Shadow Features Research Articles

SwinShadow: Shifted Window for Ambiguous Adjacent Shadow Detection

Shadow detection is a fundamental and challenging task in many computer vision applications. Intuitively, most shadows come from the occlusion of light by the object itself, resulting in the object and its shadow being contiguous (referred to as the adjacent shadow in this paper). In this case, when the color of the object is similar to that of the shadow, existing methods struggle to achieve accurate detection. To address this problem, we present SwinShadow, a transformer-based architecture that fully utilizes the powerful shifted window mechanism for detecting adjacent shadows. The mechanism operates in two steps. Initially, it applies local self-attention within a single window, enabling the network to focus on local details. Subsequently, it shifts the attention windows to facilitate inter-window attention, enabling the capture of a broader range of adjacent information. These combined steps significantly improve the network's capacity to distinguish shadows from nearby objects. And the whole process can be divided into three parts: encoder, decoder, and feature integration. During encoding, we adopt Swin Transformer to acquire hierarchical features. Then during decoding, for shallow layers, we propose a deep supervision (DS) module to suppress the false positives and boost the representation capability of shadow features for subsequent processing, while for deep layers, we leverage a double attention (DA) module to integrate local and shifted window in one stage to achieve a larger receptive field and enhance the continuity of information. Ultimately, a new multi-level aggregation (MLA) mechanism is applied to fuse the decoded features for mask prediction. Extensive experiments on three shadow detection benchmark datasets, SBU, UCF, and ISTD, demonstrate that our network achieves good performance in terms of balance error rate (BER). The source code and results are now publicly available at https://github.com/harrytea/SwinShadow .

FuDNN-FOSMO: Early detection of chronic kidney disease using FuDNN with fractional order sequence optimization algorithm classifier

Early diagnosis and prediction of chronic kidney disease (CKD) progress within a given duration are critical to ensure personalized treatment, which could improve patients’ quality of life and prolong survival time. In this paper, we employ experiential analysis of ML techniques for classifying the kidney patient dataset as CKD or NOTCKD. Feature selection is a crucial step in ML, as it helps extract a subset of important features from the dataset. This process offers several benefits, including improved prediction accuracy, reduced model complexity, and enhanced interpretability. In this study, we utilized the Tabu Search with Boruta Algorithm feature selection technique, which leverages random shadow features and an ML model. Boruta compares the importance of each feature to that of the shadow features iteratively, categorizing features as confirmed, tentative, or rejected based on their significance. Ultimately, Boruta provides a subset of the most significant features from the dataset. Feature selection is performed using the Tabu Search with Boruta algorithm, and the model is developed using ML algorithms. The proposed model was validated on the UCI CKD dataset, achieving outstanding performance with 99.75 % accuracy. This study introduces a novel Deep learning network based on PointRCNN (FuDNN) Architecture for CKD early detection and prediction. More processing attributes of characteristics chosen to indicate a kidney issue are extracted by PointRCNN. Accuracy, sensitivity, specificity, AUC score and F1 score are the performance metrics for the suggested CKD classification approach. Additional experimental findings demonstrate that the suggested method produces a better categorization of CKD than the present state-of-the-art method.

Synthetic Document Images with Diverse Shadows for Deep Shadow Removal Networks.

Shadow removal for document images is an essential task for digitized document applications. Recent shadow removal models have been trained on pairs of shadow images and shadow-free images. However, obtaining a large, diverse dataset for document shadow removal takes time and effort. Thus, only small real datasets are available. Graphic renderers have been used to synthesize shadows to create relatively large datasets. However, the limited number of unique documents and the limited lighting environments adversely affect the network performance. This paper presents a large-scale, diverse dataset called the Synthetic Document with Diverse Shadows (SynDocDS) dataset. The SynDocDS comprises rendered images with diverse shadows augmented by a physics-based illumination model, which can be utilized to obtain a more robust and high-performance deep shadow removal network. In this paper, we further propose a Dual Shadow Fusion Network (DSFN). Unlike natural images, document images often have constant background colors requiring a high understanding of global color features for training a deep shadow removal network. The DSFN has a high global color comprehension and understanding of shadow regions and merges shadow attentions and features efficiently. We conduct experiments on three publicly available datasets, the OSR, Kligler's, and Jung's datasets, to validate our proposed method's effectiveness. In comparison to training on existing synthetic datasets, our model training on the SynDocDS dataset achieves an enhancement in the PSNR and SSIM, increasing them from 23.00 dB to 25.70 dB and 0.959 to 0.971 on average. In addition, the experiments demonstrated that our DSFN clearly outperformed other networks across multiple metrics, including the PSNR, the SSIM, and its impact on OCR performance.

A shadow study for a static dyonic black hole with a global monopole surrounded by perfect fluid

In this paper, we mainly study the shadow and observable features of a dyonic black hole (DBH) with a global monopole surrounded by perfect fluid under various profiles of accretions. We investigate the influence of model parameters on the observational signatures and space-time structure of black holes (BHs). We observe that the value of bps\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$b_{ps}$$\\end{document} increases with the increasing values of α\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\alpha $$\\end{document} and η\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\eta $$\\end{document}, while it decreases with the increasing values of the electromagnetic parameter. We found that the corresponding regions and the observed specific intensities of the direct, lensing and photon ring emissions dramatically changed according to the variation of BH state parameters. It is noticed that the total observed flux is dominated by the direct emission and the lensing ring makes a small contribution while the photon ring makes a negligible contribution due to its exponential narrowness, even the photon ring intersects the thin plane more than three times to pick up a larger intensity. Assuming the spherically symmetric accretion disk, it is found that due to the Doppler effect of infalling motion, shadows of infalling accretion are found to be darker as compared to static ones. However, the size and position of the DBH shadows do not change in both cases, implying that the BH’s shadow size depends on the geometric space-time and the shadow’s luminosities rely on the accretion flow models. Finally, the discussion is done analytically and numerically, and ray-tracing methods are adopted to obtain the proper visualizations.

Visible light sensing based on shadow features using multi-scale region convolutional neural network.

There are various production items in the industrial internet of things (IIoT) environment, such as pedestrians, robots, automated automated guided vehicles, etc. The practice industrial environment requires simultaneous communication and sensing of production items to achieve intelligent production and control. Thus, sensing methods not only require the integration of communication but also achieve sensing tasks such as recognition and positioning. Compared with traditional sensing media, visible light sensing has the advantages of high-speed communication, high sensing accuracy, and security, low energy consumption, and has become a potential sensing technology. Based on the strong directivity of visible light spatial radiation and the consistency of light intensity and position, this paper proposes a multi-scale visible light sensing-region convolutional neural network (VLS-RCNN) framework based on shadow features for multiple target sensing. The framework enables the recognition and positioning to use shared visible light shadow features to assist each other, and the multi-scale compensation strategy of the shadow region makes the framework more robust. The simulation results show that positioning results in the sensing area improve the recognition accuracy. The recognition results also reduce the positioning error without additional overhead. Therefore, this paper provides a new perspective for the sensing technology in the future IIoT, which should be considered to sense objects of interest by utilizing the inherent characteristics of visible light.

Shadow-Based False Target Identification for SAR Images

In radar electronic countermeasures, as the difference between jamming and targets continues to decrease, traditional methods that are implemented based on classical features are currently unable to meet the requirements of jamming detection. Compared with classical features such as texture, scale, and shape, shadow has better discernability and separability. In this paper, target shadow is investigated and applied to detect jamming in Synthetic Aperture Radar (SAR) images, and a SAR false target identification method based on shadow features is proposed. First, a difference image is generated by change detection, which can extract the shadow region in single-time SAR images. Then, a three-step differentiation condition is proposed, which can distinguish false targets from real targets. Simulated experimental results show that the proposed method can effectively extract the shadow region in SAR images and accurately distinguishreal and false targets. Furthermore, the potential of shadow in SAR image interpretation and electronic countermeasures is also demonstrated.

Progressive Recurrent Network for shadow removal

Single-image shadow removal is a significant task that is still unresolved. Most existing deep learning-based approaches attempt to remove the shadow directly, which cannot deal with the shadow well. To handle this issue, we consider removing the shadow in a coarse-to-fine fashion and propose a simple but effective Progressive Recurrent Network (PRNet). The network aims to remove the shadow progressively, enabling us to flexibly adjust the number of iterations to strike a balance between performance and time. Our network comprises two parts: shadow feature extraction and progressive shadow removal. Specifically, the first part is a shallow ResNet which constructs the representations of the input shadow image on its original size, preventing the loss of high-frequency details caused by the downsampling operation. The second part has two critical components: the re-integration module and the update module. The proposed re-integration module can fully use the outputs of the previous iteration, providing input for the update module for further shadow removal. In this way, the proposed PRNet makes the whole process more concise and only uses 29% network parameters than the best published method. Extensive experiments on the three benchmarks, ISTD, ISTD+, and SRD, demonstrate that our method can effectively remove shadows and achieve superior performance.

Multi-Scale Influence Analysis of Urban Shadow and Spatial Form Features on Urban Thermal Environment

In urban thermal environment research (UTE), urban shadows formed by buildings and trees contribute to significant variations in thermal conditions, particularly during the mid-day period. This study investigated the multi-scale effects of indicators, including urban shadows, on UTE, focusing specifically on the mid-day hours. It integrated field temperature measurements and drone aerial data from multiple city blocks. Considering both urban shadows and direct solar radiation, a linear mixed-effects model was employed to study the multi-scale effects of urban morphological indicators. Results showed that: (1) UTE is a multi-scale, multi-factor phenomenon, with thermal effects manifesting at specific scales. Under shadow conditions, smaller scales (10–20 m) of landscape heterogeneity and larger scales (300–400 m) of landscape consistency better explained temperature variations mid-day. Conversely, under direct sunlight, temperature was primarily influenced by larger scales (150–300 m). (2) Trees significantly reduced temperature; 100% tree canopy cover within a 10-m radius reduced air temperatures by approximately 2 °C mid-day. However, there is no significant correlation between temperature and green spaces. (3) Building area and height were significantly correlated with temperature. Specifically, an increase in building area beyond 150 m, especially within a 300-m radius, leads to higher temperatures. Conversely, building height within a 10–20 m range exhibits significant cooling effects. These findings provide crucial reference data for micro-scale UTE investigations during mid-day hours and offer new strategies for urban planning and design.

A GAN-Based Augmentation Scheme for SAR Deceptive Jamming Templates with Shadows

To realize fast and effective synthetic aperture radar (SAR) deception jamming, a high-quality SAR deception jamming template library can be generated by performing sample augmentation on SAR deception jamming templates. However, the current sample augmentation schemes of SAR deception jamming templates face certain problems. First, the authenticity of the templates is low due to the lack of speckle noise. Second, the generated templates have a low similarity to the target and shadow areas of the input templates. To solve these problems, this study proposed a sample augmentation scheme based on generative adversarial networks, which can generate a high-quality library of SAR deception jamming templates with shadows. The proposed scheme solved the two aforementioned problems from the following aspects. First, the influence of the speckle noise was considered in the network to avoid the problem of reduced authenticity in the generated images. Second, a channel attention mechanism module was used to improve the network’s learning ability of the shadow features, which improved the similarity between the generated template and the shadow area in the input template. Finally, the single generative adversarial network (SinGAN) scheme, which is a generative adversarial network capable of image sample augmentation for a single SAR image, and the proposed scheme were compared regarding the equivalent number of looks and the structural similarity between the target and shadow in the sample augmentation results. The comparison results demonstrated that, compared to the templates generated by the SinGAN scheme, those generated by the proposed scheme had targets and shadow features similar to those of the original image and could incorporate speckle noise characteristics, resulting in a higher authenticity, which helps to achieve fast and effective SAR deception jamming.

Integrating Target and Shadow Features for SAR Target Recognition.

Synthetic aperture radar (SAR) sensor often produces a shadow in pairs with the target due to its slant-viewing imaging. As a result, shadows in SAR images can provide critical discriminative features for classifiers, such as target contours and relative positions. However, shadows possess unique properties that differ from targets, such as low intensity and sensitivity to depression angles, making it challenging to extract depth features from shadows directly using convolutional neural networks (CNN). In this paper, we propose a new SAR image-classification framework to utilize target and shadow information comprehensively. First, we design a SAR image segmentation method to extract target regions and shadow masks. Second, based on SAR projection geometry, we propose a data-augmentation method to compensate for the geometric distortion of shadows due to differences in depression angles. Finally, we introduce a feature-enhancement module (FEM) based on depthwise separable convolution (DSC) and convolutional block attention module (CBAM), enabling deep networks to fuse target and shadow features adaptively. The experimental results on the Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset show that when only using target and shadow information, the published deep-learning models can still achieve state-of-the-art performance after embedding the FEM.

Shadow Geometry of Kerr Naked Singularities

Direct imaging of supermassive black holes (SMBHs) at event horizon-scale resolutions, as recently done by the Event Horizon Telescope, allows for testing alternative models to SMBHs such as Kerr naked singularities (KNSs). We demonstrate that the KNS shadow can be closed, open, or vanishing, depending on the spins and observational inclination angles. We study the critical parameters where the KNS shadow opens a gap, a distinctive phenomenon that does not happen with the black hole shadow. We show that the KNS shadow can only be closed for dimensionless spin a ≲ 1.18 and vanishing for a ≳ 1.18 for certain ranges of inclination angles. We further analyze the effective angular momentum of photon orbits to demonstrate the fundamental connections between light geodesics and the KNS shadow geometry. We also perform numerical general relativistic ray-tracing calculations, which reproduce the analytical topological change in the KNS shadow, and illustrate other observational features within the shadow due to the lack of an event horizon. By comparing the geometric features of the KNS shadow with black hole shadow observations, the topological change in the shadow of KNSs can be used to test the cosmic censorship hypothesis and KNSs as alternative models to SMBHs.

Balanced Cloud Shadow Compensation Method in High-Resolution Image Combined with Multi-Level Information

As clouds of different thicknesses block sunlight, large areas of cloud shadows with varying brightness can appear on the ground. Cloud shadows in high-resolution remote sensing images lead to uneven loss of image feature information. However, cloud shadows still retain feature information, and how to compensate for and restore unbalanced cloud shadow occlusion is of great significance in improving image quality. Though traditional shadow compensation methods can enhance the shaded brightness, the results are inconsistent in a single shadow region with over-compensated or insufficient compensation problems. Thus, this paper proposes a shadow-balanced compensation method combined with multi-level information. Multi-level information comprising the information of a shadow pixel, a local super-pixel centered with the pixel, the global cloud shadow region, and the global non-shadow region information, to comply with the cloud shadow’s internal difference. First, the original image is detected via the cloud shadow detection method and post-processing. The initial shadow is detected combined with designed complex shadow features and morphological shadow index features with threshold methods. Then, post-processing considering shadow area and morphological operation is applied to remove the small, non-cloud-shadow objects. Meanwhile, the initial image is also divided into super-pixel homogeneity regions using the super-pixel segmentation principle. A super-pixel region is between the pixel and the shadow area. Different from pixel and other window regions, it can provide a different measurement levels considering object homogeneity. Thus, a balanced compensation model is designed by combining the feature value of a shadow pixel and the mean and variance of a super-pixel, shadow region, and non-shadow region on the basis of the linear correlation correction principle. The super-pixel around the shadow pixel provides a local reliable homogenous region. It can reflect the internal difference inside the shadow region. Therefore, introducing a super-pixel in the proposed model can effectively compensate for the shaded information in a balanced way. Compared to those of only using pixel and shadow region information, the compensated results introduce super-pixel information, can deal with the homogenous region as a global one, and can be adaptive to the illustration differences in a cloud shadow. The experimental results show that compared to that of other reference methods, the quality of the proposed compensation result is better. The proposed method can enhance brightness and recover detailed information in shadow regions in a more balanced way. The issue of over-compensation and insufficient compensation inside a single shadow region can be resolved. Thus, the total result is similar to that of a non-shadow region. The proposed method can be used to recover the cloud shadow information more self-adaptively to improve image quality and usage in other applications.

Slice-to-slice context transfer and uncertain region calibration network for shadow detection in remote sensing imagery

Although current methods based on deep learning (DL) have been widely employed in shadow detection tasks, the cluttered background and complex shadow features in remote sensing images (RSIs) make shadow detection still a challenging task. In this paper, we apply a neural network combined with a distance transformation algorithm to RSI shadow detection for the first time and propose a novel slice-to-slice context transfer and uncertainty region calibration network (SCUCNet). First, an interslice context semantic transfer (SCT) module is proposed to explore the connections among spatial pixel information and extract sufficient global contextual information via interslice feature transfer. This process helps alleviate the interference of multiscale differences and complex backgrounds on the detection results. Second, to further optimize the initial output mapping, calibrate the uncertainty region, and enhance the feature representation, we propose a plug-and-play uncertainty region awareness and calibration (URAC) module combined with a distance transform algorithm, which introduces only 0.09 M parameters. The quantitative results show that the proposed model outperforms state-of-the-art methods for shadow detection in RSIs, achieving an Intersection over Union (IoU) of 88.33% on the aerial imagery dataset for shadow detection (AISD). In addition, extensive experiments and visualizations further prove the validity and interpretability of our method, and the proposed method can effectively mitigate the interference caused by multiscale differences, morphological diversity, spectral similarity and heterogeneity.

Improvement and application of UNet network for avoiding the effect of urban dense high-rise buildings and other feature shadows on water body extraction

ABSTRACT Finding a means to extract water body information efficiently and accurately from high-resolution remote sensing images has been an important research direction in the field of water body extraction in recent years. However, shadows from buildings and other obstacles interfere with the accuracy of water body extraction. To address this problem, this paper proposes a neural network method incorporating an attention mechanism for water body extraction. This paper is based on the U-Net convolutional neural network and adds the squeeze-and-excitation module of SENet, an attention mechanism, to the downsampling process of the U-Net network. The module weights the feature maps so that the network focuses more on the features of the water body information and thus reduces attention to the shadow features from buildings and other features, thus improving the accuracy of image segmentation. The dropout and batch normalization layers are also added to improve the generalization ability and stability of the model. In this paper, a water extraction network SE-CU-Net model is presented to overcome the shadowing effect from buildings and other features. Using GF-2 images of Jiangsu province as the data source, the recognition results of this paper are compared with Dense-Net, Res-Net, Seg-Net, U-net, SVM, and RF. Through the comparison experiments, the model of this paper can not only better overcome the influence of shadows from buildings and other features, but it also has a stronger recognition ability and recognition effect. The average ASCR, Precision, mIoU, OA, F1-Score and kappa coefficients in the three tested areas reached 98.27%, 97.17%, 89.33%, 98.2%, 89.3% and 0.883, respectively, with significantly higher accuracy than the other six classical methods, verifying the effectiveness of the model in overcoming the influence of shadows from buildings and other features in water body extraction research.

A comparative study of the performances of joint RFE with machine learning algorithms for extracting Moso bamboo (Phyllostachys pubescens) forest based on UAV hyperspectral images

Unmanned aerial vehicle (UAV) hyperspectral remote sensing technology has developed rapidly in recent years, providing a new scheme for intelligent monitoring of forest resources. This study aims to deeply mining the UAV hyperspectral information, and construct the best model for extracting Moso bamboo (Phyllostachys pubescens) forest information. Firstly, the spectral information features of Moso bamboo, broadleaf, bare area and shadow were obtained and their spectral anisotropies were analyzed. Secondly, multiple machine learning algorithms were combined with the recursive feature elimination (RFE) to obtain three sets of optimized feature subsets. Eventually, three sets of optimization features were substituted into multiple machine learning algorithms to build nine models of Moso bamboo information extraction, comparing their classification effects and generalization capabilities. The results showed that: (1) The subset of features optimized by different base classifiers in combination with RFE and the ranking results of optimized features’ importance were different, but all the three sets of optimized feature subsets showed relatively obvious feature difference patterns; (2) The nine models based on SVM-RFE, RF-RFE and XGBoost-RFE were all capable of extracting Moso bamboo information, among which, the classification effect of the XGBoost-RFE-XGBoost model was the best and had significant advantages, with the overall classification accuracy OA, Kappa coefficient and R 2 of 98.75%, 0.983 and 0.980, respectively, and the classification accuracy for Moso bamboo was 98.34%. Thus, XGBoost was the most applicable algorithm for feature optimization in combination with RFE, and the most effective algorithm with a better generalization ability for extracting Moso bamboo information from UAV hyperspectral images. This study is crucial for Moso bamboo’s large area monitoring and fine identification, providing a technical reference for obtaining information about the spatial and temporal distribution of Moso bamboo resources timely and accurately, facilitating the monitoring and management of them, and giving full play to their ecological and economic benefits.

Observable features of charged Kiselev black hole with non-commutative geometry under various accretion flow

The light passing near the black hole (BH) is deflected due to the gravitational effect, producing the BH shadow, a dark inner region that is often surrounded by a bright ring, whose optical appearance comes directly from BH’s mass and its angular momentum. We mainly study the shadow and observable features of non-commutative (NC) charged Kiselev BH, surrounded by various profiles of accretions. To obtain the BH shadow profile, we choose specific values of the model parameters and concluded that the variations of each parameter directly vary the light trajectories and size of BH. For thin disk accretion, which includes direct lensing and photon rings emissions, we analyze that the profile of BH contains the dark interior region and bright photon ring. However, their details depends upon the emissions, generally, direct emission plays significant role in the total observed luminosity, while lensing ring has a small contribution and the photon ring makes a negligible contribution, as usual, the latter can be ignored safely. Moreover, we also consider the static and infalling accretion matters and found that the location of the photon sphere is almost the same for both cases. However, the specific intensity which is observed from BH profile found to be darker for infalling accretion case due to the Doppler effect of the infalling motion as compared to the static one.

The optical appearance of charged four-dimensional Gauss–Bonnet black hole with strings cloud and non-commutative geometry surrounded by various accretions profiles

Thanks for the releasing image of supermassive black holes (BHs) by the event horizon telescope (EHT) at the heart of the M87 galaxy. After the discovery of this mysterious object, scientists paid attention to exploring the BH shadow features under different gravitational backgrounds. In this scenario, we study the light rings and observational properties of BH shadow surrounded by different accretion flow models and then investigate the effect of model parameters on the observational display and space-time structure of BHs in the framework of our considering system. Under the incompatible configuration of the emission profiles, the images of BHs comprise that the observed luminosity is mainly determined by direct emission, while the lensing ring will provide a small contribution of the total observed flux and the photon ring makes a negligible contribution due to its exponential narrowness. More importantly, the observed regions and specific intensities of all emission profiles are changed correspondingly under variations of parameters. For optically thin accreting matters, we analyze the profile and specific intensity of the shadows with static and infalling accretions models, respectively. We find that with an infalling motion the interior region of the shadows will be darker than the static case, due to the Doppler effect of the infalling movement. Finally, it is concluded that these findings support the fact that the change of BH state parameters will change the way of space-time geometry, thus affecting the BH shadow dynamics.

A novel feature susceptibility approach for a PEMFC control system based on an improved XGBoost-Boruta algorithm

Data-driven modelling methods are being developed in the quest to achieve more accurate performance prediction of protons exchange membrane fuel cell (PEMFC) systems in response to their complicated physicochemical phenomena. However, there is little research in this field detailing the pre-processing and selection of balance of plants (BOP) features for the input layer of system performance prediction at different current densities. Furthermore, most of the previous research applies neural networks based on simulation data rather than real-time bench or vehicle operation datasets which leads to low robustness and unreliable practical results. This paper details the application of a novel algorithm denoted XGBoost-Boruta, which utilises the combination of an ensemble learning approach and a wrapping approach, to improve the robustness of feature selection and to increase the accuracy and robustness of PEMFC system performance prediction. By introduction of the Z score and shadow features to eliminate the randomness of conventional ensemble learning methods, seven key controllable BOP variables of the hydrogen anode, air cathode and cooling subsystems are selected as the original input variables to determine their dependency on the stack voltage. Two case studies are presented for verification and validation of the proposed algorithm based on the real-time dataset of bench experimental data and data obtained from heavy truck operation at current densities ranging from 100 to 1500 mA/cm2. The feature selection strategy, based on the proposed XGBoost-Boruta algorithm, largely decreases the RMSE by 23.8% and 14.1% and the R2 increases by 0.06 and 0.04 of both the bench experimental and the heavy truck validation datasets respectively.

РОЛЬ ОБЩЕСТВЕННЫХ ОРГАНИЗАЦИЙ ОХОТНИКОВ И РЫБОЛОВОВ В ИСТОЩЕНИИ БИОРАЗНООБРАЗИЯ (НА ПРИМЕРЕ УСОЛЬСКОГО РАЙОННОГО ОХОТОБЩЕСТВА ИРКУТСКОЙ ОБЛАСТИ)

The article discusses real events related to the primary hunting community and the shadow features of hunting activities on the example of the Usolsky regional hunting society (hereinafter – Usolsky RO IOOOiR), which is a direct tenant of a forest plot in the forest fund of the Irkutsk region. It is shown that each hunter is not just a person engaged in amateur hunting, but a separate entity that fulfills the terms of the lease agreement and uses hunting resources, as well as a participant in security and biotechnical measures on the territory of hunting grounds attached to the hunting society. Also, an analysis is made of a number of economic features inherent in the Usolsky hunting society. The purpose and objectives of the research of the author and at the same time the chairman of the primary hunting collective are to determine the importance of public organizations of hunters and fishermen, as well as to present the economic inexpediency of hunting communities in fixed (leased) territories (on the example of the Usolsky RO IOOOiR of the Irkutsk region). The final results of the economic activity of any hunting community depend on the size and quality characteristics of the hunting grounds.

MRPFA-Net for Shadow Detection in Remote-Sensing Images

The presence of shadows in high-resolution (HR) remote-sensing images reduces object detection accuracy. To address this problem, in this paper, we proposed a deep neural network algorithm for shadow detection by using the AISD and SSAD remote-sensing shadow image datasets. To improve the ability to extract spatial information from feature maps, we developed a cross-spatial attention module that focuses on semantic information in the horizontal and vertical directions at each position point on the remote-sensing image. This module overcomes the limitations of existing technologies in accurately judging small areas and suspected shadow areas and in missing or incorrectly detected shadow areas. In addition, to improve the ability to extract shadow features and the accuracy of shadow detection in remote-sensing images, we developed a channel attention module that assigns more attention to channels that conform to the shadow color characteristics. The network architecture comprises an encoder – decoder structure, with ResNeXt50 used as the backbone for the encoder and a multi resolution parallel fusion (MRPF) designed for the decoder; cross-spatial and channel attention were incorporated into the decoder unit. Experimental results demonstrated the superior performance of the proposed algorithm, with an F1 score of 92.6% for the shadow category on the test set, thus, outperforming other algorithms and making the proposed method an effective solution for shadow detection in HR remote-sensing images.