery types. The study is based on the visualization of feature vectors. A binary image is created with the feature matrix using feature vectors. Thus, a representative approach to the problem is presented. The forged frame-group template is obtained by processing the binary image, and then a search is done using this template. The proposed method provides solutions for both uncompressed and compressed videos. The algorithm's durability against compression has been tested by evaluating MPEG4 and H264 coded videos. A blurring attack can also be applied to the altered videos to hide the forgery. The results show that it is resistant to blurring attacks. Another factor that complicates fraud detection is the location of the forgery. The algorithm can detect forgery at the beginning or end of the video. The source of the forged frames can also be detected in the study. Experimental results show that the algorithm is resistant to compression, fast, and has a high accuracy rate. [ABSTRACT FROM AUTHOR]

This paper presents a high-speed skeletonization algorithm for detecting the skeletons of linear objects from their binary images. The primary objective of our research is to achieve rapid extraction of the skeletons from binary images while maintaining accuracy for high-speed cameras. The proposed algorithm uses edge supervision and a branch detector to efficiently search inside the object, avoiding unnecessary computation on irrelevant pixels outside the object. Additionally, our algorithm addresses the challenge of self-intersections in linear objects with a branch detection module, which detects existing intersections and initializes new searches on emerging branches when necessary. Experiments on various binary images, such as numbers, ropes, and iron wires, demonstrated the reliability, accuracy, and efficiency of our approach. We compared the performance of our method with existing skeletonization techniques, showing its superiority in terms of speed, especially for larger image sizes. [ABSTRACT FROM AUTHOR]

DNA-based data storage is a rapidly evolving technology that could solve the rising energy and space needs of current information storage. However, DNA's stability limits its potential for future data storage systems. To address limitations to the current working storage method, we developed a novel method to store DNA in an aqueous solution with enhanced stability. Forming a Transcription Factor A Mitochondria (TFAM)-DNA complex in an aqueous solution, where TFAM encapsulates the DNA to protect it from UV irradiation and oxidation stress, could enhance the stability of DNA during storage and retrieval. First, we cloned the TFAM gene, overexpressed the TFAM protein, and purified the TFAM protein from E. coli. Next, we designed a smiley face pixel information in a black and white image (10x10). Then, we converted the data into binary numbers (white=0, black=1). The 10x10 binary numbers were then switched to nucleotides, with zero being either G or C and one being A or T. The image was encoded into nucleotides, synthesized into DNA, and cloned into a plasmid vector. After each stress was induced, the cloned DNA plasmid was sequenced and decoded back to the original image. Our result shows that the TFAM-DA complex provides a stable storage method even with harsh UV irradiation and oxidative stress conditions in aqueous solutions. In conclusion, our storage method's novelty allows for frequent access to stored data in an aqueous solution and provides protection from external stress. [ABSTRACT FROM AUTHOR]

In the process of handwritten signature recognition, preprocessing is an important step before calculation its informative features. Signatures of any person always have some differences, in addition, they can be of different colors, sizes and orientations. After signature digitization, their images may contain some noise. The purpose of signature image preprocessing is to obtain the most invariant representation of the digital image of a person's signature, which will allow us to identify him or define that the signature is forged.This paper describes a sequence of transformations necessary to perform preprocessing of the signature image and form its representation of a single orientation and size. It is assumed that there are no graphic elements in the image that are not related to the signature and the background is relatively uniform. The transformations under consideration sequentially perform binarization of the signature image, its filtering, rotation, cropping of the circumscribed rectangle and scaling to a fixed size.The described preprocessing procedures were applied to a number of available digital signature image databases, such as CEDAR, BHSig260-Bengali, BHSig260-Hindi. Experiments on signature recognition confirm that the presented approach to the signature image preprocessing leads to increasing of the recognition accuracy.

• First use of binary images for building noise attenuation calculation. • Binary image & Bresenham algorithm for efficient building noise attenuation calculation. • Strategic noise sub-map tasking of road segments enhances parallel computation. • Multi-source data approach for acquiring strategic noise maps. Generally, when generating noise maps, the Weiler-Atherton algorithm is employed for polygon intersection calculations to estimate building obstruction attenuation. However, for larger areas, the computational efficiency of this method heavily depends on computational power and expensive hardware. To address these challenges, our method employs binary images to streamline the calculation of intersection area between the building polygon and the noise propagation path, thereby improving computational efficiency. The proposed method consists of two features: converting polygon shapefiles into binary images and subsequently applying the Bresenham Algorithm to calculate building occlusion attenuation. Our study demonstrated that this approach reduced the calculation time to 39.1 % of the original duration, with a mean absolute discrepancy between the two methods of 0.77 dB(A). By utilizing the proposed method to generate noise maps with comparable accuracy, our approach offers an attractive alternative that reduces hardware requirements. [ABSTRACT FROM AUTHOR]

Automatic detection of Red Blood Cells (RBCs) by using image-processing techniques is important in determining blood disorders and diseases. This study proposes an algorithm for automatic detection of red blood cells in optical microscopy images of blood smears. The proposed method depends on binary conversion at multi-threshold and includes a morphological operation as erosion and image fill according to certain conditions. In this study, we used 50 images from IDB data of blood samples taken with an optical microscope. The suggested method is compared with other modern techniques based on the two accuracy coefficients, namely, detection and false alarm rates. Results show that the proposed method has a high detection accuracy compared to other methods.

Ship route planning is a crucial activity that must consider not only the safety of the ship but also the safe passage of nearby ships in the same space and time. This study aims to provide general route-planning guidance to shipping traffic by improving conventional sampling-based route-planning algorithms in accordance with the maritime environment from a ship operator's perspective. The obstacle safety margin in a marine environment can be reflected in a binary image map space based on an electronic navigational chart. Consequently, an initial route was created using the probabilistic road map (PRM) algorithm in the configured map space to increase the speed of the conventional sampling-based route-planning algorithm. Based on the initial route created, a designated space—that is, a multi-elliptical area—was created to limit the route-search range. After searching the final route in the designated space based on the rapidly-exploring random tree (RRT*) algorithm, optimal route planning could be achieved by generating a collision-free space graph to remove unnecessary nodes from the searched final route. The simulation results showed that the route was shortened by approximately 33.7 km compared with the conventional RRT* algorithm, and the calculation time was shortened by approximately 2.5 times. [ABSTRACT FROM AUTHOR]

A non-additive distortion binary image steganography method was proposed which divided the cover image into two sub cover images, updated the distortion through delivering influence factor, and used the minimizing distortion codes to achieve message embedding.Compared with the previous additive distortion steganography methods with fixed distortion, the proposed method combined the characteristics of binary images with only black and white pixels.Hence, it facilitated the correlation capture of pixel points and dynamic modification of distortion, improving the visual quality and steganography security of the binary image steganography.

A non-additive distortion binary image steganography method was proposed which divided the cover image into two sub cover images, updated the distortion through delivering influence factor, and used the minimizing distortion codes to achieve message embedding. Compared with the previous additive distortion steganography methods with fixed distortion, the proposed method combined the characteristics of binary images with only black and white pixels. Hence, it facilitated the correlation capture of pixel points and dynamic modification of distortion, improving the visual quality and steganography security of the binary image steganography.

Since, in many routine analytical laboratories, a stereomicroscope coupled with a digital camera is not equipped with advanced software enabling automatic detection of features of observed objects, in the present study, a procedure of feature detection using open-source software was proposed and validated. Within the framework of applying microscopic expertise coupled with image analysis, a set of digital images of microplastic (MP) items identified in organs of fish was used to determine shape descriptors (such as length, width, item area, etc.). The edge points required to compute shape characteristics were set manually in digital images acquired by the camera coupled with a binocular, and respective values were computed via the use of built-in MotiConnect software. As an alternative, a new approach consisting of digital image thresholding, binarization, the use of connected-component labeling, and the computation of shape descriptors on a pixel level via using the functions available in an OpenCV library or self-written in C++ was proposed. Overall, 74.4% of the images were suitable for thresholding without any additional pretreatment. A significant correlation was obtained between the shape descriptors computed by the software and computed using the proposed approach. The range of correlation coefficients at a very high level of significance, according to the pair of correlated measures, was higher than 0.69. The length of fibers can be satisfactorily approximated using a value of half the length of the outer perimeter (r higher than 0.75). Compactness and circularity significantly differ for particles and fibers.

Finding the exact position of the rotational quasi-symmetry focus using the Jaccard index for binary images was not considered so far. The focus is the point at which the Jaccard index reaches its maximum as the figure rotates. In this paper, we deal with only order 2 rotational symmetry images. A basic extensive focus search procedure is proposed, and the symmetry function profile is subsequently estimated in the vicinity of the maximum. With the search problem analysis, we proposed a computationally efficient procedure for finding the rotational symmetry function maximum based on the quadratic approximation of the symmetry function. The seed point of the search area is the center of mass of the figure. The proposed procedure correctly identified the true rotational symmetry focus for the test image set while the performance was significantly improved compared to the extensive reference procedure. [ABSTRACT FROM AUTHOR]

• A method of generating phase-only hologram of binary image based on deep learning was proposed. • The global loss training strategy was used to enhance the global learning ability of neural networks. • The stochastic homogenization training strategy was used to improve the learning efficiency of binary image features. Deep learning presents a highly effective for computer-generated holography (CGH). However, existing learning-based CGH algorithms may encounter significant obstacles in generating high-quality holograms for binary images. This study proposes a novel diffraction model-driven neural network, termed load sharing yielding holography (LSY-Holo), which leverages a dual-branch architecture and a global structural similarity index measure (Global_SSIM) loss function to generate phase-only holograms (POHs). LSY-Holo framework facilitates capturing global information, enhancing the network's performance to address the ill-posed inverse problem. To further optimize the model for binary images, the research introduces a custom Binary dataset alongside a stochastic homogenization preprocessing method (SHPM), which mitigates the prevalent issue of excessive noise in reconstructed images. Additionally, this deep learning approach is integrated with an iterative algorithm, which employs holograms rapidly generated by LSY-Holo as the initial phase of the AdamW Holography (AH) algorithm. This strategy significantly accelerates the convergence of the iterative process. LSY-Holo is further extended to 3D scenes, enabling dynamic defocusing and focusing effects at varying depths. Results from numerical simulations and full-color optical reconstruction experiments demonstrate that the proposed method can enhance image quality and effectively reduce the artifacts in reconstructed images. [ABSTRACT FROM AUTHOR]

This paper presents a high-speed skeletonization algorithm for detecting the skeletons of linear objects from their binary images. The primary objective of our research is to achieve rapid extraction of the skeletons from binary images while maintaining accuracy for high-speed cameras. The proposed algorithm uses edge supervision and a branch detector to efficiently search inside the object, avoiding unnecessary computation on irrelevant pixels outside the object. Additionally, our algorithm addresses the challenge of self-intersections in linear objects with a branch detection module, which detects existing intersections and initializes new searches on emerging branches when necessary. Experiments on various binary images, such as numbers, ropes, and iron wires, demonstrated the reliability, accuracy, and efficiency of our approach. We compared the performance of our method with existing skeletonization techniques, showing its superiority in terms of speed, especially for larger image sizes.

In any medical organization, there are large number of medical images are available. To keep the improvement records of the patient, it is necessary to take the medical images in regular basis. It is very much important to store those images for future references as well as a doctor's notes. These large numbers of images are needed to be stored in storage place and also to be transmitted from one storage place to another medical sector. Due to the controlled bandwidth of transmission channel and storage capacity, medical images should be compressed before storage and transmission. The privacy and security of medical images are essential. Compression should be done in such a way so that no one can access the patient data without decryption key. In this work, we have proposed a novel compression algorithm which compress the clinically relevant region of medical image as well as decryption algorithm to reconstruct original image with 100% fidelity, so that minimum space will be taken to store those images sequentially. The suggested technique is quantitatively tested against 25 eye OCT images to assess the vision loss caused by sub-retinal fluid buildup and 89 fundus images of DIARETDB1 database to find vascular structure. When compared to existing approaches in the literature, the created algorithm produces equivalent or even superior compression ratio. [ABSTRACT FROM AUTHOR]

Manual measurements of quantitative traits are time consuming and error prone. Therefore, high‐throughput phenotyping methods that allow a rapid and accurate assessment are vital to a growing range of researchers such as agronomists, breeders, phytopathologists, geneticists, ecologists and biologists.Here, I describe the pliman R package, a collection of functions designed (but not limited) to conduct plant image analysis. The package will help researchers to (a) manipulate, segment and compute image indexes based on Red, Green, Blue, Red‐Edge and Near‐Infrared bands; (b) measure leaf area and shape; (c) quantify plant disease severity; (d) count and extract features (e.g. area, perimeter, radius, circularity and eccentricity) of objects such as grains, leaves, pods, pollen and cells; and (e) compute RGB indexes for each object in an image.In this paper, I describe the main features implemented in the package, guiding the user along a gentle learning curve with practical and reproducible examples. The results of validation studies implemented to count objects in different scenarios (combination of seed sizes, background colour and image resolution), measure leaf area, and quantify plant disease severity have shown that pliman measurements are highly concordant with existing 'gold standard' tools.The pliman package offers a flexible, intuitive and richly documented working environment for image‐based phenotyping, being an interesting alternative to free and commercial 'point‐and‐click' solutions. R users will find the package fairly easy to use and will be surprised at how the setting of a few arguments will allow processing thousands of images while they enjoy a cup of coffee. [ABSTRACT FROM AUTHOR]

Low-permeability sandstone reservoirs play a great role in the exploration and production of oil and gas resources, but most of them undergo a rapid reduction in permeability at the beginning of the production process and exhibit a depleted production rate as time progresses. To discern the rapid reduction mechanism of permeability, an experimental study was performed using low-permeability sandstone specimens. Using a digital image correlation method, a binary image process was used to capture fluid flow passage through microscopic pore structures. Combined with a laser engraving technique, a simplified displacement experiment was conducted, and it was found that the rapid reduction in permeability was caused by loose grain particles, which can be transferred with the fluid and thus block the pore throats. These preliminary findings can provide guidance for improving the production prediction accuracy and understanding of the rapid depleted production rate at the beginning of the production history. [ABSTRACT FROM AUTHOR]

So-called Vertex Chain Codes have been widely used to describe the shape of the objects. From these codes, several describing features can be obtained, e.g., the Euler characteristic. In this research, we show how Vertex Chain Codes can be used to train an Artificial Neural Network to compute the Euler characteristic of a 2-D binary image. We experimentally demonstrate how a simple linear neuron is enough to attain the goal. We present results with sets of 2-D binary images and objects of different complexity and size. [ABSTRACT FROM AUTHOR]

Copyright of Al-Mustansiriyah Journal of Science is the property of Republic of Iraq Ministry of Higher Education & Scientific Research (MOHESR) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper is focused on a description of a new method of detecting continuities in a binary image. The detecting method is called “Binary Large Object” (BLOB). A new algorithm with other elementary parameters, as processing speed and memory capability, are described here. The developed BLOB method with described algorithms is implemented in MATLAB. The simulation of the algorithms is tested in different conditions, with the time dependences determination. The research results of the computing time or the BLOB memory demand during computation are presented as well. The developed BLOB method is usable as one of the elementary image processing methods of the field detection dependencies in the images, like pattern recognition or OCR algorithm.

In the recent decade, chaos‐based image encryption algorithms gained attention due to their pros and cons. The authors suggest one such algorithm of image encryption for different colour space using feed forward initial condition to pursue random permutation by combining 1D logistic map with a series of tent maps. The algorithm adds one more novel step to protect and encrypt binary image by concealing it within a cover grey image using bit‐plane decomposition methods. Sensitivity towards initial condition is shown using block division of image by applying a stream of random initial conditions to each block for encryption along with XOR operation which leads the key space high (≃2484). For better performance analysis, the proposed scheme has been tested with many colour space images including a binary image. The proposed technique is more efficient with respect to time complexity and resistance to vulnerability aspects.

This study detected water bodies which included ponds, lakes, rivers, canals, irrigation land, etc. from Landsat TM (Thematic Mapper) and OLI (Operational Land Imager) data by Pixel Based Model (PBM) for the years 2000 to 2018 and identified the change in area of water body. Rajshahi Division of Bangladesh was selected as the study area for this case study. Landsat 5 data acquired in December 18th, 2000, January 12th, 2001, November 14th, 2005, January 26th, 2006, December 23rd, 2010, and December 14th, 2010 along with Landsat 8 data acquired in December 28th, 2015, January 6th, 2016, December 29th, 2018, and November 18th, 2018 were used as satellite imagery for this analysis. Two images of each month were taken to cover study area. The lowest and highest pixel value of the water body for each band was investigated. Then, a Pixel Based Model (PBM) was formed by using the entire Landsat band and run. The output of the model was shown as a binary image which includes water bodies and non-water bodies. The goal of this study is to verify the accuracy in finding common areas, accreted areas and abolished areas of the water bodies using the PBM model. The results show that overall accuracy of the model is 97% for the year 2000 with Kappa 0.94, 97% for the year 2005 with Kappa 0.94, 99% for the year 2010 with Kappa 0.98, 95% for the year 2015 and 2018 with Kappa 0.90. Also, it was observed that there have been significant changes in the water bodies during the chosen time periods.

The Earth remote sensing technologies (ERS) in exploration largely determine their effectiveness. Therefore, development of a new methodological support for the use of remote sensing data in predicting mining and geological conditions is a key priority area. The studies are based on the analysis of the assessment of information, social, and economic efficiency of remote sensing data application at mineral deposits. The role of application of new technologies (including remote ones) in the process of optimizing initial exploration stages is noted. The possibilities of using remote sensing data to assess general nature, direction, and extent of environmental changes due to mining activities are shown. A technique is proposed that can be used in the process of tracking changes in the topology of objects in the course of mining. The differences in the results obtained using the proposed technique for processing satellite images on rectangular and hexagonal grids (rasters) are considered. The advantages of using the hexagonal grid for tracking the boundaries of objects and formation of signs are shown. Practical examples - a number of open source satellite images processed using the proposed method - are presented. The study findings allow applying intelligent analysis of satellite imagery data with the subsequent identification of the earth's surface objects of interest. An example of using the obtained results together with specialized software tools (such as GIS INTEGRO geographic information system capable of solving geological problems, or the foreign ArcGIS system) for constructing contour maps of the territory and obtaining its description based on topological relations and metric information is shown.