bluuebunny/crossref_metadata_2025 huggingface dataset:
Vehicle Target Detection Method for Wide-Area SAR Images Based on Coarse-Grained Judgment and Fine-Grained Detection
Authors: Song Yucheng , Wang Shuo , Li Qing , Mu Hongbin , Feng Ruyi , Tian Tian , Tian Jinwen
Published: June 2023

The detection of vehicle targets in wide-area Synthetic Aperture Radar (SAR) images is crucial for real-time reconnaissance tasks and the widespread application of remote sensing technology in military and civilian fields. However, existing detection methods often face difficulties in handling large-scale images and achieving high accuracy. In this study, we address the challenges of detecting vehicle targets in wide-area SAR images and propose a novel method that combines coarse-grained judgment with fine-grained detection to overcome these challenges. Our proposed vehicle detection model is based on YOLOv5, featuring a CAM attention module, CAM-FPN network, and decoupled detection head, and it is strengthened with background-assisted supervision and coarse-grained judgment. These various techniques not only improve the accuracy of the detection algorithms, but also enhance SAR image processing speed. We evaluate the performance of our model using the Wide-area SAR Vehicle Detection (WSVD) dataset. The results demonstrate that the proposed method achieves a high level of accuracy in identifying vehicle targets in wide-area SAR images. Our method effectively addresses the challenges of detecting vehicle targets in wide-area SAR images, and has the potential to significantly enhance real-time reconnaissance tasks and promote the widespread application of remote sensing technology in various fields.
Equidistant Nodes Orthogonal Polynomial Fitting for Harmonic Constants of Long-Period Tides Based on Satellite Altimeter Data
Authors: Zhang Yunfei , Wang Qixiang , Zhang Yibo , Xu Minjie , Wang Yonggang , Lv Xianqing
Published: June 2023

The harmonic constants of long-period tidal constituents are critical for simulating the tide process, obtaining theoretical datum sounding reduction, and conducting further ocean research. In this paper, the equidistant node orthogonal polynomial fitting (ENOPF) method is employed to fit the harmonic constants of the T/P satellite altimeter data and the gridded altimeter data from the Data Unification and Altimeter Combination System (DUACS) to obtain the full-field harmonic constants of the long-period tidal constituents (Sa and Ssa) in the Bohai Sea, Yellow Sea, and East China Sea (BYECS). To verify the validity of the ENOPF method, the long-period tidal harmonic constants of the ENOPF method, the Finite Element Solutions 2014 (FES2014) model, the Empirical Ocean Tide 20 (EOT20) model, and the DUACS data were compared with the observations of the X-TRACK products and the tide gauges. In addition, the root-mean-square errors (RMSEs), amplitude differences (ΔH), and phase-lag differences (ΔG) of the comparison results were calculated. According to the error analysis and the cotidal charts, the ENOPF method is better than the other three methods or models for comparing tide gauges (6.19 cm, 5.85 cm, 25.44°; 1.10 cm, 0.72 cm, 59.09°, respectively. The cotidal charts obtained by the ENOPF method are smoother and have better consistency with the actual track data. The results indicate that, due to the polynomial fitting method adopted in ENOPF method, which has the characteristics of improving the resolution infinitely, it is easier to obtain the harmonic constant of full field than with other models or methods. This study proves that the ENOPF method is a reasonable and simple tool in extracting the harmonic constants of the BYECS.
A Novel Method for Monitoring Tropical Cyclones’ Movement Using GNSS Zenith Tropospheric Delay
Authors: Lian Dajun , He Qimin , Li Li , Zhang Kefei , Fu Erjiang , Li Guangyan , Wang Rui , Gao Biqing , Song Kangming
Published: June 2023

Precipitable water vapor (PWV) is an important meteorological factor for predicting extreme weather events such as tropical cyclones, which can be obtained from zenith tropospheric delay (ZTD) by using a conversion. A time difference of ZTD arrival (TDOZA) model was proposed to monitor the movement of tropical cyclones, and the fifth-generation reanalysis dataset of the European Centre for Medium-range Weather Forecasting (ERA5)-derived ZTD (ERA5-ZTD) was used to estimate the movement of tropical cyclones based on the model. The global navigation satellite system-derived ZTD and radiosonde data-derived PWV (RS-PWV) were used to test the accuracy of the ERA5-ZTD and analyze the correlation between ZTD and PWV, respectively. The statistics showed that the mean Bias, RMS and STD of the ERA5-ZTD were 6.4 mm, 17.1 mm and 16.5 mm, respectively, and the mean correlation coefficient of the ERA5-ZTD and RS-PWV was 0.951, which indicates that the ZTD can be used to predict weather events instead of PWV. Then, spatiao-temporal characteristics of ZTD during the four tropical cyclone (i.e., Merbok, ROKE, Neast and Hato) periods in 2017 were analyzed, and the result showed that the moving directions of ZTD and the tropical cyclones were consistent. Thus, the ZTD time series over the ERA5 grids around the tropical cyclones’ paths were used to estimate the velocity of the tropical cyclones based on the TDOZA model, when the tropical cyclones are approaching or leaving. Compared with the result from the China Meteorological Administration, the mean absolute and relative deviations of the TDOZA model-derived velocity were 2.55 km/h and 10.0%, respectively. These results suggest that ZTD can be used as a new supplementary meteorological parameter for monitoring tropical cyclone events.
Quantitative Study on Salinity Estimation of Salt-Affected Soils by Combining Different Types of Crack Characteristics Using Ground-Based Remote Sensing Observation
Authors: Zhang Zhuopeng , Li Xiaojie , Zhou Shuang , Zhao Yue , Ren Jianhua
Published: June 2023

Soil salinity is one of the parameters used for determining the extent of soil salinization. During water evaporation, the surface of salt-affected soils in the Songnen Plain, China, exhibits obvious shrinkage and cracking phenomena due to the high salt content. The aim of this current study is to quantify the influence of the salt content on the surface shrinkage–cracking process and to achieve quantitative extraction of soil salinity parameters based on different crack parameter types. In order to achieve the above objectives, a controlled shrinkage–cracking experiment was conducted. Subsequently, three kinds of crack characteristics such as crack length, box-counting dimension, and 12 gray-level co-occurrence matrix (GLCM) texture features were quantitatively extracted from the standard binary crack patterns. In order to predict the soil physical–chemical properties of salt-affected soils in the Songnen Plain, three models such as multiple linear regression (MLR), multiple stepwise regression (MSR), and artificial neural network (ANN) were developed and compared based on the crack length, box-counting dimension, and the first two principal components of GLCM texture features. The results show that the extent of desiccation cracks was determined by soil salinity since the water film caused by exchangeable cations and the thickness of DDL determined by soil salinity can promote desiccation cracking. Although the three methods have high prediction accuracy for Na+, electrical conductivity (EC), and total soil salinity, the ANN-based method showed the best prediction with R2 values for Na+, EC, and soil salinity as high as 0.91, 0.91, and 0.89, and ratio of performance to deviation (RPD) values for Na+, EC, and soil salinity corresponding to 2.96, 3.47, and 2.95.
Level Sets Guided by SoDEF-Fitting Energy for River Channel Detection in SAR Images
Authors: Han Bin , Basu Anup
Published: June 2023

To achieve river channel detection in SAR (synthetic aperture radar) images, we developed a level-set-based model (LSBM) guided by a designed data-fitting energy which is called the SoDEF (sum of dual exponential functions)-fitting energy. Firstly, we designed a function by computing the sum of dual exponential functions to substitute for the quadratic function, and used it to construct the data-fitting energy. Secondly, the adaptive area-fitting centers (AFCs) were computed based on two kinds of grayscale characteristics, which are more accurate and more stable. Thirdly, the Dirac function in gradient descent flow was displaced by an edge indicator function to help the evolving level sets stop at the target edges. Moreover, some regularized terms were incorporated into the objective function to guarantee the model’s stability. The river channel detection experiments conducted with real SAR images indicated that the developed model is superior to the related state-of-the-art methods in its detection accuracy and efficiency.
Filtering Green Vegetation Out from Colored Point Clouds of Rocky Terrains Based on Various Vegetation Indices: Comparison of Simple Statistical Methods, Support Vector Machine, and Neural Network
Authors: Štroner Martin , Urban Rudolf , Suk Tomáš
Published: June 2023

Filtering out vegetation from a point cloud based on color is only rarely used, largely due to the lack of knowledge of the suitability of input information (color, vegetation indices) and the thresholding methods. We have evaluated multiple vegetation indices (ExG, ExR, ExB, ExGr, GRVI, MGRVI, RGBVI, IKAW, VARI, CIVE, GLI, and VEG) and combined them with 10 methods of threshold determination based on training set selection (including machine learning methods) and the renowned Otsu’s method. All these combinations were applied to four clouds representing vegetated rocky terrain, and the results were compared. The ExG and GLI indices were generally the most suitable for this purpose, with the best F-scores of 97.7 and 95.4, respectively, and the best-balanced accuracies for the same combination of the method/vegetation index of 98.9 and 98.3%, respectively. Surprisingly, these best results were achieved using the simplest method of threshold determination, considering only a single class (vegetation) with a normal distribution. This algorithm outperformed all other methods, including those based on a support vector machine and a deep neural network. Thanks to its simplicity and ease of use (only several patches representing vegetation must be manually selected as a training set), this method can be recommended for vegetation removal from rocky and anthropogenic surfaces.
MIMO DFRC Signal Design in Signal-Dependent Clutter
Authors: Yao Xue , Pan Bunian , Fan Tao , Yu Xianxiang , Cui Guolong , Nie Xiangfei
Published: June 2023

This paper deals with the Dual-Function Radar and Communication (DFRC) signal design for a Multiple-Input–Multiple-Output (MIMO) system, considering the presence of signal-dependent clutter. A modulation methodology called Spectral Position Index and Amplitude (SPIA) modulation is proposed, which involves selecting passband and stopband positions and applying amplitude modulation. Signal to Interference plus Noise Ratio (SINR) is maximized to enhance radar detectability. Meanwhile, variable modulus and communication modulation constraints are enforced to ensure compatibility with the current hardware techniques and communication demand, respectively. In addition, the mainlobe width and sidelobe level constraints used to concentrate energy in a specific area of space are enforced. To tackle the resulting nonconvex and NP-hard optimization problem, an Iterative Block Enhancement (IBE) framework that alternately updates each signal in each emitting antenna is exploited to monotonically increase SINR. Each block involves the Dinkelbach’s Iterative Procedure (DIP), Sequential Convex Approximation (SCA) and Alternating Direction Method of Multipliers (ADMM) to obtain a single signal. The computational complexity and convergence of the algorithm are analyzed. Finally, the numerical results highlight the effectiveness of the proposed dual-function scheme in sidelobe signal-dependent clutter.
Analysis of Four Decades of Land Use and Land Cover Change in Semiarid Tunisia Using Google Earth Engine
Authors: Kadri Nesrine , Jebari Sihem , Augusseau Xavier , Mahdhi Naceur , Lestrelin Guillaume , Berndtsson Ronny
Published: June 2023

Semiarid Tunisia is characterized by agricultural production that is delimited by water availability and degraded soil. This situation is exacerbated by human pressure and the negative effects of climate change. To improve the knowledge of long-term (1980 to 2020) drivers for Land Use and Land Cover (LULC) changes, we investigated the semiarid Rihana region in central Tunisia. A new approach involving Google Earth Engine (GEE) was used to map LULC using Landsat imagery and vegetative indices (NDVI, MSAVI, and EVI) by applying a Random Forest (RF) classifier. A Rapid Participatory Systemic Diagnosis (RPSD) was used to consider the relation between LULC changes and their key drivers. The methodology relied on interviews with the local population and experts. Focus groups were conducted with practicians of the Regueb Agricultural Extension Services, followed by semi-structured interviews with 52 households. Results showed the following: (1) the RF classifier in Google Earth Engine had strong performance across diverse Landsat image types resulting in overall classification accuracy of ≥0.96 and a kappa coefficient ≥0.93; (2) rainfed olive land increased four times during the study period while irrigated agriculture increased substantially during the last decade; rangeland and rainfed annual crops decreased by 58 and 88%, respectively, between 1980 and 2021; (3) drivers of LULC changes are predominately local in nature, including topography, local climate, hydrology, strategies of household, effects of the 2010 revolution, associated increasing demand for natural resources, agricultural policy, population growth, high cost of agricultural input, and economic opportunities. To summarize, changes in LULC in Rihana are an adaptive response to these various factors. The findings are important to better understand ways towards sustainable management of natural resources in arid and semiarid regions as well as efficient methods to study these processes.
Evaluating the Abilities of Satellite-Derived Burned Area Products to Detect Forest Burning in China
Authors: Wang Xueyan , Di Zhenhua , Liu Jianguo
Published: June 2023

Fire plays a prominent role in the construction and destruction of ecosystems, and the accurate estimation of the burned area (BA) after a fire occurrence is of great significance to protect ecosystems and save people’s lives and property. This study evaluated the performances of three publicly available BA satellite products (GFED4, MCD64CMQ, and FireCCI5.1) in detecting Chinese forest fire burning from 2001 to 2016 across different time scales (yearly, monthly, and seasonally) and spatial scales (regional and provincial). The reference data were derived from the monthly China Forestry Statistical Yearbook (CFSY), and they were mainly used to evaluate the detection ability of each of the three BA products in the three major forest fire areas of China consisting of the Northeast (NE), Southwest (SW), and Southeast (SE) regions. The main results are as follows: (1) A significant declining BA trend was demonstrated in the whole study area and in the NE and SE subregions. Specifically, the slopes for the whole area ranged from −3821.1 ha/year for MCD64CMQ to −33,218 ha/year for the CFSY, the slopes for the NE region ranged from −3821.1 ha/year for MCD64CMQ to −33,218 ha/year for the CFSY, and the slopes for the SE region ranged from −594.24 ha/year for GFED4 to −3162.1 ha/year for the CFSY. The BA in China was mainly dominated by forest fires in the NE region, especially in 2003 and 2006 when this region accounted for 90% and 87% of occurrences, respectively. (2) Compared with the CFSY, GFED4 had the best performance at the yearly scale with an RMSE of 23.9 × 104 ha/year and CC of 0.83. Similarly, at the monthly scale, GFED4 also had the best performance for the three regions, with the lowest RMSE ranging from 0.33 × 104 to 5.4 × 104 ha/month—far lower than that of FireCC5.1 which ranged from 1.16 × 104 to 8.56 × 104 ha/month (except for the SE region where it was slightly worse than MCD64CMQ). At the seasonal scale, GFFD4 had the best performance in spring and winter. It was also noted that the fewer BAs in summer made the differences among the products insignificant. (3) Spatially, GFED4 had the best performance in RMSEs for all the provinces of the three regions, in CCs for the provinces of the SW and SE regions, and in MEs for the provinces of the SE region. (4) All three products had stronger detection abilities for severe and disaster fires than for common fires. Additionally, GFED4 had a more consistent number of months with the CFSY than the other products in the NE region. Moreover, the conclusion that GFED4 had the best performance in the China region was also proved using other validated BA datasets. These results will help us to understand the BA detection abilities of the satellite products in China and promote the further development of multi-source satellite fire data fusion.
Characteristics of Air Pollutant Distribution and Sources in the East China Sea and the Yellow Sea in Spring Based on Multiple Observation Methods
Authors: Wang Yucheng , Xu Guojie , Chen Liqi , Chen Kui
Published: June 2023

The composition of marine aerosol is quite complex, and its sources are diverse. Across the East China Sea (ECS) and the Yellow Sea (YS), multi-dimensional analysis of marine aerosols was conducted. The characteristics of carbonaceous aerosols and gaseous pollutants were explored through in situ ship-based observation, MERRA-2 reanalysis datasets and TROPOMI data from Sentinel-5P satellite. Black carbon (BC)’s average concentration is 1.35 ± 0.78 μg/m3, with high-value BC observed during the cruise. Through HYSPLIT trajectory analysis, sources of BC were from the northern Eurasian continent, the Shandong Peninsula, the ECS and Northwest Pacific Ocean (NWPO). The transport of marine sources like ship emissions cannot be ignored. According to the absorption Angstrom exponent (AAE), BC originates from biomass burning (BB) in the shortwave band (~370 nm) and from fossil fuel combustion in the longwave band (~660 nm). Organic carbon (OC), sulfate (SO42−) and BC report higher Angstrom exponent (AE) while dust and sea salt reveal lower AE, which can be utilized to classify the aerosols as being fine- or coarse-mode, respectively. OC has the highest AE (ECS: 1.98, YS: 2.01), indicating that anthropogenic activities could be a significant source. The process of biomass burning aerosol (BBA) mixed with sea salt could contribute to the decline in BBA’s AE. Ship emissions may affect the distribution of tropospheric nitrogen dioxide (NO2) in the ECS, especially during the COVID-19 pandemic. Tropospheric NO2 over the YS has the highest value (up to 12 × 1015 molec/cm2). Stratospheric NO2 has a ladder-like distribution from north to south, and the variation gradient was lower than that in the troposphere. Carbon monoxide (CO) accumulates in the south and east of the ECS and the east of the YS, while the variation over the eastern YS is relatively frequent. Seas near the Korean Peninsula have extremely high CO concentration (up to 1.35 × 1017 molec/cm2).
Reconstructing Groundwater Storage Changes in the North China Plain Using a Numerical Model and GRACE Data
Authors: Zhang Junchao , Hu Litang , Sun Jianchong , Wang Dao
Published: June 2023

Groundwater has been extensively exploited in the North China Plain (NCP) since the 1970s, leading to various environmental issues. Numerous studies have utilized Gravity Recovery and Climate Experiment (GRACE) satellite data to analyze changes in groundwater storage in the NCP and provide valuable insights. However, the low spatial resolution of GRACE data has posed challenges for its widespread application, and there have been limited studies focusing on refining groundwater storage changes in the NCP. In addition, the lack of data on the gap period between GRACE and GRACE-FO hinders in-depth research on regional groundwater storage anomalies (GWSA). This paper applied a groundwater storage model called NGFLOW-GRACE to construct a groundwater storage change model in the NCP at spatial resolutions of both 1° and 0.05°. The groundwater storage change model was calibrated and driven using gratis data, with hydrogeological parameter values estimated using the shuffled complex evolution algorithm (SCE-UA). The model exhibited favorable performance, with correlation coefficients greater than 0.85 during the calibration period and 55% of coefficients greater than 0.50 during the validation period. Interestingly, the results indicate that different combinations of remote sensing data do not significantly impact the outcomes, while the hydraulic gradient coefficient demonstrates the highest sensitivity. Appropriate reconstructed data were selected within the empty window period, and by downscaling the model to a resolution of 0.05°, a complete cycle (January 2003 to December 2020) of GWSA was derived. Through comprehensive comparisons with previous research findings on both temporal and spatial scales, it can be concluded that the downscaled groundwater storage changes obtained from the established model demonstrated high reliability.
Unmanned Aerial Vehicles for Search and Rescue: A Survey
Authors: Lyu Mingyang , Zhao Yibo , Huang Chao , Huang Hailong
Published: June 2023

In recent years, unmanned aerial vehicles (UAVs) have gained popularity due to their flexibility, mobility, and accessibility in various fields, including search and rescue (SAR) operations. The use of UAVs in SAR can greatly enhance the task success rates in reaching inaccessible or dangerous areas, performing challenging operations, and providing real-time monitoring and modeling of the situation. This article aims to help readers understand the latest progress and trends in this field by synthesizing and organizing papers related to UAV search and rescue. An introduction to the various types and components of UAVs and their importance in SAR operations is settled first. Additionally, we present a comprehensive review of sensor integrations in UAVs for SAR operations, highlighting their roles in target perception, localization, and identification. Furthermore, we elaborate on the various applications of UAVs in SAR, including on-site monitoring and modeling, perception and localization of targets, and SAR operations such as task assignment, path planning, and collision avoidance. We compare different approaches and methodologies used in different studies, assess the strengths and weaknesses of various approaches, and provide insights on addressing the research questions relating to specific UAV operations in SAR. Overall, this article presents a comprehensive overview of the significant role of UAVs in SAR operations. It emphasizes the vital contributions of drones in enhancing mission success rates, augmenting situational awareness, and facilitating efficient and effective SAR activities. Additionally, the article discusses potential avenues for enhancing the performance of UAVs in SAR.
Dim and Small Target Detection Based on Energy Sensing of Local Multi-Directional Gradient Information
Authors: Fan Xiangsuo , Li Juliu , Min Lei , Feng Linping , Yu Ling , Xu Zhiyong
Published: June 2023

It is difficult for traditional algorithms to remove cloud edge contours in multi-cloud scenarios. In order to improve the detection ability of dim and small targets in complex edge contour scenes, this paper proposes a new dim and small target detection algorithm based on local multi-directional gradient information energy perception. Herein, based on the information difference between the target area and the background area in the four direction neighborhood blocks, an energy enhancement model for multi-directional gray aggregation (EMDGA) is constructed to preliminarily enhance the target signal. Subsequently, a local multi-directional gradient reciprocal background suppression model (LMDGR) was constructed to model the background of the image. Furthermore, this paper proposes a multi-directional gradient scale segmentation model (MDGSS) to obtain candidate target points and then combines the proposed multi-frame energy-sensing (MFESD) detection algorithm to extract the true targets from sequence images. Finally, in order to better illustrate the effect of the algorithm proposed in this paper in detecting small targets in a cloudy background, four sequence images are selected for detection. The experimental results show that the proposed algorithm can effectively suppress the edge contour of complex clouds compared with the traditional algorithm. When the false alarm rate Pf is 0.005%, the detection rate Pd is greater than 95%.
Spatial-Convolution Spectral-Transformer Interactive Network for Large-Scale Fast Refined Land Cover Classification and Mapping Based on ZY1-02D Satellite Hyperspectral Imagery
Authors: Wang Yibo , Zhang Xia , Huang Changping , Qi Wenchao , Wang Jinnian , Yang Xiankun , Ding Songtao , Tao Shiyu
Published: June 2023

Satellite hyperspectral imagery is an important data source for large-scale refined land cover classification and mapping, but the high spatial heterogeneity and spectral variability at low spatial resolution and the high computation cost for massive data remain challenges in the research community. In recent years, convolutional neural network (CNN) models with the capability for feature extraction have been widely used in hyperspectral image classification. However, incomplete feature extraction, inappropriate feature fusion, and high time consumption are still the major problems for CNN applications in large-scale fine land cover mapping. In this study, a Spatial-Convolution Spectral-Transformer Interactive Network (SCSTIN) was proposed to integrate 2D-CNN and Transformer into a dual-branch network to enhance feature extraction capabilities by exploring spatial context information and spectral sequence signatures in a targeted manner. In addition, spatial-spectral interactive fusion (SSIF) units and category-adaptive weighting (CAW) as two feature fusion modules were also adopted between and after the two feature extraction branches to improve efficiency in feature fusion. The ZY1-02D hyperspectral imagery was collected to conduct the experiments in the study area of the eastern foothills of the Helan Mountains (EFHLM), covering an area of about 8800 km2, which is the largest hyperspectral dataset as far as we know. To explore the potential of the proposed network in terms of accuracy and efficiency, SCSTIN models with different depths (SCSTIN-4 and SCSTIN-2) were performed. The results suggest that compared with the previous eight advanced hyperspectral image classifiers, both SCSTIN models achieved satisfactory performance in accuracy and efficiency aspects with low complexity, where SCSTIN-4 achieved the highest accuracy and SCSTIN-2 obtained higher efficiency. Accordingly, the SCSTIN models are reliable for large-scale fast refined land cover classification and mapping. In addition, the spatial distribution pattern of diverse ground objects in EFHLM is also analyzed.
Improving Feature Learning in Remote Sensing Images Using an Integrated Deep Multi-Scale 3D/2D Convolutional Network
Authors: Tinega Haron C. , Chen Enqing , Nyasaka Divinah O.
Published: June 2023

Developing complex hyperspectral image (HSI) sensors that capture high-resolution spatial information and voluminous (hundreds) spectral bands of the earth’s surface has made HSI pixel-wise classification a reality. The 3D-CNN has become the preferred HSI pixel-wise classification approach because of its ability to extract discriminative spectral and spatial information while maintaining data integrity. However, HSI datasets are characterized by high nonlinearity, voluminous spectral features, and limited training sample data. Therefore, developing deep HSI classification methods that purely utilize 3D-CNNs in their network structure often results in computationally expensive models prone to overfitting when the model depth increases. In this regard, this paper proposes an integrated deep multi-scale 3D/2D convolutional network block (MiCB) for simultaneous low-level spectral and high-level spatial feature extraction, which can optimally train on limited sample data. The strength of the proposed MiCB model solely lies in the innovative arrangement of convolution layers, giving the network the ability (i) to simultaneously convolve the low-level spectral with high-level spatial features; (ii) to use multiscale kernels to extract abundant contextual information; (iii) to apply residual connections to solve the degradation problem when the model depth increases beyond the threshold; and (iv) to utilize depthwise separable convolutions in its network structure to address the computational cost of the proposed MiCB model. We evaluate the efficacy of our proposed MiCB model using three publicly accessible HSI benchmarking datasets: Salinas Scene (SA), Indian Pines (IP), and the University of Pavia (UP). When trained on small amounts of training sample data, MiCB is better at classifying than the state-of-the-art methods used for comparison. For instance, the MiCB achieves a high overall classification accuracy of 97.35%, 98.29%, and 99.20% when trained on 5% IP, 1% UP, and 1% SA data, respectively.
Potential Rockfall Source Identification and Hazard Assessment in High Mountains (Maoyaba Basin) of the Tibetan Plateau
Authors: Sun Juanjuan , Wang Xueliang , Guo Songfeng , Liu Haiyang , Zou Yu , Yao Xianglong , Huang Xiaolin , Qi Shengwen
Published: June 2023

Potential rockfall source areas are widely distributed in the high mountain areas of the Tibetan Plateau, posing significant hazards to human lives, infrastructures, and lifeline facilities. In a combination of field investigation, high-precision aerial photogrammetry, and numerical simulation, we took the Maoyaba basin as an example to explore a rapid identification method for high-altitude rockfall sources. An automatic potential rockfall source identification (PRSI) procedure was introduced to simplify the process of rockfall source identification. The study revealed that rockfall sources are concentrated in areas with intense frost weathering. Our identification results were validated using rockfall inventory data detection from remote sensing images and field investigation. Of the rockfall source areas identified by the PRSI procedure, 80.85% overlapped with the remote sensing images result. The accuracy assessment using precision, recall, and F1 score was 0.91, 0.81, and 0.85, respectively, which validates the reliability and effectiveness of the PRSI procedure. Meanwhile, we compared the rockfall source distribution of five DEMs with different resolutions and four neighborhood areas. We discovered that, in addition to high-resolution DEMs (i.e., 1 m and 2 m), medium-resolution DEMs (i.e., 5 m, 12.5 m) also perform well in identifying rockfall sources. Finally, we conducted a hazard assessment based on Culmann’s two-dimensional slope stability model and rockfall hazard vector method. Appropriate protective measures should be taken at high-hazard sections to safeguard pedestrians, vehicles, and related infrastructure from rockfalls.
Self-Supervised Monocular Depth Estimation Using Global and Local Mixed Multi-Scale Feature Enhancement Network for Low-Altitude UAV Remote Sensing
Authors: Chang Rong , Yu Kailong , Yang Yang
Published: June 2023

Estimating depth from a single low-altitude aerial image captured by an Unmanned Aerial System (UAS) has become a recent research focus. This method has a wide range of applications in 3D modeling, digital terrain models, and target detection. Traditional 3D reconstruction requires multiple images, while UAV depth estimation can complete the task with just one image, thus having higher efficiency and lower cost. This study aims to use deep learning to estimate depth from a single UAS low-altitude remote sensing image. We propose a novel global and local mixed multi-scale feature enhancement network for monocular depth estimation in low-altitude remote sensing scenes, which exchanges information between feature maps of different scales during the forward process through convolutional operations while maintaining the maximum scale feature map. At the same time, we propose a Global Scene Attention (GSA) module in the decoder part of the depth network, which can better focus on object edges, distinguish foreground and background in the UAV field of view, and ultimately demonstrate excellent performance. Finally, we design several loss functions for the low-altitude remote sensing field to constrain the network to reach its optimal state. We conducted extensive experiments on public dataset UAVid 2020, and the results show that our method outperforms state-of-the-art methods.
Expansion of the Analytical Modeling of Capacitance for 1-N-1 Multilayered CID Structures with Monotonically Increasing/Decreasing Permittivity
Authors: Khan Anwar Ulla
Published: June 2023

Capacitive sensors that utilize the Coplanar Interdigitated (CID) electrode structure are widely employed in various technical and analytical domains, such as healthcare, infectious disease management, pharmaceuticals, metrology, and environmental monitoring. The present exigency for lab-on-a-chip contrivances and the requisite for the miniaturization of sensors have led to the widespread adoption of CID sensors featuring multiple dielectric layers (DLs), either in the form of substrates or superstrates. Previously, we derived an analytical model for the capacitance of CID capacitive sensors with four distinct 1-N-1 patterns (namely, 1-1-1, 1-3-1, 1-5-1, and 1-11-1) using partial capacitance (PC) and conformal mapping (CM) techniques. The aforementioned model has been employed in various applications wherein the permittivity of successive layers exhibits a monotonic decrease as one moves away from the electrode plane, resulting in highly satisfactory outcomes. Nevertheless, the PC technique is inadequate for structures with multiple layers where the permittivity exhibits a monotonic increase as the distance from the electrodes increases. Given these circumstances, it is necessary to adapt the initial PC method to incorporate these novel configurations. In this work, we have discussed a new approach, splitting the concept of PC into partial parallel capacitance (PPC) and partial serial capacitance (PSC), where new CM transformations are proposed for the latter case. Thus, the present study proposes a novel methodology to expand upon our prior analytical framework, which aims to incorporate scenarios where the permittivity experiences a reduction across successive layers. The outcomes are juxtaposed with the finite element simulation and analytical findings.
Liquid Mixing on Falling Films: Marker-Free, Molecule-Sensitive 3D Mapping Using Raman Imaging
Authors: Nachtmann Marcel , Feger Daniel , Wühler Felix , Rädle Matthias , Scholl Stephan
Published: June 2023

Following up on a proof of concept, this publication presents a new method for mixing mapping on falling liquid films. On falling liquid films, different surfaces, plain or structured, are common. Regarding mixing of different components, the surface has a significant effect on its capabilities and performance. The presented approach combines marker-free and molecule-sensitive measurements with cross-section mapping to emphasize the mixing capabilities of different surfaces. As an example of the mixing capabilities on falling films, the mixing of sodium sulfate with tap water is presented, followed by a comparison between a plain surface and a pillow plate. The method relies upon point-by-point Raman imaging with a custom-built high-working-distance, low-depth-of-focus probe. To compensate for the long-time measurements, the continuous plant is in its steady state, which means the local mixing state is constant, and the differences are based on the liquids’ position on the falling film, not on time. Starting with two separate streams, the mixing progresses by falling down the surface. In conclusion, Raman imaging is capable of monitoring mixing without any film disturbance and provides detailed information on liquid flow in falling films.
State-of-the-Art Deep Learning Methods for Objects Detection in Remote Sensing Satellite Images
Authors: Adegun Adekanmi Adeyinka , Fonou Dombeu Jean Vincent , Viriri Serestina , Odindi John
Published: June 2023

Introduction: Object detection in remotely sensed satellite images is critical to socio-economic, bio-physical, and environmental monitoring, necessary for the prevention of natural disasters such as flooding and fires, socio-economic service delivery, and general urban and rural planning and management. Whereas deep learning approaches have recently gained popularity in remotely sensed image analysis, they have been unable to efficiently detect image objects due to complex landscape heterogeneity, high inter-class similarity and intra-class diversity, and difficulty in acquiring suitable training data that represents the complexities, among others. Methods: To address these challenges, this study employed multi-object detection deep learning algorithms with a transfer learning approach on remotely sensed satellite imagery captured on a heterogeneous landscape. In the study, a new dataset of diverse features with five object classes collected from Google Earth Engine in various locations in southern KwaZulu-Natal province in South Africa was used to evaluate the models. The dataset images were characterized with objects that have varying sizes and resolutions. Five (5) object detection methods based on R-CNN and YOLO architectures were investigated via experiments on our newly created dataset. Conclusions: This paper provides a comprehensive performance evaluation and analysis of the recent deep learning-based object detection methods for detecting objects in high-resolution remote sensing satellite images. The models were also evaluated on two publicly available datasets: Visdron and PASCAL VOC2007. Results showed that the highest detection accuracy of the vegetation and swimming pool instances was more than 90%, and the fastest detection speed 0.2 ms was observed in YOLOv8.
Human Activity Recognition Using Hybrid Coronavirus Disease Optimization Algorithm for Internet of Medical Things
Authors: Khalid Asmaa M. , Khafaga Doaa Sami , Aldakheel Eman Abdullah , Hosny Khalid M.
Published: June 2023

Background: In our current digital world, smartphones are no longer limited to communication but are used in various real-world applications. In the healthcare industry, smartphones have sensors that can record data about our daily activities. Such data can be used for many healthcare purposes, such as elderly healthcare services, early disease diagnoses, and archiving patient data for further use. However, the data collected from the various sensors involve high dimensional features, which are not equally helpful in human activity recognition (HAR). Methods: This paper proposes an algorithm for selecting the most relevant subset of features that will contribute efficiently to the HAR process. The proposed method is based on a hybrid version of the recent Coronavirus Disease Optimization Algorithm (COVIDOA) with Simulated Annealing (SA). SA algorithm is merged with COVIDOA to improve its performance and help escape the local optima problem. Results: The UCI-HAR dataset from the UCI machine learning repository assesses the proposed algorithm’s performance. A comparison is conducted with seven well-known feature selection algorithms, including the Arithmetic Optimization Algorithm (AOA), Gray Wolf Optimizer (GWO), Whale Optimization Algorithm (WOA), Reptile Search Algorithm (RSA), Zebra Optimization Algorithm (ZOA), Gradient-Based Optimizer (GBO), Seagull Optimization Algorithm (SOA), and Coyote Optimization Algorithm (COA) regarding fitness, STD, accuracy, size of selected subset, and processing time. Conclusions: The results proved that the proposed approach outperforms state-of-the-art HAR techniques, achieving an average performance of 97.82% in accuracy and a reduction ratio in feature selection of 52.7%.
On the Reliability of Wearable Technology: A Tutorial on Measuring Heart Rate and Heart Rate Variability in the Wild
Authors: Dudarev Veronica , Barral Oswald , Zhang Chuxuan , Davis Guy , Enns James T.
Published: June 2023

Wearable sensors are quickly making their way into psychophysiological research, as they allow collecting data outside of a laboratory and for an extended period of time. The present tutorial considers fidelity of physiological measurement with wearable sensors, focusing on reliability. We elaborate on why ensuring reliability for wearables is important and offer statistical tools for assessing wearable reliability for between participants and within-participant designs. The framework offered here is illustrated using several brands of commercially available heart rate sensors. Measurement reliability varied across sensors and, more importantly, across the situations tested, and was highest during sleep. Our hope is that by systematically quantifying measurement reliability, researchers will be able to make informed choices about specific wearable devices and measurement procedures that meet their research goals.
Intelligent Diagnostics of Radial Internal Clearance in Ball Bearings with Machine Learning Methods
Authors: Ambrożkiewicz Bartłomiej , Syta Arkadiusz , Georgiadis Anthimos , Gassner Alexander , Litak Grzegorz , Meier Nicolas
Published: June 2023

This article classifies the dynamic response of rolling bearings in terms of radial internal clearance values. The value of the radial internal clearance in rolling-element bearings cannot be described in a deterministic manner, which shows the challenge of its detection through the analysis of the bearing’s dynamics. In this article, we show the original approach to its intelligent detection through the analysis of short-time intervals and the calculation of chosen indicators, which can be assigned to the specific clearance class. The tests were carried out on a set of 10 brand new bearings of the same type (double row self-aligning ball bearing NTN 2309SK) with different radial internal clearances corresponding to individual classes of the ISO-1132 standard. The classification was carried out based on the time series of vibrations recorded by the accelerometer and then digitally processed. Window statistical indicators widely used in the diagnosis of rolling bearings, which served as features for the machine learning models, were calculated. The accuracy of the classification turned out to be unsatisfactory; therefore, it was decided to use a more advanced method of time series processing, which allows for the extraction of subsequent dominant frequencies into experimental modes (Variational Mode Decomposition (VMD)). Applying the same statistical indicators to the modes allowed for an increase in classification accuracy to over 90%.
Closed-Chain Inverse Dynamics for the Biomechanical Analysis of Manual Material Handling Tasks through a Deep Learning Assisted Wearable Sensor Network
Authors: Bezzini Riccardo , Crosato Luca , Teppati Losè Massimo , Avizzano Carlo Alberto , Bergamasco Massimo , Filippeschi Alessandro
Published: June 2023

Despite the automatization of many industrial and logistics processes, human workers are still often involved in the manual handling of loads. These activities lead to many work-related disorders that reduce the quality of life and the productivity of aged workers. A biomechanical analysis of such activities is the basis for a detailed estimation of the biomechanical overload, thus enabling focused prevention actions. Thanks to wearable sensor networks, it is now possible to analyze human biomechanics by an inverse dynamics approach in ecological conditions. The purposes of this study are the conceptualization, formulation, and implementation of a deep learning-assisted fully wearable sensor system for an online evaluation of the biomechanical effort that an operator exerts during a manual material handling task. In this paper, we show a novel, computationally efficient algorithm, implemented in ROS, to analyze the biomechanics of the human musculoskeletal systems by an inverse dynamics approach. We also propose a method for estimating the load and its distribution, relying on an egocentric camera and deep learning-based object recognition. This method is suitable for objects of known weight, as is often the case in logistics. Kinematic data, along with foot contact information, are provided by a fully wearable sensor network composed of inertial measurement units. The results show good accuracy and robustness of the system for object detection and grasp recognition, thus providing reliable load estimation for a high-impact field such as logistics. The outcome of the biomechanical analysis is consistent with the literature. However, improvements in gait segmentation are necessary to reduce discontinuities in the estimated lower limb articular wrenches.
A High-Density EEG Study Investigating the Neural Correlates of Continuity Editing Theory in VR Films
Authors: Cheng Wanqiu , Wang Xuefei , Zou Jiahui , Li Mingxuan , Tian Feng
Published: June 2023

This paper presents a cognitive psychology experiment to explore the differences between 2D and virtual reality (VR) film editing techniques. We recruited sixteen volunteers to view a range of different display modes and edit types of experimental material. An electroencephalogram (EEG) was recorded simultaneously while the participants watched. Subjective results showed that the VR mode reflects higher load scores, particularly in the effort dimension. Different editing types have no effect on subjective immersion scores. The VR mode elicited stronger EEG energy, with differences concentrated in the occipital, parietal, and central regions. On the basis of this, visual evoked potential (VEP) analyses were conducted, and the results indicated that VR mode triggered greater spatial attention, while editing in 2D mode induced stronger semantic updating and active understanding. Furthermore, we found that while the effect of different edit types in both display modes is similar, cross-axis editing triggered greater cognitive violations than continuity editing, which could serve as scientific theoretical support for the development of future VR film editing techniques.