bluuebunny/crossref_metadata_2025 huggingface dataset:
Integrating Data and Model Optimization for Improved Multi-Class Land Cover Classification Using Multispectral Remote Sensing Data
Authors: Li Yichuan , Sun Huanying , Yu Junchuan , Xie Minying , Jin Dingjian , Wang Ming , Xia Guobin
Published: May 2024

Abstract. With the rapid development of artificial intelligence, significant progress has been made in land cover classification using deep learning methods. However, in existing research, most studies focus more on improving classification accuracy by optimizing the model structure and less on mining the value of the data itself. In this paper, experiments on remote sensing multi-class land cover classification were conducted based on Worldview3 data, and strategies to improve classification accuracy were proposed in terms of sampling methods, band combination, loss function, and model optimization. Experiment results show that the proposed improvement strategies are effective for multi-class land cover classification, with recall, F1, and IoU improved by 29%, 17%, and 19%, respectively. The significant improvement in classification accuracy for less-represented targets confirms that enhancing data richness and balance leads to greater improvement than just optimizing the model.
A Straightforward Camera Calibration Method Based on a Single Low-cost Cubical Target
Authors: Cai Yuezhen , Xia Linyuan , Chan Ting On
Published: May 2024

Abstract. Calibrating optical sensors with common targets facilitates the efficient and convenient acquisition of the sensor's internal parameters. In this paper, we present a new method of camera calibration utilizing a low-cost foamy cube, in a form of dice, which is based on the fact that arrangement of pip and cubical die surfaces is mutually orthogonal. Initially, each face and pips are identified through the color information on the die’s surfaces. Subsequently, the centers of pips are corrected using a circular projection model, and radial distortion coefficients are estimated based on centers’ one-to-one correspondences. After that, the tangent information between pairs of pips on orthogonal dice faces are utilized to compute vanishing points, leading to estimation of intrinsic parameters. Experimental results demonstrate that our method has similar effects compared to well-known checkerboard calibration method, reaching an average relative error of 2.43%, simplifying the calibration process in practical applications and showcasing good practicality and robustness.
Heritage Building Information Modelling (HBIM): A Review of Published Case Studies
Authors: Liu Junshan , Li Botao
Published: May 2024

Abstract. This paper presents a systematic review of published case studies on Heritage Building Information Modelling (HBIM) since 2018, and identifies research gaps in the subject matter. Building upon the foundational work of Ewart and Zuecco (2019), this research aims to reveal the latest trends in HBIM implementation, identify recent developments of HBIM technologies, changes in the purpose of HBIM programs and stakeholder roles and responsibilities, and uncover knowledge gaps that provide avenues for future research. Utilizing the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) approach, two major academic databases, Scopus and Web of Science (WOS), were searched, resulting in a rich and diverse dataset for analysis. The paper reports findings on the status of reality capture techniques used to acquire data for HBIM development, focusing on terrestrial laser scanning (TLS) technology. The review highlights the benefits and limitations of TLS for data acquisition in HBIM, as well as the integration of TLS with other reality capture technologies, such as Structure from Motion (SfM) and photogrammetry. The paper further outlines the typical workflow for processing TLS scan data and explores the integration of multiple point clouds for comprehensive heritage site modeling. In addition to the state of the art, this systematic review also uncovers several research gaps in the field of HBIM that offer opportunities for future research and innovation, including the lack of guidelines for data acquisition in HBIM programs, the predominantly manual development process of HBIM from TLS point cloud data, and the under-utilized capacity of TLS for long-term monitoring and change detection. This comprehensive review provides valuable insights into the current landscape of HBIM, offering guidance for future research and development in the heritage sector and highlighting areas in need of further investigation to advance the field.
Study on the Application of Water Resource Management Based on GF-7 Stereo Mapping Satellite
Authors: Liu Ke , Liu Lirong , Luo Zhengyu , Gan Yuhang
Published: May 2024

Abstract. GF-7 satellite was successfully launched on 3 Nov 2019. The two-line stereo camera can effectively obtain 20 km-width panchromatic stereo images with resolution better than 0.8m and 3.2m-resolution multispectral images. Through the composite mapping mode of stereo camera and laser altimeter, the satellite realizes 1:10,000-scale stereo mapping. This article selected the Qiafuqihai Reservoir and its upstream of the Yili River Basin as the study area. Hydrological landforms and vegetation coverage were monitored and three-dimensional dynamic simulation software was developed to verify the potential application of GF-7 stereo mapping satellite data in supporting drainage basin water resource allocation and management in future scenarios. 3 meters DSM derived from GF-7 surveying and mapping satellite finely portrayed the characteristics of hydroponic landforms of small watersheds. The upstream of Qiafuqihai was divided into eleven small watersheds. The water density was 8.5 times more than which was produced from STRM 90m. The elevation of the water-level fluctuation zone of the Qiafuqihai Reservoir ranged from 970 to 998 meters with the water area changed from 28.3 to 57.6 square kilometres. The terrain in the northwest and northeast was flat, with the main vegetation types being natural grasslands (64.9%) and arid lands (5.03%). The three-dimensional dynamic visualization software was developed and demonstrated hydrology information, vegetation coverage information and dynamic changes in the landform of the basin in three-dimensional environment. GF-7, the stereo surveying and mapping satellite, could be perfectly able to support the application of water resources deployment and management in the future.
Relative truth value acquisition method of heterogeneous surface based on the Improved Point Spread Function
Authors: Liu Ning , Liu Qiyue , Tao Zui , Cao Li , Zhang Wenhao , Wang Menglin , Jia Shikang , Guo Ziyang
Published: May 2024

Abstract. The acquisition of pixel-scale relative truth value is important for remote sensing validation.The Point Spread Function (PSF) has been widely used in the field of the acquisition of relative truth value of heterogeneous surface. In this study, we propose an Improved Point Spread Function (IPSF) based on the Median Pixel Variability Weighted (MPVW) method and PSF to acquire relative truth value of heterogeneous surface. Firstly, the size of variance and clustering window are confirmed based on the pixel scale information of the satellite product to be validated. Secondly, the PSF suitable for heterogeneous surface is selected from 5 PSFs. Thirdly, the IPSF is constructed according to the MPVW and the PSF suitable for heterogeneous surface. Finally, the IPSF is used to acquire relative true value at pixel-scale of heterogeneous surface from airborne hyperspectral image. This study shows: (1) Good correlation between relative true value obtained using IPSF and reference value is indicated as R2 values among 256 channels reach 0.985, structural similarity (SSIM) ranges from 0.992 to 0.998, and peak signal noise ratio (PSNR) more than 35dB. (2) Better accuracy performance is observed in the acquisition of relative truth value of heterogeneous surface for IPSF than PSF. Compared with conventional PSF, the R2, PSNR and SSIM of the result of IPSF are increased by about 1.34%, 9% and 0.7% on the average. (3) Significant advantage of IPSF is also reported in reducing the deviation compared to PSF, as the average root mean square error (RMSEave) is reduced by 30.37% and the average mean absolute error (MAEave) is reduced by 35.98%, respectively. (4) Comparing the RMSE and MAE of PSF and IPSF result, the RMSE and MAE of the result of IPSF become smaller. The RMSE decreases from 3 ~ 195 to 2 ~ 131. The MAE changes from 2 ~ 138 to 1 ~ 89. Overall, the IPSF proposed in this paper can effectively calculate the relative true value of heterogeneous surface, which can provide reference for the validation of multi-spectral and hyper-spectral satellite products.
LOLA DEM Assisted Photogrammetric Processing of Lunar Reconnaissance Orbiter NAC Images for the Lunar South Pole
Authors: Liu Pengying , Geng Xun , Wang Yuying , Zhang Jiujiang
Published: May 2024

Abstract. Because of its special significance, exploration missions and research at the Lunar South Pole (LSP) are of considerable importance. The mapping products of the LSP such as Digital Ortho Maps (DOMs) and Digital Elevation Models (DEMs) are essential to support exploration missions. However, in the LSP region, the available data are relatively limited. The large amounts of shadows and the special environment of the LSP has an important impact on both image matching and generation of mapping products. In this paper, we propose a photogrammetric processing method to improve the accuracy of the LRO NAC images by using LOLA DEM as auxiliary control data. The LOLA DEM was first rendered using the same illumination conditions as the corresponding LRO NAC raw images, and then matched to the LRO NAC DOMs. Next, the corresponding elevations of the tie points between LOLA DEMs and LRO NAC images were extracted and automatically added to the control network for bundle adjustment. In order to verify the feasibility of the method, we selected 17 LRO NAC images near the LSP at 85° south latitude. The results show that the proposed method can efficiently acquire uniformly distributed elevation control points for bundle adjustment and the generated DEM has a good consistency with the LOLA DEM.
The Progress/Status of Ecological Assessment on the Intensive Land Use in Selenge and Darkhan-Uul Province, Mongolia
Authors: Lkhamjav Ochirkhuyag , Batbold Uyanga , Renchinmyag Delgertsetseg , Ulziibat Bilguun , Enkhtaivan Uuriintsolmon , Erdenegerel Ariunbold , Borkhuu Oyundari , Tsogtbaatar Solongo , Akhmadi Khaulanbek
Published: May 2024

Abstract. The study area, it includes Darkhan-Uul and Selenge provinces of Mongolia, is included in the most favourable natural-geographical areas, and the migration of people with livestock from the peripheral areas has led to an increase in the population, as well as a sharp increase in the number of grazing animals, resulting in the effects of natural and human activities. give an evaluation, develop the basis for the proper use of the land in the future. In Mongolia, the methods and principles of land evaluation differ depending on the general classification and purpose of land, so considering these characteristics, land evaluation is carried out by (1) the Department of Agriculture, (2) the Department of Urban Development, Industry and Mines, (3) the Department of Roads and Networks, (4) it is divided into types of land with forest reserves (Tserenbaljir, B. Naranchimeg, 2004). In the "instructions for land evaluation" issued by the United Nations Food and Agriculture Organization (FAO) in 1976, in the assessment of land quality, in addition to the main indicators of soil fertility and moisture, climate, land cover and use, chemical pollutions such as an alkaline acidity. In the ecological assessment study, data on land use, soil, vegetation, climate, natural conditions, resources, socio-economic, satellite and field studies were collected in numerical and tabular form. The Ecological assessment in intensive land use is divided into qualitative and quantitative assessment. The Qualitative assessment predicts ecological properties. The Quantitative methods use multi-species numerical methods to record ecological elements and calculate the overall percentage of ecological characteristics.
Monitoring the spread of a pathogenic insect on vineyards using UAS
Authors: Longhi Valeria , Martino Alessio , Lingua Andrea Maria , Maschio Paolo Felice , Belcore Elena
Published: May 2024

Abstract. Globalisation has contributed to rapid economic growth but has also exposed vulnerabilities such as the spread of pests in agriculture. An example is the Popillia Japonica Newman beetle, introduced to Italy in 2014, which has caused significant economic losses, mainly affecting vine cultures. Reliable identification of pests is essential for its management, but it is time-consuming and laborious. This has prompted growing interest in image-based methods, supported by computer vision (CV), which can significantly improve efficiency in insect detection. This study aims to evaluate a CV algorithm's effectiveness in identifying adult specimens of Popillia using Near-Infrared sensors on Uncrewed Aerial Systems (UAS). The project, conducted in two vineyards in northern Italy, intends to establish a replicable and standardised data acquisition protocol for future monitoring activities. Insects detected by the CV-based method are validated by manual counting performed by entomologists. In a GIS environment, prescription maps are generated in near real-time to identify where the vineyard is most affected and to guide the drone spraying treatment only on the areas in which the threshold is exceeded. The study demonstrates effective semi-automated monitoring, with a clear correlation between CV-based and manual insect measurements, as indicated by the Pearson correlation coefficients ranging from 0.89 to 0.96. Although the CV-based method may overestimate insect numbers, it provides valuable insights for targeted pest management interventions and damage assessment. The project outcomes offer a promising approach to safeguarding agriculture against invasive species, enhancing regional economic resilience while minimising the spread of insecticide, the required time, and human interaction with harmful substances.
Comparison between DInSAR and Stacking in Deformation Monitoring Using LuTan-1
Authors: Lu Jing , Tang Xinming , Li Tao , Zhang Xiang , Zhang Xuefei , Li Tan , Qiu Xiaofeng
Published: May 2024

Abstract. LuTan-1 (LT-1) satellite constellation is used to conduct surface deformation monitoring. Following the successful launch of two satellites, we began to use the independently developed LandSAR software for differential interferometric data processing and application at the beginning of 2022. In this paper, we tested the LandSAR software by assessing the DInSAR and stacking modules. We used 21 LT-1 single look complex (SLC) images from December 2022 to May 2023 to conduct DInSAR and stacking processing of Datong City, Shanxi Province. DInSAR revealed more subsidence funnels and larger relative deformation values compared to stacking. We tested the DInSAR technology with different parameter combination. Results show that multi-looking factor is the most important to detect the deformation details because of the phase gradient limitation of DInSAR. Compared multi-looking ratios of 2×2 and 4×4, the subsiding funnels that were detected decreased from 60 to 47. After deep testing and assessment, we found that the linear model used by stacking affects the result severely, meaning that most of the subsidence of coal mines were not linear. Stacking only found 32 subsidence funnels which were active coal mines during our observation time. The test result show that LandSAR was compatible for DInSAR and stacking. And it is useful to observe linear and nonlinear deformation in the coal mine regions.
Affordable Sensors for Speditive and Accurate Documentation of Built Heritage: First Tests and Preliminary Results
Authors: Martino Alessio , Lingua Andrea Maria , Maschio Paolo
Published: May 2024

Abstract. When facing unregulated human interventions, the tumultuous backdrop of wars, and the relentless impact of climate change, a significant effect is held on those structures that stand as custodians of our cultural identity and historical legacy. The need to document their status is an essential first step toward preservation and restoration. This contribution will analyze and compare diverse datasets acquired through three distinct methods: Terrestrial Laser Scanner, Apple’s iPhone 15 Pro with no additional tool, and iPhone 15 Pro using Pix4D viDoc RTK Rover to assess the quality and accuracy of point clouds, as well as to determine the feasibility of each technique for the expeditious documentation of endangered built heritage. The use of mass-distributed low-cost sensors provides a rapid and cost-effective means to capture detailed 3D models of built heritage while, at the same time, democratizing the process of documentation. Low-cost sensors not only facilitate documentation but also enhance the efficiency of conservation efforts. The precision of LiDAR sensors helps identify structural vulnerabilities, allowing conservationists to prioritize interventions and allocate resources wisely. Furthermore, detailed imagery will enable conservationists to monitor subtle structural changes over time, acting as an early warning system against potential threats. As an additional aid, the RTK Rover enables centimeter-level positioning accuracy while maintaining a compact and fast deployable design, and the direct measurement of Ground Control Points (GCPs) significantly streamlines the surveying process and equipment footprint.
Cascaded framework for earthquake building damage detection combining spatial and frequency domain feature integration
Authors: Ming Dongping , Xie Shizhe , Dong Dehui , Zhang Jing
Published: May 2024

Abstract. Building collapse is a major cause of casualties after an earthquake, so accurately extracting building damage information is critical for post-earthquake assessment and rescue. Currently, most deep learning methods focus on the end-to-end detection of building collapse. However, in real-world earthquake scenarios, the end-to-end computational process often lacks flexibility and struggles to meet the requirements of rapid emergency response. To address this issue, this paper proposes a cascaded framework that combines pre-earthquake building extraction and post-earthquake building damage classification. The proposed framework includes two sections: (1) Progressive building semantic segmentation model in the joint frequency domain. This model is designed to accurately extract buildings prior to an earthquake, with the goal of minimizing error propagation throughout the cascading process. The model addresses the spatial similarity of buildings under complicated backgrounds, as well as the high internal heterogeneity of buildings, by utilizing frequency domain techniques. It compensates for the shortcomings of traditional models in terms of incomplete information extraction through the effective integration of global and local information. Finally, the model employs edge priors for edge regularization. (2) Rapid building damage classification process. Based on the accurate building extraction results, a fast and efficient classification process is developed. This process uses a simple and lightweight classification network to effectively extract building damage information caused by the earthquake. The superiority of the proposed framework is validated through comparison with traditional cascading architectures and end-to-end models. The results show that the cascading framework not only provides accurate pre-earthquake building extraction, but also enables efficient and accurate post-earthquake damage classification, which meets the requirements of rapid post-earthquake emergency response. This balance of accuracy and speed is essential for effective disaster management and recovery.
Autonomous Landing Spot Detection for Unmanned Aerial Vehicles Based on Monocular Vision
Authors: Cao Yanan , Li Minglei , Xu Li , Wei Mingqiang
Published: May 2024

Abstract. Enhancing the autonomous landing capability of unmanned aerial vehicles (UAVs) is of great significance for improving their operational efficiency and field survival capabilities. To this end, we propose a real-time autonomous landing spot detection method for UAVs. Firstly, the pose of the UAV at any given time and the initial three-dimensional point cloud of the scene are estimated using simultaneous localization and mapping (SLAM) techniques. Then, since the initial point cloud is sparse and cannot be used for terrain analysis, we generate a voxel-based elevation map of the scene, which can establish interconnectivity among the points. Finally, we propose a shift-box strategy to comprehensively analyze various terrain factors in the elevation map, determine the landing spot for UAVs, and update the landing spot in real time. The UAV flight experiments conducted in the real world have demonstrated the effectiveness and real-time performance of the proposed method.
Comprehensive Vibration Analysis and Modeling for ZY-3 Satellite's Lifecycle
Authors: Mo Fan , Xie Junfeng , Wang Shuyi , Zhao Liping , Li Xiang , Xia Haoran , Shang Yongjie
Published: May 2024

Abstract. A stable platform environment is essential for achieving high-quality imaging and accurate attitude measurement of the ZY-3's stereo camera. It serves as the fundamental guarantee for the satellite's high geometric precision. Throughout its orbital operation, the platform encounters periodic vibrational occurrences resulting from internal load mechanics and significant external environmental variations, thereby impeding the high-fidelity collection of information by the satellite payload. The detection and modeling of vibrations are crucial tasks for the satellite ground system, as they provide reliable parameter inputs for optimizing attitude accuracy and image quality. This paper presents a method for developing a comprehensive frequency variation vibrational model of the ZY-3 satellite platform throughout its lifecycle, utilizing the inertial gyroscopes (gyros) data of the ZY-3 satellite. The instrument has identified vibrations at three specific frequencies: 0.21 Hz, 0.56 Hz, and 1.12 Hz, each with amplitudes of 0.07″, 0.04″, and 0.04″, respectively. Additionally, it has detected two fluctuating frequencies around 0.3 Hz and 0.6Hz. The period of frequency variation is approximately one year, with vibration ranges of (0.267, 0.299) and (0.603, 0.695). Following a malfunction of the solar panels on the satellite, there has been a shift in the vibration range. Moreover, as the satellite's on-orbit duration increases, this range is also undergoing gradual changes.
Research on the Effectiveness of Lidar Survey for Large Mapping
Authors: Nekhin Sergey , Rubenok Alexey , Kovrov Alexandеr
Published: May 2024

Abstract. The analysis of the time spent on the processes and the complex of works on topographic mapping in accordance with production projects is given. The projects were carried out according to traditional technological schemes of work and technological schemes using airborne laser scanning materials to generate digital topographic plans at scale the 1:2000 based on the use of unmanned aircraft. The comparative performance of the technologies was determined based on taking into account the time spent for field and in-house work. When using air laser scanning data in addition to air photography, there is an increase in work productivity.
Estimating Forest Stock Volume Based on Airborne Lidar Data
Authors: Niu Xiao , Jiang Na , Hou Ke , Yin Yuan
Published: May 2024

Abstract. Forest stock volume (FSV) stands as an important indicator in evaluating the potential for carbon sequestration. It is crucial for forest resource management at local, regional, and national scales. In order to achieve an accurate estimation of FSV, this article takes Mengyin County, Shandong Province, China as the research area, builds a random forest (RF) model for four tree species based on airborne Lidar data, and forms a monitoring system of "individual tree - grid - county" granularities. The results demonstrated that all four models exhibited excellent generalization capabilities, with no signs of overfitting. In the test phase, the R2 of the poplar and pine models exceeded 0.9, while the R2 of the cypress model was 0.81, and the rRMSE was controlled within 20%, indicating that the fitting effect of the three tree species models was better; the accuracy of the robinia pseudoacacia model was relatively poor, with R2 of 0.60 and rRMSE of 20.60%. This study provides a feasible method for estimating forest stock volume within the county, which provides strong technical support for forest resource management and planning, and helps promote sustainable forestry development.
Dense collaborative mapping with deep visual SLAM method
Authors: Pan Peiwei , Zhang Wei , Haala Norbert
Published: May 2024

Abstract. The creation of highly accurate and collaborative mapping algorithms is crucial for the progress of SLAM technology, as it greatly improve the efficiency of building detailed maps. In the area of mapping based on single moving trajectory, DROID-SLAM (Differentiable Recurrent Optimization-Inspired Design) by (Teed and Deng, 2021) stands out as an innovative method based on deep learning, providing a visual-only solution that works with various types of camera, such as monocular, stereo, and RGB-D. Its ability to create maps with excellent accuracy makes it superior to well-known methods like ORB-SLAM3 by (Campos et al., 2021). Despite its impressive individual mapping performance, DROID-SLAM does not account for scenarios involving multisession data or the collaborative map creation by multiple agents. To address this problem, we propose two collaborative map construction algorithms built upon DROID-SLAM. Compared to prior methods that compute explicit relative transformations for loop closures, our algorithm leverages the power of deep learning-based bundle adjustment, using dense per-pixel correspondence, to merge into a globally consistent state. These algorithms have been thoroughly tested with stereo and RGB-D models. we validated the effectiveness of our proposed algorithms on both public and self-collected datasets, showing higher accuracy than prior methods. By leveraging the strengths of DROID-SLAM while addressing its limitations with our novel algorithms, we extend the application scenarios of this method and provide a new way of thinking about collaborative mapping.
A Long-time-series Spatio-Temporal-Spectral Fusion Method via Multi-task Learning
Authors: Peng Mingyuan , Li Canhai , Zhou Xiaoqing , Li Guoyuan
Published: May 2024

Abstract. Due to the limitations of sensor hardware, clouds and fog, and data transmission limitations, it is difficult for the data obtained by spaceborne remote sensing imager to achieve high temporal, spatial and spectral resolution at the same time, which limits its application in long-time-series high-frequency monitoring. At present, there are several spatio-temporal-spectral algorithms that can realize the fusion of temporal, spatial and spectral resolution, but most of them are based on one to two discrete images, and the integrated fusion at the multi-dimensional level has not yet been realized. There is currently no research on the spatio-temporal-spectral fusion method based on LONG-TIME-SERIES multi-scene remote sensing data. Aiming at solving the bottleneck of spatio-tempora-spectral resolution of remote sensing data, this study proposes a new long-time-series spatio-temporal-spectral fusion method based on multi-task learning to realize the multi-dimensional optimization of multi-source remote sensing data resolutions. Experiments used simulated and real datasets, both of which contain 4 images of 10m ZY1-02D multispectral data, 7 images of 16m GF-6 multispectral data and 4 images of 30m ZY1-02D hyperspectral data, and obtained 7 images of 10m hyperspectral data. The experiments The results show that our method performs the best compared to other methods. This method can provide effective data support for applications based on long-time series remote sensing data.
Surface topography modelling and ice flow velocity mapping of Dalk Glacier, East Antarctica: application of UAV remote sensing
Authors: Cao Zheyi , Tang Leyue , Yuan Xiaohan , Qiao Gang
Published: May 2024

Abstract. The Antarctic Ice Sheet is the largest potential contributor to global sea level rise due to global climate warming, making it important to monitor changes in the marginal outlet glacier dynamics and surface topography. The unmanned aerial vehicle (UAV), a new platform, has been proven to be a useful tool in glaciological applications because it can collect data in a variety of ways and has a high spatial and temporal resolution compared to traditional ground-based measurements and remote sensing from space. In this study, we used the combined UAV platform composed of the DJI Mavic 3 Enterprise (M3E) and M300 (L1) versions to perform high-accuracy on-site investigations of Dalk Glacier, a typical outlet glacier near the Zhongshan Station in East Antarctica. During the 39th Chinese Antarctic Expedition (CHINARE) in 2022–2023, UAV surveys with multiple sensors were performed. The M3E optical camera makes it possible to reconstruct high-resolution ortho-mosaic and digital elevation models (DEMs) of Dalk Glacier with high accuracy based on photogrammetric principles, helping us to extract the features of glacier surface topography, while dense point clouds derived from the L1 LiDAR camera are used to validate and improve the three-dimensional accuracy. To further monitor the dynamics and stability of Dalk Glacier, we generate ice flow velocity maps using ortho-mosaics for the years 2019, 2020, and 2023. The spatiotemporal changes in ice flow velocities were further compared with the products derived from satellite remote sensing data.
Multi-scale Target Detection Algorithm of Optical Remote Sensing Image Based on Improved YOLOv8
Authors: Wang Haoyu , Yang Haitao , Wang Jinyu , Zhou Xixuan , Xu Yifan , Zhang Honggang
Published: May 2024

Abstract. With the progress of remote sensing sensors, the quality of optical remote sensing image is significantly improved, and target detection on it can extract rich feature information. However, due to the characteristics of remote sensing image with various target sizes and a large proportion of the number of small targets, increasing the difficulty in target detection for it. In response to this challenge, this paper proposes an improved YOLOv8 algorithm for multi-scale target detection of optical remote sensing images. First, we propose a PSPPF module, which improves the model's ability to adapt to different data distributions; Second, DSConv is introduced into the Backbone structure of YOLOv8 to reduce the complexity of the network while maintaining the performance of model detection; Finally, we replace the original loss function CIoU with MPDIoU to improve the localization accuracy of the prediction box. Applying the improved algorithm to the public dataset NWPU VHR-10, the mAP value of the our algorithm is 95.1%, which is 3.0% higher than that of the original YOLOv8, indicating that the proposed algorithm is able to effectively detect multi-scale targets in optical remote sensing images.
Spatiotemporal coupling analysis of surface water and groundwater in Tibet based on multi-source sensors
Authors: Wang Mengran , Yao Jiaqi , Mo Fan , Xu Nan
Published: May 2024

Abstract. Lakes and groundwater are two crucial components of the global terrestrial water cycle, collectively forming a vital network for Earth's water resources. However, in the Tibetan region, the spatiotemporal relationship between lakes and groundwater and their impact on the local hydrological cycle remain inadequately understood. Satellite remote sensing serves as an effective observational tool, enabling comprehensive investigation and analysis of surface lakes and groundwater in Tibet with high spatial resolution. Therefore, this study integrates Landsat and Cryosat-2 satellite data to examine the spatiotemporal patterns of surface lake extents and water levels in Tibet. Additionally, combining Gravity Recovery and Climate Experiment (GRACE) satellite observations with the Global Land Data Assimilation System (GLDAS) model data, we quantitatively analyze the spatiotemporal variations in groundwater storage and its correlation with lake water. The results indicate that: 1) Rivers and lakes in Tibet are mainly located in the central and northwest regions, displaying noticeable intra-annual variations; 2) Substantial lagged relationships exist between groundwater storage and lake water levels and areas, revealing that lakes contribute significantly to groundwater replenishment, especially in the Ngari prefecture and Lhokha prefecture. This study comprehensively utilizes multi-source remote sensing data to dynamically monitor surface and groundwater in Tibet, providing robust support for a better understanding of the interaction between groundwater and surface water.
Underground Target Extraction by Local Entropy Feature of Voxelized 3D Ground Penetrating Radar
Authors: Wang Wenlong , Hu Qingwu , Zhang Ju , Zhao Pengcheng , Ai Mingyao
Published: May 2024

Abstract. To solve the current problem of insufficient exploration of three-dimensional spatial information detected by 3D ground penetrating radar (3D GPR) and the data processing mainly based on the analysis and interpretation of two-dimensional slice images, a method is proposed to extract underground target based on the local entropy feature of discrete point clouds after the voxelization of 3D GPR data. First, the acquired 3D GPR data was voxelized into discrete three-dimensional point clouds. Then the local entropy feature of the voxelized point clouds over the entire region were calculated. The soil background and underground targets were distinguished by classifying them from multiple dimensions through Support Vector Machine (SVM). Finally, the urban road underground environment was taken as the research object, and this method was used for experimental analysis using measured data. The experimental results show that the accuracy of this method in extracting underground targets is as high as 90.1%, and the missing detection rate of missing underground targets is as low as 7.8%. The proposed method is accurate and effective, providing a new approach for 3D GPR to extract underground target.
Visual Reinforcement Learning for Dynamic Object Detection
Authors: Wang Xiangsheng , Hu Xikun , Zhong Ping
Published: May 2024

Abstract. Object detection is a widely studied task in computer vision. Current methods often focus on images captured from appropriate viewpoints. However, there is a large disparity between objects observed from different viewpoints in the real world. Dynamic Object Detection (DOD) method automatically adjusts the camera viewpoint in a visual scene to sequentially find optimal viewpoints. Currently, the DOD tasks are usually modeled as a sequential decision-making problem and solved using reinforcement learning methods. Existing approaches face challenges with sparse rewards and training instability. To tackle these issues, we proposed a single-step reward function and a lightweight network, respectively. The single-step reward function, which provides timely feedback, gives an efficient training process for DOD tasks. The lightweight network with few parameters can ensure the stability of the training process. To evaluate the effectiveness of our method, we developed a simulation dataset based on UE4, which consists of 1800 training images and 450 testing images. The dataset includes five object categories: vans, cars, trailers, box trucks and SUVs. Experiments demonstrate that our method outperforms SOTA object detectors on our simulation dataset. Specifically, the average precisions(APs) are improved from 89.1% to 96.0% when using the YOLOv8 object detector.
Analysis of Multiple Scattering Characteristics of Cable-Stayed Bridges with Multi-band SAR
Authors: Xu Yanhao , Wen Yangmao , Li Tao , Ma Sijie , Liu Jie
Published: May 2024

Abstract. Accurate localization of multi-scattering features of cable-stayed bridges in multi-band Synthetic Aperture Radar (SAR) imagery is crucial for intelligent recognition of bridge targets within images, as well as for precise water level extraction. This study focuses on the Badong Yangtze River Bridge, utilizing Unmanned Aerial Vehicle (UAV) LiDAR data of the bridge, and analyzes the multi-scattering characteristics of different bridge structural targets based on Geometric Optics (GO) methods and the Range-Doppler principle. Furthermore, the study integrates LiDAR data of the bridge's cable-stays to examine their multi-scattering phenomena, finding that the undulations of the Yangtze River's surface waves significantly contribute to the pronounced double scattering features of the bridge's cable-stays. Additionally, statistical analysis of multi-source SAR data indicates that this phenomenon is not directly correlated with radar wavelength, implying no direct connection to surface roughness. Utilizing LiDAR point cloud data from the bridge's street lamps, this paper proposes a novel method for estimating water level elevation by identifying the center position of spots formed by double scattering from lamp posts. The results show that using TerraSAR ascending and descending orbit images, this method achieves a water level elevation accuracy of approximately 0.2 meters.
Mega-NeRF++: An Improved Scalable NeRFs for High-resolution Photogrammetric Images
Authors: Xu Yiwei , Wang Tengfei , Zhan Zongqian , Wang Xin
Published: May 2024

Abstract. Over the last few years, implicit 3D representation has attracted more and more research endeavors, typified by the so-called Neural Radiance Fields (NeRF). The original NeRF and some relevant variants mostly address on small-scale scene (such as, indoor or tiny toys), which already show good novel views rendering results. It still remains challenging when dealing with wide coverage area that is captured by large number of high-resolution images, the time efficiency and rendering quality is generally limited. To cope with large-scale scenario, recently, Mega-NeRF was proposed to divide the area into several overlapping sub-area and train corresponding sub-NeRFs, respectively. Mega-NeRF adopts the method of parallel training of multiple sub-modules, which means sub-modules are absolutely independent of each other, which might in principle not be an optimal solution, as two sub-NeRFs of adjacent sub-models obtained by parallel training are likely to get different rendering results for the overlapping area, and the final rendering result is supposed to be negative affected. Therefore, we present Mega-NeRF++, and our goal is to improve Mega-NeRF by implementing extra sub-models optimization that alleviate the rendering discrepancy of overlapping sub-NeRFs. More specifically, we further fine tune the original Mega-NeRFs by considering the consistency of adjacent overlapping area, which means the training data used in the optimization are only from the overlapping region, and we also proposed a novel loss, so that it not only takes into account the difference between the prediction of each sub-model and the true value, but also considers the consistency of the predicted results between various adjacent sub-modules in the overlapping region. The experimental results show that, for the overlapping area, our Mega-NeRF++ can qualitatively render better images with higher fidelity and quantitively have higher PNSR and SSIM compare to original Mega-NeRF.
Revealing Urban Deformation Patterns through InSAR Time Series Analysis with TCN and Transfer Learning
Authors: Yang Mengshi , Li Saiwei , Yu Hang , Wu Hao , Li Menghua
Published: May 2024

Abstract. Current multi-epoch InSAR techniques heavily rely on the assumption of linear deformation. This can sometimes overlook crucial deformation signals when using velocities for evaluation. The process of interpreting InSAR time series is not only time-consuming and labor-intensive but also requires a certain level of expertise. This study refines existing InSAR deformation categories, such as stable, linear, step, piecewise linear, power, and undefined, to define 'canonical deformation time series patterns.' We propose an innovative approach for InSAR post-processing using Temporal Convolutional Networks (TCN) and transfer learning. Due to the limited availability of real data, we use simulated data to train a pre-existing model. We then assess the effectiveness of our method in identifying urban deformation patterns. This research could significantly improve our understanding of large-scale InSAR time series deformation and reveal the underlying patterns.
Study on Fault Monitoring Technology of Photovoltaic Panel Based on Thermal Infrared and Optical Remote Sensing
Authors: Zhang Wei , Wang Guanghui , Yao Guoqing , Lu Chen , Liu Yu
Published: May 2024

Abstract. Rapid access to the operating status of Photovoltaic (PV) panels and troubleshooting can save management and maintenance costs for the development of PV power plants, which is important for PV power plant management and power generation capacity assurance. The use of remote sensing technology to identify the faults of photovoltaic panels has developed rapidly, however, current research usually relies only on a single optical data source to identify and count the area of PV panels in a PV electric field, although there are literature on PV panel fault detection, only the surface fault identification of PV panels is tested, while the internal faults (such as panel bad points or bad lines) cannot be identified because of the limitations of optical remote sensing. In this paper, a photovoltaic panel fault monitoring technology based on multi-source remote sensing is proposed. The optical and thermal infrared hybrid data combined with deep learning technology are used to achieve rapid and accurate fault identification and localization of PV panel arrays. It can not only automatically identify PV panels that are obscured by dust and foreign objects, but also locate PV panels that have bad dots or bad lines, which greatly improves the ability and effectiveness of remote sensing PV panel fault monitoring. The high-resolution unmanned air vehicle (UAV) optical image and thermal infrared image are applied in this experiment. The Mask RCNN algorithm is used to accurately locate and number the photovoltaic panel of the optical image. Then, the fault scene classification model is established for the multi-type fault characteristics of the optical image and thermal infrared image within the panel range, so as to identify five types of faults, such as dust cover, branch cover, bird droppings cover, internal bad points and bad lines of PV panel, which effectively solves the problem that the single optical remote sensing image cannot identify the internal component faults of the photovoltaic panel under normal conditions.
LT-1 SAR Satellite Constellation for Permafrost Deformation Monitoring Along the Tibetan Plateau Engineering Corridor
Authors: Zhang Xuefei , Li Tao , Zhang Xiang , Zhou Xiaoqing , Lu Jing , Zhang Xueguang
Published: May 2024

Abstract. The Tibetan Plateau stands as one of China's largest middle and low latitude permafrost regions. However, the effects of global warming and human activities have led to permafrost thawing, inducing surface instability and posing significant threats to infrastructure and indigenous communities. The deployment of Lu Tan-1 (LT-1), China's premier L-band synthetic aperture radar (SAR) satellite constellation, offers a novel opportunity to assess these changes. This paper evaluates the deformation of critical engineering corridors, such as the Qinghai-Tibet Railway (QTR) and the Qinghai-Tibet Highway (QTH), utilizing time-series InSAR techniques with LT-1 SAR constellation data. We introduce both Stacking InSAR and a multi-baseline persistent scatterer multitemporal (MT-InSAR) method to enhance permafrost and engineering corridor deformation detection capabilities. Results obtained through the MT-InSAR approach reveal line-of-sight (LOS) deformation velocities of permafrost in the Beiluhe region ranging from -90 mm/y to approximately 70 mm/y, with an average velocity amplitude of 0.06 m/y. Differential displacement between alpine meadows and alpine deserts across the Beiluhe region is successfully discerned using LT-1 SAR data. Deformation velocities of QTR, QTH were found to be lower than that of permafrost, with average velocities of 0.027 m/y. These findings underscore the LT-1 SAR constellation's potential to serve as a valuable SAR data source for monitoring engineering corridor deformation within the Tibetan Plateau permafrost region.
Geometric accuracy evaluation and analysis of ZY-1 02E IRS thermal infrared image data using GCP extraction based on phase correlation matching method
Authors: Zhao Liping , Dou Xianhui , Mo Fan , Li Hongmo , Zhang Fangxu , Qu Dian , Xie Junfeng
Published: May 2024

Abstract. The Ziyuan-1 (ZY-1) 02E launched on December 26, 2021 is equipped with a thermal infrared sensor (IRS), which has a ground resolution of better than 16m and a width priority of 115km, balancing the advantages of high resolution and large wide observation. The geometric performance of image data is the premise of remote sensing application, and the difficulty in evaluating the geometric performance of thermal infrared image data lies in the extraction of well-distributed, reliable and accurate GCPs. To extract GCP from high-precision reference images, it is necessary to overcome the feature differences between images caused by different spectral responses. This paper adopts a phase correlation matching method based on frequency domain to realize the fine registration of the data obtained by the emission thermal spectral band with the data from the reflectance spectral band, which can not only solve the GCP extraction of conventional thermal infrared images collected during the day, but also obtain satisfactory GCP data from thermal infrared data acquired at night. In order to test the GCP method proposed in this paper, three typical areas are selected as the experimental areas, including Yiyang City in Hunan, Nagqu City in Xizang and Hami City in Xinjiang, and the internal geometric accuracy and absolute geolocation accuracy of the thermal infrared data spanning one year are evaluated and analyzed by using the reference data composed of the DOM with an accuracy of 2m and the DEM with an accuracy of 10m. The research results indicate that the internal geometric accuracy of ZY-1 02E IRS satellite image data is better than 1.0 pixels, and the performance is satisfactory. However, its absolute geolocation accuracy needs to be continuously improved, especially there are systematic errors in the ascending data at night that require further research. Overall, it meets the design accuracy indicators of satellites and can meet the application requirements of thermal infrared remote sensing.