Table of Content

26 November 2024, Volume 53 Issue 10

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Review

The transformation of the scientific concept of GIS: from Map-based GIS to Space-oriented GIS

Renzhong GUO, Yebin CHEN, Zhigang ZHAO, Ding MA, Biao HE, Weixi WANG, Wuyang HONG, Minmin LI

2024, 53(10):  1853-1862.  doi:10.11947/j.AGCS.2024.20240152.

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From the 1960s to the present, GIS has undergone a development journey of over 60 years, during which its connotations have evolved from geographic information system (GISystem) to geographic information science (GIScience), and further to geographic information service (GIService). During this period, GIS research has primarily been based on the two-dimensional abstract expression logic of cartography (Map-based GIS), achieving abstract analysis and representation of the real world. However, with the continuous emergence of new technologies and new demands such as 3D real scene, digital twins, and city information modeling (CIM), the original two-dimensional logic of GIS is facing challenges in the collection, processing, and fusion of multi-source heterogeneous spatiotemporal big data, the representation of complex spatiotemporal dynamic processes, and the mining of potential spatiotemporal patterns. How to adjust the scientific positioning of GIS to adapt to the multi-type, multi-level, and multi-role needs of spatial object expression and analysis in the digital society has become an important issue that GIS development urgently needs to consider. From a methodological perspective, we deeply analyzes the bottlenecks of Map-based GIS in spatial representation, spatial analysis, and comprehensive application. Furthermore, based on the development needs of GIS in the new era, we propose a scientific concept transformation model from Map-based GIS to Space-oriented GIS, integrating the logical thinking of Map-based GIS from the perspectives of theoretical foundations, management models, visualization forms, and functional positioning, and innovative applications under the transformation of GIS scientific concepts. This research aims to provide reference ideas for the development of GIS in the new era.

Major Satellite Surveying and Mapping Project “LuTan-1”

In-orbit application parameters test and analysis of L-band differential interferometric SAR satellite constellation

Xinming TANG, Tao LI, Xiang ZHANG, Xiaoqing ZHOU, Jing LU, Xuefei ZHANG

2024, 53(10):  1863-1872.  doi:10.11947/j.AGCS.2024.20230240.

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After one year in-orbit test of the China's first L-Band differential interferometric SAR (L-SAR) satellite, which is also named as LuTan-1, we finally conclude that the satellite has reached the pre-defined accuracies. In this paper, we analyzed the key technologies that related to the parameters of the quantitative applications. Three kinds of parameters belonging the long-time geolocation capacity, the digital surface model (DSM) accuracy, as well as the deformation products accuracy are introduced. Results show that the geolocation error of L-SAR is within 3.9 m. We obtained the DSM calibration and validation data in Henan and Jiangsu Provinces. DSM accuracies before interferometric calibration, after calibration and after least square estimation are 13.2, 6.3 and 2.9 m, respectively. We have defined three kinds of deformation products, i. e., deformation products generated using differential InSAR, stacking and multi-temporal InSAR. Accuracies of the three deformation products obtained in Datong of Shanxi Province are 2.7 mm, 8.6 mm/a and 3.7 mm, respectively. Meanwhile, 90% of the strict regression orbit radius was tested to be smaller than 323 m. The components of the decoherence sources were discussed. Final coherence before filtering is better than 0.7 when ignoring the temporal decoherence. The in-orbit test result is encouraging and the satellite will be widely used after its delivery from satellite producers to the users.

An strictly-regressive orbit optimization algorithm for L-band differential interferometric SAR satellite

Nan LI, Junjian WEN, Yanyang LIU, Huixiang LING, Chun WEI, Junli CHEN

2024, 53(10):  1873-1880.  doi:10.11947/j.AGCS.2024.20230250.

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Compared with the traditional application direction of SAR satellites, the first generation of China's L-band differential interferometric SAR (L-SAR) satellite is mainly used to realize global accurate surface deformation measurement. In order to achieve the deformation measurement index of heavy-orbit differential interferometry better than 5 cm, it is necessary to solve the engineering problem of high-precision regression of satellite reference orbit. In this paper, a precision reference repeating orbit optimization design method is proposed. The high-order dynamic model is established by analyzing the impact of gravity field with different complexities on the precision of regression orbits; The modified sun-synchronous regression orbit parameters achieved by taking into account J4 perturbation are used as the initial value for the algorithm, and carry out the orbit optimization by using a heuristic multi-objective evolutionary algorithm with the goal of satisfying the convergent domain value of the satellite's position and velocity regression accuracy in the WGS-84 coordinate system. Simulation tests show that the regression accuracy of the optimization results can reach centimeter level, which is better than the established engineering indexes, and the validity and correctness of the method have been verified by L-SAR satellites in orbit.

Key technologies for spaceborne SAR payload of LuTan-1 satellite system

Yunkai DENG, Yu WANG, Kaiyu LIU, Naiming OU, Dacheng LIU, Heng ZHANG, Jili WANG

2024, 53(10):  1881-1895.  doi:10.11947/j.AGCS.2024.20230263.

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LuTan-1 (referred as LT-1) is China's first civil synthetic aperture radar (SAR) satellite mission to monitor the ground deformation with high precision by differential interferometry technology. The LT-1A and LT-1B have been success-fully launched on January 26 and February 27, 2022, respectively. The data acquisition schedule of LT-1 mission is divided into two stages, which corresponding to two specific orbit configurations. In the first stage, two satellites fly in a compact formation to get the digital elevation model (DEM) using the bistatic InSAR strip mode. In the second stage, both satellites fly in a common reference orbit with 180° separation. The revisit time of the individual satellite is 8 days, and it can be reduced to 4 days with two satellites. LT-1 satellite constellation can stably obtain time series data, so that we can monitor the ground deformation with high precision. Moreover, the multi-mode polarimetric payload will be utilized to obtain single-pass multi-polarimetric InSAR and hybrid polarimetric SAR data for forestry, land resource surveys, disaster monitoring, etc. In this paper, the key technologies of the LT-1 SAR payload, including phase synchronization, ambiguity suppression and system calibration, are systematically described and analyzed.The maximum resolution of LT-1 is 3 m, and the maximum swath width is 400 km, respectively. The azimuth ambiguity-to-signal ratio (AASR) of the interference wave position is better than -20 dB.The performance is partially demonstrated by ground testing and on-orbit actual measurement data.

Mitigation of radio frequency interference signatures and image quality enhancement for L-band differential interferometric SAR satellite images

Mingliang TAO, Jieshuang LI, Yanyang LIU, Junli CHEN, Yifei LIU, Jiawang LI

2024, 53(10):  1896-1909.  doi:10.11947/j.AGCS.2024.20230272.

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LuTan-1 (LT-1) is China's first L-band differential interferometry synthetic aperture radar (SAR) satellite with land monitoring as its core mission. It achieves high-precision, all-day, and all-weather surface deformation monitoring, and topographic mapping by twin satellites in constellation and in formation, respectively. The LT-1 satellite operates in the L-band, where radio services are relatively congested and crowded, and the resulting radio frequency interference (RFI) poses a significant challenge to accurate remote sensing. RFI will lead to image quality degradation, phase distortion, and coherence bias, which will directly affect the inversion accuracy of deformation monitoring products, resulting in the waste of scientific data of earth remote sensing observation. e. In order to mitigate the adverse effects of the interference, this paper thoroughly analyzes the radio frequency interference characteristics in LT-1 satellite images. It proposes a reliable automatic interference detection and suppression method for LT-1 single-look complex images. Experiments on massive real measured LT-1 images verify that the proposed method can effectively suppress the interference artifacts, restore and improve the interferometry coherence. This method has been successfully integrated into the ground processing system of LT-1 in the Ministry of Natural Resources, providing quality support for the application-related land monitoring products.

A phase unwrapping method based on phase quality fusion estimation and information filtering

Yandong GAO, Nanshan ZHENG, Yansuo ZHANG, Shijin LI, Huachao YANG, Hefang BIAN, Qiuzhao ZHANG, Shubi ZHANG, Yu TIAN

2024, 53(10):  1910-1919.  doi:10.11947/j.AGCS.2024.20230247.

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The LuTan-1 (LT-1) L-band differential interferometric synthetic aperture radar (SAR) system is China's first fully polarized L-band civil SAR satellite constellation, which will extensively monitor and supervise natural resources.One of the main tasks of this satellite constellation is producing a digital elevation model (DEM). As we all know, phase unwrapping is one of the key factors affecting the accuracy of DEM inversion.However, the low accuracy of unwrapping results in areas with high noise and large gradient changes has always been a bottleneck for phase unwrapping. To address the problem, this paper proposes an iterative approach for information filtering and unwrapping that uses the phase quality fusion strategy. First, the coherence, phase gradient, noise component, and interferometric fringe edge of the interferogram are estimated and fitted to guide the unwrapping path to improve the precision of the PU result. Then, during the iteration process, a central differential information filter is used to obtain quasi-real-time state estimation of the unwrapped phase, improving the PU model's robustness. Finally, experimental verification was conducted through TerraSAR-X/TanDEM-X binary star and LT-1 A/B binary star SAR data, and the results showed that the proposed PU approach in this paper is approximately 65.3% more accurate than the statistical cost flow algorithm and about 13.6% more accurate than the unscented information filter unwrapping algorithm. In addition, the experimental results not only confirm the effectiveness of the proposed method but also demonstrate that the LT-1 helix bistatic formation can achieve the expected results for high-precision terrain reconstruction, providing a high-precision data source for the generation of DEM products covering the cloudy and rainy regions of China.

Baseline refinement and DEM accuracy analysis during the in-orbit test phase of LT-1 SAR

Xinyou SONG, Lei ZHANG, Tao LI, Baocheng LEI, Ruiqing SONG

2024, 53(10):  1920-1929.  doi:10.11947/j.AGCS.2024.20230540.

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Lutan-1 (LT-1) is the first L-band radar satellite constellation in China primarily utilized for surface deformation measurements.This study utilized SAR data from the LT-1 satellite over the Gansu region to analyze the spatial characteristics of phase ramps in differential interferograms caused by orbital errors. The findings indicated that significant errors in the estimation of parallel baseline component variation rates were the primary factor contributing to orbital phase ramps. In response to some limitations of existing methods, an iterative baseline refinement estimation method based on Fast Fourier Transform (FFT) is developed. Subsequently, three-dimensional surface reconstruction of the study area is conducted based on the corrected baselines. Finally, the quality of the LT-1 DEM product is comprehensively assessed and analyzed using SRTM, AW3D, and Copernicus digital elevation model. Results indicate that the new method effectively corrects residual baseline errors, with an approximately 16.64% improvement in accuracy compared to non-iterative FFT algorithms. The average deviations of the LT-1 DEM from SRTM, AW3D, and Copernicus DEMs are 5.49 m, 3.41 m, and 1.94 m, respectively, demonstrating outstanding elevation accuracy and timeliness advantages.

Analysis of InSAR time-series deformation monitoring accuracy of domestic satellite

Bing XU, Yan ZHU, Zhiwei LI, Huiwei YI, Miaowen HU, Qi CHEN, Kun HAN, Xun DU

2024, 53(10):  1930-1941.  doi:10.11947/j.AGCS.2024.20230572.

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The successful launch of the Lutan-1 satellite group (LT-1) has achieved the development of China's L-band interferometric SAR satellite from scratch. For obstacle avoidance, a small part of the spatial baselines of LT-1 satellite was long, but the length of the baseline has been controlled to within 400 meters after the orbit adjustment. In order to verify the availability and accuracy of LT-1 satellite data, this article takes the Datong mining area in Shanxi Province as an example and obtains 25 LT-1 strip pattern image data from December 23, 2022 to May 20, 2023, respectively, for SBAS-InSAR and PS-InSAR data processing. By comparing and analyzing the deformation monitoring results of time-series InSAR and GPS stations in the light of sight, the standard deviations of the two are 5.7 mm/a (SBAS-InSAR) and 3.4 mm/a (PS-InSAR), respectively. The root mean square error of the time series is less than 5 mm, indicating high consistency. The research has shown that domestically produced LT-1 satellites have high-precision deformation monitoring capabilities, providing reliable data assurance for domestic terrain surveying and deformation monitoring.

Remote Sensing Large Model

Multi-modal remote sensing large foundation models: current research status and future prospect

Yongjun ZHANG, Yansheng LI, Bo DANG, Kang WU, Xin GUO, Jian WANG, Jingdong CHEN, Ming YANG

2024, 53(10):  1942-1954.  doi:10.11947/j.AGCS.2024.20240019.

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The increasing remote sensing capabilities for Earth observation have eased the access to abundant data and enabled the emergence and development of remote sensing foundation models (RSFMs). Designing distinct deep neural networks and optimizing for different data and task types require substantial development efforts and prohibitively high computational resources. In order to address these issues, researchers in the remote sensing field have shifted their focus to the study of RSFMs and presented many dedicated designed unified models. To enhance the generalizability and interpretability of RSFMs, the integration of extensive geographic knowledge has been recognized as a pivotal/key approach. While existing works have explored or incorporated geographic knowledge into the architecture design or pre-training methods of RSFMs, there lacks of a comprehensive survey to review the current status of geographic knowledge-guided RSFMs. Therefore, this paper starts with summarizing and categorizing large-scale pre-training datasets and then provides an overview of the research progress in this field. Subsequently, we introduce intelligent interpretation algorithms for remote sensing imagery guided by geographic knowledge, along with advancements in the exploration and utilization of geographic knowledge specifically tailored for RSFMs. Finally, several future research prospects are outlined to tackle the persisting challenges in this field, aiming to shed light on future investigations into RSFMs.

Large models enabling intelligent photogrammetry: status, challenges and prospects

Mi WANG, Xu CHENG, Jun PAN, Yingdong PI, Jing XIAO

2024, 53(10):  1955-1966.  doi:10.11947/j.AGCS.2024.20240068.

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Developed from deep learning and transfer learning techniques, large models leverage vast training datasets and immense parameter capacities to create scale effects, thus inspiring the model's emergent capabilities and demonstrating strong generalization and adaptability in numerous downstream tasks. Large models, represented by ChatGPT and SAM, signify the arrival of the era of general artificial intelligence, providing new theories and techniques for the automation and intelligence of Earth's spatial information processing. To further explore the methods and pathways for large models to empower the field of photogrammetry, this paper reviews the basic problems and mission tasks in the field of photogrammetry, summarizes the research achievements of deep learning methods in intelligent photogrammetric processing, analyzes the advantages and limitations of supervised pre-training methods aimed at specific tasks; Besides, we elaborates on the characteristics and research progress of general artificial intelligence large models, focusing on the generalizability of large models in basic visual tasks and the potential in three-dimensional representation; Finally, this paper explores the current challenges and future trends of large model technologies in the field of photogrammetry, from the perspectives of training data, model fine-tuning strategies, and heterogeneous multi-modal data fusion strategies.

Research progress and trend of intelligent remote sensing large model

Qin YAN, Haiyan GU, Yi YANG, Haitao LI, Hengtong SHEN, Shiqi LIU

2024, 53(10):  1967-1980.  doi:10.11947/j.AGCS.2024.20240053.

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AI large models, with their advantages in generalization, universality, and high accuracy, have become the cornerstone of various AI applications such as computer vision, natural language processing. Based on the analysis of the development process, value, and challenges of AI large models, this article first discusses the research progress of remote sensing large models from three perspectives: data, model, and downstream tasks. At the data level, there is a transition from single modality to multi-modality; at the model level, there is a shift from small models to large models; and at the downstream task level, there is a development from single-task to multi-task. Next, the article explores three key development directions for remote sensing large models: multi-modal remote sensing large models, interpretable remote sensing large models, and reinforcement learning from human feedback(RLHF). Furthermore, it realizes a construction approach for remote sensing large models, namely “construction of unlabeled dataset-self-supervised model learning-downstream transfer application”. Technical experiments have been conducted to validate the significant advantages of remote sensing large models. Finally, the article concludes and provides prospects, emphasizing the need to focus on application tasks and combine theoretical methods, engineering technology, and iterative applications to achieve low-cost training, efficient and fast inference, lightweight deployment, and engineering-based applications for remote sensing large models.

Geodesy and Navigation

An improved model for short-term qualitative rainfall prediction combined with GNSS PWV and meteorological parameters

Zhaohui XIONG, Dunyong ZHENG, Yibin YAO, Changyong HE, Sichun LONG, Shide LU, Jian ZHOU, Xiangen LAI

2024, 53(10):  1981-1992.  doi:10.11947/j.AGCS.2024.20230064.

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With the progress of GNSS data processing technology and the improvement of the accuracy of its derived water vapor products, rich information of water vapor contained in GNSS PWV (precipitable water vapor) has been gradually applied to precipitation forecast. Due to the limited ability of the current short-range rainfall forecasting model, which combines GNSS PWV and meteorological parameters in mining parameter information, this paper proposes an improved model based on RF algorithm and the use of the variations and anomalies of PWV, temperature, pressure and relative humidity as model inputs. Applying the new model to Hubei, Hunan and Jiangxi provinces, the model performance is assessed and compared with the BPNN algorithm. The results show that, compared with the BPNN algorithm, the rainfall forecast correct rate of the new method rises from 87.28% to 89.57% while the false rate is reduced from 17.82% to 15.06%. Thereby, the new model can better capture the influence of PWV and meteorological parameters on rainfall. During periods of frequent severe rains, the new model performs even better with an increase of correct rate by 5.57% and a decrease of false rate by 2.37%. Further studies reveal that setting the forecast starting time at the current epoch t and the previous epoch t-1 to make a forecast at time t+1, the correct rate is increased slightly, but the false rate is also increased marginally.

Wi-Fi RTT/RSS fusion localization CRLB derivation and optimal access points layout design

Meng SUN, Yunjia WANG, Qianxin WANG, Guoliang CHEN, Zengke LI

2024, 53(10):  1993-2006.  doi:10.11947/j.AGCS.2024.20230220.

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The fine time measurement (FTM) protocol-based Wi-Fi RTT positioning is currently a research spotlight in the community. However, the performance evaluation methods of localization algorithms are relatively simplistic and lack theoretical basis. Furthermore, the impact of Wi-Fi access points (AP) layout on positioning accuracy has not been thoroughly studied. This paper focuses on smartphone-based Wi-Fi FTM as the research object. It derives the Cramer-Rao lower bound (CRLB) of the Wi-Fi RTT/RSS hybrid positioning method and clarifies the theoretical CRLB relationships of the single and hybrid positioning methods, which establishes a theoretical foundation for algorithm performance evaluation. The influence of Wi-Fi AP deployments on the localization accuracy of the RTT/RSS fusion positioning is studied, and an optimal access point layout approach for Wi-Fi RTT/RSS hybrid positioning is designed based on the enhanced genetic algorithm (EGA) and CRLB. Research shows that Wi-Fi RSS/RTT fusion localization is also an efficient FTM positioning method. The proposed EGA and CRLB-based optimal layout approach for Wi-Fi APs can quickly produce the optimal access point deployment strategy for the positioning area. The experimental results reveal that under the optimal layout of 7 Wi-Fi APs, the theoretical accuracy of Wi-Fi RSS, RTT, and RSS/RTT fusion positioning in the testing site of this manuscript is 0.92, 1.07 and 0.61 m, respectively. The research work provides theoretical support for evaluating the performance of Wi-Fi FTM positioning and offers practical strategies for deploying Wi-Fi APs effectively, thereby reducing positioning investment.

Cartography and Geoinformation

A generative neural network method for road simplification

Piao LUO, Junkui XU, Fang WU, Yakun LÜ, Qingwen ZHUANG

2024, 53(10):  2007-2020.  doi:10.11947/j.AGCS.2024.20230245.

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Road data is an important part of geospatial data due to its large quantity and high frequency of change. Road feature simplification is also one of the core technical steps of cartographic generalization and spatial data updating.Traditional methods based on data point compression, bend recognition and existing machine learning algorithms have problems of poor stability, weak controllability and low degree of automation in road simplification. Based on the theory of combining visual thinking and syntactic pattern. This paper uses the feature mining ability of deep learning algorithm to introduce generative artificial neural network model into the field of road simplification.Firstly, the road data to be simplified is transformed into sequence data, and the sequence features are extracted to construct the feature data set. Secondly, the Seq2Seq coding model constructed by the gated recurrent unit (GRU) neural network is used to embed large-scale road data to form high-dimensional semantic coding. The simplified small-scale road data is generated by decoding the semantic coding. Finally, the effectiveness and applicability of the model were evaluated according to four indexes: compression rate of arc segment, change rate of length, bend of curve and buffer limit difference.The experimental results show that the proposed model can be applied to road shape simplification, enrich road simplification methods, and promote the comprehensive and intelligent development of map drawing.

Trajectory prediction enhanced by geographic knowledge graph and multi-spatio temporal constraints

Jia LI, Jing LI, Haiyan LIU, Chuanwei LU, Xiaohui CHEN, Junnan LIU, Wen SHI

2024, 53(10):  2021-2033.  doi:10.11947/j.AGCS.2024.20230571.

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Trajectory prediction methods based on machine learning typically rely on the quantity and quality of historical trajectories. But social media check-in data has a low update frequency, that would lead to difficulties in learning and overfitting during trajectory prediction. To overcome the difficulty of low-quality trajectories data in prediction tasks, we propose a trajectory prediction method enhanced by geographic knowledge graph and multi-spatio temporal constraints. The proposed model transforms complex and heterogeneous multi-source geographic information into a geographic knowledge graph composed of several triples for unified expression, and mines entity associations through knowledge embedding models to enhance the feature representation of trajectories. At the same time, the model utilizes a multi-head self-attention with multi-spatio temporal constraints to extract multiple features from check-in trajectories. The proposed model is validated on Foursquare social media check-in data from New York. It's shown in experiment results that proposed model has improved to varying degrees in hit rate (HR) and mean reciprocal rank (MRR) evaluation indicators, comparing with other representation learning methods and prediction models. The result indicate that the proposed model can effectively enhance the representation of check-in trajectories, extract multiple temporal features of trajectories, and improve the prediction accuracy of social media user check-in trajectories.

Summary of PhD Thesis

Study on the mechanism, law and prediction model of surface subsidence in closed mine based on InSAR

Meinan ZHENG

2024, 53(10):  2034-2034.  doi:10.11947/j.AGCS.2024.20230241.

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Study on time-space evolution and driving mechanism of coupling and coordination between new urbanization and eco-environment in central Yunnan megalopolis

Dan HUANG

2024, 53(10):  2035-2035.  doi:10.11947/j.AGCS.2024.20230244.

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Research on parametric expression and reconstruction method of 3D building facade semantic model

Yuefeng WANG

2024, 53(10):  2036-2036.  doi:10.11947/j.AGCS.2024.20230297.

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Missing imputation and short-term prediction of urban traffic flow based on spatiotemporal view learning

Peixiao WANG

2024, 53(10):  2037-2037.  doi:10.11947/j.AGCS.2024.20230304.

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Research on rupture and early warning of strong earthquakes based on high-rate GNSS and synthetic earthquakes

Zhiyu GAO

2024, 53(10):  2038-2038.  doi:10.11947/j.AGCS.2024.20230356.

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Study on the deformation process of the Wenchuan and Lushan earthquakes based on GPS data

Jing ZHAO

2024, 53(10):  2039-2039.  doi:10.11947/j.AGCS.2024.20230361.

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Research on extraction methods for sematic and structural parameters and modeling application of laser point cloud

Bufan ZHAO

2024, 53(10):  2040-2040.  doi:10.11947/j.AGCS.2024.20230280.

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Analysis and application of GNSS time series in Antarctica and Greenland

Wenhao LI

2024, 53(10):  2041-2041.  doi:10.11947/j.AGCS.2024.20230275.

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