Group Of Pixels Research Articles

An Approach for the Automatic Characterization of Underwater Dunes in Fluviomarine Context

The identification of underwater landforms represents an important role in the study of the seafloor morphology. In this context, the segmentation and characterization of underwater dunes allow a better understanding of the dynamism of the seafloor, since the formation of these structures is directly related to environmental conditions, such as current, tide, grain size, etc. In addition, it helps to ensure safe navigation, especially in the context of navigation channels requiring periodic maintenance. This paper proposes a novel method to automatically characterize the underwater dunes. Its originality relies on the extraction of morphological descriptors not only related to the dune itself, but also to the fields where the dunes are located. Furthermore, the proposed approach involves the entire surface of the dunes, rather than profiles or group of pixels as generally found in previous works. Considering the surface modelled by a digital bathymetric model (DBM), the salient features of the dunes (i.e., crest line, stoss trough, and lee trough) are first identified using a geomorphometric analysis of the DBM. The individual dunes are built by matching the crest lines with their respective troughs according to an object-oriented approach. Then, a series of morphological descriptors, selected through a literature review, are computed by taking advantage of the dune salient features, surface representation, and spatial distribution in the fields where they are located. The validation of the proposed method has been conducted using more than 1200 dunes in the fluvio-marine context of the Northern Traverse of the Saint Lawrence River.

Analysis of Communication Compression and Transmission in a Multimedia and Internet of Things Environment Integrating Scene Elements

In order to improve the effect of multimedia data transmission, this paper integrates scene elements to analyze the needs of multiple types of data transmission and uses the technology of the Internet of Things to analyze the compression and transmission of multimedia data communication. Moreover, this paper proposes a reversible information hiding algorithm for encrypted images based on pixel sorting and grouping prediction and discusses how to improve the prediction accuracy through the histogram of prediction errors. In addition, this paper designs a communication compression transmission system in a multimedia and Internet of Things environment integrating scene elements and builds system function modules and system processes. Finally, this paper verifies the performance of the proposed system by means of simulation experiments. From the experimental results, we can see that the system proposed in this paper has a good multimedia information transmission effect.

An unsupervised approach of colonic polyp segmentation using adaptive markov random fields

• Unsupervised segmentation of polyps in colonoscopic images. • Superpixel level grouping of pixels using simple linear iterative clustering (SLIC). • Deployment of adaptive Markov random field (MRF) to stitch homogeneous regions with region adjacency graph (RAG). • Polyp segmentation efficiency of 60.77% is achieved in terms of dice co-efficient. In this article, an unsupervised image segmentation method is proposed to segment out polyps in endoscopic images. Based on the skeleton of an over-segmented image, an adaptive Markov Random Field (MRF)-based framework is employed. The polyps or abnormal regions show markedly different texture and color characteristics in contrast to normal tissues. In our method, the endoscope images are first over-segmented into superpixels . For final refinement, Local binary pattern (LBP) and color features are used in the adaptive MRF. The experimental results show that the proposed method can perfectly localize polyp regions, and it can give a mean dice value of 60.77% in a test set of 612 images taken from 29 different video sequences. Deep learning models require a huge number of training images for tweaking of network parameters. In this context, it is sometimes challenging to deploy a deep model for unseen medical datasets having many segmentation challenges, like complex background, specularity, non-uniform illuminations, etc. Also, manual labeling of a huge number of images is another challenge. That is why we adopted an unsupervised approach in this paper.

Content-Aware SLIC Super-Pixels for Semi-Dark Images (SLIC++).

Super-pixels represent perceptually similar visual feature vectors of the image. Super-pixels are the meaningful group of pixels of the image, bunched together based on the color and proximity of singular pixel. Computation of super-pixels is highly affected in terms of accuracy if the image has high pixel intensities, i.e., a semi-dark image is observed. For computation of super-pixels, a widely used method is SLIC (Simple Linear Iterative Clustering), due to its simplistic approach. The SLIC is considerably faster than other state-of-the-art methods. However, it lacks in functionality to retain the content-aware information of the image due to constrained underlying clustering technique. Moreover, the efficiency of SLIC on semi-dark images is lower than bright images. We extend the functionality of SLIC to several computational distance measures to identify potential substitutes resulting in regular and accurate image segments. We propose a novel SLIC extension, namely, SLIC++ based on hybrid distance measure to retain content-aware information (lacking in SLIC). This makes SLIC++ more efficient than SLIC. The proposed SLIC++ does not only hold efficiency for normal images but also for semi-dark images. The hybrid content-aware distance measure effectively integrates the Euclidean super-pixel calculation features with Geodesic distance calculations to retain the angular movements of the components present in the visual image exclusively targeting semi-dark images. The proposed method is quantitively and qualitatively analyzed using the Berkeley dataset. We not only visually illustrate the benchmarking results, but also report on the associated accuracies against the ground-truth image segments in terms of boundary precision. SLIC++ attains high accuracy and creates content-aware super-pixels even if the images are semi-dark in nature. Our findings show that SLIC++ achieves precision of 39.7%, outperforming the precision of SLIC by a substantial margin of up to 8.1%.

Novel Algorithm for Radon Real-Time Measurements with a Pixelated Detector.

Nowadays, radon gas exposure is considered one of the main health concerns for the population because, by carrying about half the total dose due to environmental radioactivity, it is the second cause of lung cancer after smoking. Due to a relatively long half-life of 3.82 days, the chemical inertia and since its parent Ra-226 is largely diffuse on the earth’s crust and especially in the building materials, radon can diffuse and potentially saturate human habitats, with a concentration that can suddenly change during the 24 h day depending on temperature, pressure, and relative humidity. For such reasons, ‘real-time’ measurements performed by an active detector, possibly of small dimensions and a handy configuration, can play an important role in evaluating the risk and taking the appropriate countermeasures to mitigate it. In this work, a novel algorithm for pattern recognition was developed to exploit the potentialities of silicon active detectors with a pixel matrix structure to measure radon through the α emission, in a simple measurement configuration, where the device is placed directly in air with no holder, no collection filter or electrostatic field to drift the radon progenies towards the detector active area. This particular measurement configuration (dubbed as bare) requires an α/β-discrimination method that is not based on spectroscopic analysis: as the gas surrounds the detector the α particles are emitted at different distances from it, so they lose variable energy amount in air depending on the traveled path-length which implies a variable deposited energy in the active area. The pixels matrix structure allows overcoming this issue because the interaction of α, β and γ particles generate in the active area of the detector clusters (group of pixels where a signal is read) of different shape and energy dispersion. The novel algorithm that exploits such a phenomenon was developed using a pixelated silicon detector of the TimePix family with a compact design. An α (Am-241) and a β (Sr-90) source were used to calibrate the algorithm and to evaluate its performances in terms of β rejection capability and α recognition efficiency. Successively, the detector was exposed to different radon concentrations at the ENEA-INMRI radon facility in ‘bare’ configuration, in order to check the linearity of the device response over a radon concentration range. The results for this technique are presented and discussed, highlighting the potential applications especially the possibility to exploit small and handy detectors to perform radon active measurements in the simplest configuration.

Change Target Extraction Based on Scale-Adaptive Difference Image and Morphology Filter for KOMPSAT-5

Multitemporal synthetic aperture radar (SAR) images have been widely used for change detection and monitoring of the environment owing to their competency under all weather conditions. However, owing to speckle backgrounds and strong reflections, change detection in urban areas is challenging. In this study, to automatically extract changed objects, we developed a model that integrated change detection and object extraction in multiple Korean Multi-Purpose Satellite-5 (KOMPSAT-5) images. Initially, two arbitrary L1A-level SAR images were input into the proposed model, and after pre-processing, such as radio calibration and coordinate system processing, change detection was performed. Subsequently, the desired targets were automatically extracted from the change detection results. Finally, the model obtained images of the extraction targets and metadata, such as date and location. Noise was removed by applying scale-adaptive modification to the generated difference image during the change detection process, and the detection accuracy was improved by emphasizing the occurrence of the change. After polygonizing the pixel groups of the change detection map in the target extraction process, the morphology-based object filtering technique was applied to minimize the false detection rate. As a result of the proposed approach, the changed objects in the KOMPSAT-5 images were automatically extracted with 90% accuracy.

Hybrid Segmentation Approach for Different Medical Image Modalities

The segmentation process requires separating the image region into sub-regions of similar properties. Each sub-region has a group of pixels having the same characteristics, such as texture or intensity. This paper suggests an efficient hybrid segmentation approach for different medical image modalities based on particle swarm optimization (PSO) and improved fast fuzzy C-means clustering (IFFCM) algorithms. An extensive comparative study on different medical images is presented between the proposed approach and other different previous segmentation techniques. The existing medical image segmentation techniques incorporate clustering, thresholding, graph-based, edge-based, active contour, region-based, and watershed algorithms. This paper extensively analyzes and summarizes the comparative investigation of these techniques. Finally, a prediction of the improvement involves the combination of these techniques is suggested. The obtained results demonstrate that the proposed hybrid medical image segmentation approach provides superior outcomes in terms of the examined evaluation metrics compared to the preceding segmentation techniques.

ИНФОРМАЦИОННЫЙ ОБРАЗ РАСТРОВОГО ИЗОБРАЖЕНИЯ ШЕРОХОВАТОЙ ПОВЕРХНОСТИ ДЕТАЛЕЙ МАШИНОСТРОЕНИЯ

The article proposes a method for parametrizing an image of a rough surface using its information image, which was formed on the basis of solving the problem of orthogonal packing of squares into a square. I found that the information image allows you to track any changes in the brightness of pixels in the image of a controlled rough surface. The method allows you to visually fix the loss or change in the brightness of a group of pixels in the image of a rough surface. In this case, a group of pixels can be less than 0.2% of the analyzed image. To form an information image of a monochrome image, its normalized histogram was used. Each value of the histogram was considered as the area of squares, which are placed orthogonally and without intersections in a square with an area of 2. Such packing of squares into a square was considered as an information image of the image. For the numerical analysis of the information image, a parameter was used that made it possible to track any changes in the number and brightness of pixels in the image of a rough surface.

A Multilayer Steganographic Method Using Improved Exploiting Modification Directions Scheme

The exploiting modification direction scheme is a well-known irreversible steganographic method because of its high embedding efficiency and imperceptibility. The shortcomings of the exploiting modification direction scheme are the use of a (<inline-formula> <tex-math notation="LaTeX">$2n +1$ </tex-math></inline-formula>)-ary notational system secret digit and low payload. Some studies transform secret data into a sequence of secret digits in the 8-ary notational system to overcome the disadvantage of using a non-binary secret data problem, which also increases the payload. Additionally, some studies have proposed a two-layer embedding method to enhance data security, which also increases the payload. Besides, most of the EMD-based scheme has the fall of boundary problems when the pixel group is located in the boundary area, which increase the distortion of the stego-image. In this study, we proposed a multilayer steganographic method using an improved exploiting modification direction scheme, which includes the abovementioned advantages, using binary secret data, increasing the payload, enhance data security and preventing the fall of boundary problem. In addition, we demostrate a three-layer EMD-based data hiding example. The experimental results show that the proposed method achieves a high payload (approximately 4.5 bpp) and the quality of the stego-image satisfying the human vision system sensitivity (PSNR value is greater than 30 dB).

Data reduction strategies at a time-of-flight NSE for a lamellar microemulsion

Neutron spin echo (NSE) spectroscopy provides the ultimate energy resolution in quasi-elastic thermal and cold neutron scattering spectroscopy. A peculiarity of the SNS-NSE, the only NSE spectrometer at a pulsed beam port at the moment, is that the wavelength spread δλ/λ can be chosen during evaluation with an appropriate time channel binning. The Q-resolution can be adjusted in certain limits a posteriori by choosing the appropriate detector binning (as on a continuous source) and time channel binning. This can be exploited for samples with a strongly varying scattering function S (Q, t), e.g. due to Bragg peaks in a crystal or lamellar ordering in microemulsions. The data reduction software DrSpine allows for appropriate slicing and masking for this task. In this contribution the correlation function of microemulsions, thermodynamically stable mixtures of oil, water and surfactant, is measured with NSE on length scales where structural correlations are important, and data reduction strategies varying the Q-resolution by pixel and time channel grouping are discussed. The typical ”de Gennes narrowing” or structural narrowing is observed with a relaxation time proportional to I(Q). In these regions of strongly varying intensity it is shown that a too coarse grouping has an influence on the data reduction, with a broadened in Q of the apparent slowing down.

Adaptive Fuzzy Learning Superpixel Representation for PolSAR Image Classification

The increasing applications of polarimetric synthetic aperture radar (PolSAR) image classification demand for effective superpixels’ algorithms. Fuzzy superpixels’ algorithms reduce the misclassification rate by dividing pixels into superpixels, which are groups of pixels of homogenous appearance and undetermined pixels. However, two key issues remain to be addressed in designing a fuzzy superpixel algorithm for PolSAR image classification. First, the polarimetric scattering information, which is unique in PolSAR images, is not effectively used. Such information can be utilized to generate superpixels more suitable for PolSAR images. Second, the ratio of undetermined pixels is fixed for each image in the existing techniques, ignoring the fact that the difficulty of classifying different objects varies in an image. To address these two issues, we propose a polarimetric scattering information-based adaptive fuzzy superpixel (AFS) algorithm for PolSAR images classification. In AFS, the correlation between pixels’ polarimetric scattering information, for the first time, is considered through fuzzy rough set theory to generate superpixels. This correlation is further used to dynamically and adaptively update the ratio of undetermined pixels. AFS is evaluated extensively against different evaluation metrics and compared with the state-of-the-art superpixels’ algorithms on three PolSAR images. The experimental results demonstrate the superiority of AFS on PolSAR image classification problems.

SD-UNet: A Novel Segmentation Framework for CT Images of Lung Infections

Due to the outbreak of lung infections caused by the coronavirus disease (COVID-19), humans have to face an unprecedented and devastating global health crisis. Since chest computed tomography (CT) images of COVID-19 patients contain abundant pathological features closely related to this disease, rapid detection and diagnosis based on CT images is of great significance for the treatment of patients and blocking the spread of the disease. In particular, the segmentation of the COVID-19 CT lung-infected area can quantify and evaluate the severity of the disease. However, due to the blurred boundaries and low contrast between the infected and the non-infected areas in COVID-19 CT images, the manual segmentation of the COVID-19 lesion is laborious and places high demands on the operator. Quick and accurate segmentation of COVID-19 lesions from CT images based on deep learning has drawn increasing attention. To effectively improve the segmentation effect of COVID-19 lung infection, a modified UNet network that combines the squeeze-and-attention (SA) and dense atrous spatial pyramid pooling (Dense ASPP) modules) (SD-UNet) is proposed, fusing global context and multi-scale information. Specifically, the SA module is introduced to strengthen the attention of pixel grouping and fully exploit the global context information, allowing the network to better mine the differences and connections between pixels. The Dense ASPP module is utilized to capture multi-scale information of COVID-19 lesions. Moreover, to eliminate the interference of background noise outside the lungs and highlight the texture features of the lung lesion area, we extract in advance the lung area from the CT images in the pre-processing stage. Finally, we evaluate our method using the binary-class and multi-class COVID-19 lung infection segmentation datasets. The experimental results show that the metrics of Sensitivity, Dice Similarity Coefficient, Accuracy, Specificity, and Jaccard Similarity are 0.8988 (0.6169), 0.8696 (0.5936), 0.9906 (0.9821), 0.9932 (0.9907), and 0.7702 (0.4788), respectively, for the binary-class (multi-class) segmentation task in the proposed SD-UNet. The result of the COVID-19 lung infection area segmented by SD-UNet is closer to the ground truth compared to several existing models such as CE-Net, DeepLab v3+, UNet++, and other models, which further proves that a more accurate segmentation effect can be achieved by our method. It has the potential to assist doctors in making more accurate and rapid diagnosis and quantitative assessment of COVID-19.

Clustering Feature Constraint Multiscale Attention Network for Shadow Extraction From Remote Sensing Images

Shadow extraction is an important and challenging task in remote sensing image analysis because the presence of shadows not only reduces radiation information but also affects the interpretation of remote sensing images. In this article, a clustering feature constraint multiscale attention network for shadow extraction from remote sensing images is proposed. First, in addition to the pixel-level description of the traditional neural network, our method focuses on the clustering relationships between pixel pairs to obtain the pixel group features of shadows. The feature extraction capability of the network is improved with a reweighting mechanism at the pixel level and pixel group features. Second, we employ a feature fusion algorithm by considering contextual information to improve the network’s attention toward shadow areas and enhance the nonlinear expression ability during the encoding and decoding layers. Furthermore, considering the most prominent multiscale features of shadows in remote sensing images, a deep multiscale feature aggregation structure is established to better fit the multiscale feature expression of shadows. Finally, we construct a shadow extraction dataset to verify the proposed approach. We compare our method with the results of state-of-the-art deep learning models. The results show that the intersection over union (IOU) of our method is improved by 0.85%–9.51% and that the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$F1$ </tex-math></inline-formula> -score is improved by 0.73–6.48. In addition, the test results for images with different resolutions prove that the proposed approach is more robust than the other methods.

Matlab Software for Supervised Habitat Mapping of Freshwater Systems Using Image Processing

We present a software package for the supervised classification of images useful for cover-type mapping of freshwater habitat (e.g., water surface, gravel bars, vegetation). The software allows the user to select a representative subset of pixels within a specific area of interest in the image that the user has identified as a cover-type habitat of interest. We developed a graphical user interface (GUI) that allows the user to select single pixels using a dot, line, or group of pixels within a defined polygon that appears to the user to have a spectral similarity. Histogram plots for each band of the selected ground-truth subset aid the user in determining whether to accept or reject it as input data for the classification processes. A statistical model, or classifier, is then built using this pixel subset to assign every pixel in the image to a best-fit group based on reflectance or spectral similarity. Ideally, a classifier incorporates both spectral and spatial information. In our software, we implement quadratic discriminant analysis (QDA) for spectral classification and choose three spatial methods—mode filtering, probability label relaxation, and Markov random fields—to incorporate spatial context after computation of the spectral type. This multi-step interactive process makes the software quantitatively robust, broadly applicable, and easily usable for cover-type mapping of rivers, their floodplains, wetlands often components of these functionally linked freshwater systems. Indeed, this supervised classification approach is helpful for a wide range of cover-type mapping applications in freshwater systems but also estuarine and coastal systems as well. However, it can also aid many other applications, specifically for automatic and quantitative extraction of pixels that represent the water surface area of rivers and floodplains.

Экспериментальное обоснование ряда требований к аппаратным и методическим средствам неконтактной фотоплетизмографии на основе анализа видеоизображений

Experimental assessment of the possibility of using a scientific video camera for realization of non-contact photoplethysmography is carried out. In view of the wide spread of digital cameras in endoscopic units photoplethysmography based on video-image analysis is an inexpensive and promising method for solving problems of medical diagnostics. A number of requirements to the camera parameters ensuring the specified level of the registered signals coded by RGB values, to the external illumination and video image postprocessing algorithms was substantiated. It was found that at signal levels on the green channel of not less than 130 and not more than 220 a. u. (for a camera with 8-bit color coding depth), the highest level of useful pulse signal is provided. It was shown that any source of light of equivalent color temperature in the range of 3500--6500 K as well as green LEDs can be used as a light source to obtain a high-quality signal of non-contact photoplethysmography. It is demonstrated that the signal has higher signal/noise ratio when the averaging area is larger than 40 × 40 pixels than when averaging over a smaller group of pixels. The obtained results can be used for the implementation of non-contact photoplethysmography to study local blood flow based on video image analysis

Fusion of Deep Features With Superpixel Based Local Handcrafted Features for Surface Condition Assessment of Metal Oxide Surge Arrester Using Infrared Thermal Images

This letter presents an innovative approach to sense surface contamination severity of in-service metal oxide surge arrester through combining handcrafted and deep learning-based features. For this purpose, different color regions in the captured infrared thermal (IRT) images are isolated to generate superpixel regions through grouping of informative and meaningful pixels. Thereafter, handcrafted color features are extracted from each superpixel region. Additionally, the IRT images are analyzed through pretrained “ResNet50” deep architecture, and deep features are extracted. Thereafter, handcrafted color features and deep features are combined and fed to a classifier for classification of different surface contamination severity. The findings of this letter indicate that the proposed fusion strategy strongly boosts the classification performance compared to each stand-alone approach. Therefore, proposed framework with feature fusion strategy can be implemented for surface condition assessment of metal oxide surge arrester is service.

Evaluation of A Micro CT Scan System Based on Digital Image Correlation Profile of Multiple Radiographs

X-ray computed tomography system is a non-destructive testing tool which can visualize the internal structure of an object. However, the reconstruction image result can be disturbed by artefacts. The center of rotation position is a factor that needs to be controlled to avoid artefact. That is why a method to evaluate the performance of CT system is needed. The method is developed using x-ray micro-CT system and evaluated based on Digital Image Correlation. DIC measurement is based on tracking correlation function of a group of pixel which have a unique grey value from the test images and a reference image. The DIC value of multiple radiographs and the reference radiograph are calculated and then plotted by the angle of rotation to get the DIC profile value. The multiple radiographs are obtained from scanning object by simulation and experiment using CT Simulator software and x-ray Micro-CT system at Fisika Atom dan Inti FMIPA UGM laboratory. The results show that the ideal DIC profile value of radiograph image from simulation is a constant straight line for every size variation of the object. However, from the experiment, the result show deviation toward –y for every size variation of the object that indicate the shift of COR position in micro-CT system. The result also similar using center of mass profile value as comparison.

Doppler Ultrasound under Image Denoising Algorithm in the Diagnosis and Treatment of Fetal Growth Restriction Using Aspirin Combined with Low-Molecular-Weight Heparin.

Objective This study explored the clinical application value of image denoising algorithm combined with Doppler ultrasound imaging in evaluation of aspirin combined with low-molecular-weight heparin (LMWH) on fetal growth restriction (FGR). Method A two-stage image denoising by principal component analysis (PCA) with local pixel grouping (LPG-PCA) denoising algorithm was constructed in this study. Eighty FGR pregnant women were included in the study, and they were rolled into an experimental group (aspirin enteric-coated tablets + LMWH calcium injection) and a control group (LMWH calcium injection) according to the different treatment plans, with 40 cases in each group. All patients were performed with Doppler ultrasound imaging. The blood flow parameters (BFPs) were recorded and compared before and after the treatment in two groups, including power index (PI), resistance index (RI), high systolic blood flow velocity (S), high diastolic blood flow velocity (D), S/D value, and peak systolic velocity (PSV). In addition, the middle cerebral artery (MCA) BFPs, cerebral placental rate (CPR), amniotic fluid index (AFI) and perinatal outcome (PO) of the two groups were compared. Results The total effective rate of treatment in group A (87.5%) was greatly higher than that in group B (62.5%), showing statistical difference (P < 0.05). The PI (0.72 ± 0.19), RI (0.57 ± 0.17), and S/D values (2.26 ± 0.43) in group A were dramatically lower than those in group B, which were 0.92 ± 0.21, 0.75 ± 0.14, and 2.64 ± 0.45, respectively (P < 0.05), and the AFI was higher (13.71 ± 2.2 cm vs 11.38 ± 2.16 cm) (P < 0.05). The Apgar score (9.17 ± 0.26), weight (3.57 ± 1.08), and gestational age (38.85 ± 2.50) of group A were all higher in contrast to those of group B, which were 7.33 ± 0.25, 2.61 ± 1.13, and 36.18 ± 2.25, respectively (P < 0.05). In addition, the fetal double parietal diameter (2.4 ± 0.9 mm), femur diameter (2.2 ± 0.6 mm), head circumference (1.2 ± 0.4 mm), abdominal circumference (1.3 ± 0.7 mm), and uterine height (0.8 ± 0.3 mm) in group A were obviously superior to those in group B, which were 1.8 ± 0.4 mm, 1.7 ± 0.5 mm, 0.8 ± 0.2 mm, 0.9 ± 0.4 mm, and 0.4 ± 0.6 mm, respectively, showing statistically observable differences (P < 0.05). Conclusion Doppler ultrasound based on image denoising algorithm can accurately evaluate the effect of aspirin combined with LMWH on the improvement of FGR and showed good application value.

Evaluating the Ability to Use Contextual Features Derived from Multi-Scale Satellite Imagery to Map Spatial Patterns of Urban Attributes and Population Distributions

With an increasing global population, accurate and timely population counts are essential for urban planning and disaster management. Previous research using contextual features, using mainly very-high-spatial-resolution imagery (&lt;2 m spatial resolution) at subnational to city scales, has found strong correlations with population and poverty. Contextual features can be defined as the statistical quantification of edge patterns, pixel groups, gaps, textures, and the raw spectral signatures calculated over groups of pixels or neighborhoods. While they correlated with population and poverty, which components of the human-modified landscape were captured by the contextual features have not been investigated. Additionally, previous research has focused on more costly, less frequently acquired very-high-spatial-resolution imagery. Therefore, contextual features from both very-high-spatial-resolution imagery and lower-spatial-resolution Sentinel-2 (10 m pixels) imagery in Sri Lanka, Belize, and Accra, Ghana were calculated, and those outputs were correlated with OpenStreetMap building and road metrics. These relationships were compared to determine what components of the human-modified landscape the features capture, and how spatial resolution and location impact the predictive power of these relationships. The results suggest that contextual features can map urban attributes well, with out-of-sample R2 values up to 93%. Moreover, the degradation of spatial resolution did not significantly reduce the results, and for some urban attributes, the results actually improved. Based on these results, the ability of the lower resolution Sentinel-2 data to predict the population density of the smallest census units available was then assessed. The findings indicate that Sentinel-2 contextual features explained up to 84% of the out-of-sample variation for population density.

Steganalysis of the methods of concealing information in graphic containers

Aim. Today, there is a pressing matter of protection against steganography-based attacks against information systems. These attacks present a danger as they use the most common data files – especially graphics files – as containers that deliver malicious code to a system or cause a leak of sensitive information. Developing methods of detecting such hidden information is the responsibility of a special subsection of steganography, the steganalysis. Such methods should be extensively used in computer forensics as part of security incident investigation, as well as in automated security systems with integrated modules for analyzing data files for malicious or dangerous information. An important feature of such activities is the need to examine a wide variety of elements and containing files. In particular, it is required to verify not only the colour values of the pixels in images, but their frequency characteristics as well. This raises a number of important questions associated with the best practices of applying steganalysis algorithms and making correct conclusions based on the outputs. The paper aims to briefly analyse the most important and relevant methods of steganalysis, both spatial and frequency, as well as to make conclusions regarding their performance and ways to analyse the outputs based on the test results of the software that implements such methods. Methods. The steganalysis of concealment within the least significant bits of an image’s pixels uses Pearson’s Chi-square statistical analysis, as well as the Regular-Singular method that involves signature analysis of pixel groups and analytical geometry tools for estimating the relative volume of the hidden message. The Koch-Zhao method of steganalysis is used for the purpose of detecting information embedded in the frequency-domain image representation. It also allows identifying the parameters required for extracting the hidden message. Results. A software suite was created that includes the software implementations of the analysed methods. The suite was submitted to a number of tests in order to evaluate the outputs of the examined methods. For the purpose of testing, a sample of images of various formats was compiled, in which information was embedded using a number of methods. Based on the results of the sample file analysis, conclusions were made regarding the efficiency of the analysed methods and interpretation of the outputs. Conclusion. Based on the test results, conclusions were made on the accuracy of the steganalysis methods in cases of varied size of the embedded message and methods of its concealment. The patterns identified with the help of the analysis outputs allowed defining a number of rules for translating the outputs into conclusions on the identification of the fact of detection of hidden information and estimation of its size.