Table of Content

20 April 2022, Volume 51 Issue 4

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The 90th Anniversary of Tongji University Surveying and Mapping Discipline

The design of deep learning framework and model for intelligent remote sensing

GONG Jianya, ZHANG Mi, HU Xiangyun, ZHANG Zhan, LI Yansheng, Jiang Liangcun

2022, 51(4):  475-487.  doi:10.11947/j.AGCS.2022.20220027

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The rapid development of remote sensing technology has achieved massive remote sensing images, and the deep-learning-based remote sensing image interpretation has shown certain advantages in image feature extraction and representation. However, the intelligent processing framework and information service capabilities are relatively lagging. Open-source deep learning frameworks and models cannot yet meet the requirements of intelligent remote sensing processing. Based on the analysis of existing intelligent frameworks and models, we design a dedicated deep learning framework and model with remote sensing characteristics for the problems of large remote sensing image size, large-scale changes, and multiple data channels. The focus is on the construction of a dedicated framework that takes into account remote sensing data characteristics and the preliminary experimental results on remote sensing image classification. The design of this remote sensing image interpretation framework will provide strong support for the construction of a dedicated deep learning framework and models that integrate the temporal, spatial, and spectral features of remote sensing data.

From Earth mapping to extraterrestrial planet mapping

TONG Xiaohua, LIU Shijie, XIE Huan, XU Xiong, YE Zhen, FENG Yongjiu, WANG Chao, LIU Sicong, JIN Yanmin, CHEN Peng, HONG Zhonghua, LUAN Kuifeng

2022, 51(4):  488-500.  doi:10.11947/j.AGCS.2022.20220117

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With the continuous development of human space exploration technology, lunar and deep space exploration has become a new battlefield and frontier of surveying and remote sensing science and technology. Driven by many types of deep space exploration tasks, surveying and mapping remote sensing technology has been developed. With various tasks of deep space exploration, this paper systematically summarizes the research status and achievements of orbital remote sensing and mapping of extraterrestrial planets, obstacle avoidance of landing navigation, patrol environment perception and visual navigation. In view of the requirements of future lunar and deep space exploration missions, the development of deep space remote sensing and mapping technology is discussed, including intelligent processing of massive global remote sensing data of extraterrestrial planets, refinement of global control network, high-resolution three-dimensional topography mapping of lunar south pole, multi-sensor-fusion based obstacle avoidance and navigation for landing and patrol.

BDS-3 cycle slip and data gap repair based on the geometry-free ionosphere-filter model

LI Bofeng, QIN Yuanyang, CHEN Guang'e

2022, 51(4):  501-510.  doi:10.11947/j.AGCS.2022.20220036

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High-accurate positioning service relies on carrier phase observations with ambiguities correctly fixed. However, the cycle slips and data gaps caused by environment complexity enhance the challenges for ambiguity resolution and data processing. BDS-3 system broadcasts the quad- and penta-frequency signals all over the world, providing an opportunity to improve the performance of cycle slip processing. Firstly, we establish the pseudorange-to-phase and phase-to-phase geometry-free combination models to detect the extra-wide-lane (EWL) and narrow-lane (NL) cycle slips, respectively. With maximizing the success rate of cycle slips detection, the optimal EWL and NL combinations are determined. Since the ionospheric delay is the key to limit the detection of NL cycle slip and data gap in case of active ionospheric situation, a filtering model with assimilating the ionospheric effects is then developed. The experiment results show as follows. For the sampling interval of 30 s, the geometry-free based model can obtain the success rate as large as 96% for both EWL and NL cycle slip detection. While for the data gap of 3 min, the geometry-free based NL cycle slip detection can only achieve the success rate of 70%, but it can be improved to larger than 95% by the ionospheric delay compensated filtering model.

Processing algorithms and performance evaluation of BDS RDSS location reporting service

CHEN Junping, ZHANG Yize, YU Chao, DING Junsheng

2022, 51(4):  511-521.  doi:10.11947/j.AGCS.2022.20220024

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RDSS (radio determination satellite service) short message is a characteristic service of the BeiDou satellite navigation system (BDS), which realizes user short-message communication and location reporting. The BDS RDSS based location reporting was realized for the first time on its first-generation system (BDS-1) using two GEO (geo-stationary earth orbit) satellites, and its accuracy was limited by the accuracy of the digital elevation model (DEM) database, which was used to assist its realization of 3D positioning service using two satellites only. Starting from its regional System (BDS-2), BDS has developed new technology of the location reporting in the Master Control Center (MCC) by integrating the RNSS (radio navigation satellite system) and RDSS observations. In the new development, the RNSS range and carrier phase observations of user stations are transmitted to the MCC through RDSS link, and the RNSS observations are used to determine users' precise positions in the MCC. Supported by the various types of precise spatio-temporal data, e.g. precise satellite orbits and clocks, the precision of user positioning is thus greatly improved. In this paper, we introduce the three location reporting processing algorithms used in the MCC central station, and evaluates their performance with respect to the sampling rates and baseline length. Data of the 50 BDS tracking stations in Mainland China is used to validate the performance. Results show that the precision of the location reporting realized in the MCC reaches 0.51 m and 0.94 m for horizontal and height components, respectively. The positioning precision shows little relation to both the baseline length and sampling rates, while the precision of BDS-2+BDS-3 combined solutions is improved by around 50% over the solutions using BDS-2 observations only.

Optimization and scheduling of tile loading for three-dimensional model of land consolidation

LIU Xiaobo, TU Jianguang, ZHANG Haifeng, LI Zhijiang

2022, 51(4):  522-533.  doi:10.11947/j.AGCS.2022.20220115

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As a national strategy, land consolidation is an important method to solve land use problems in the process of social and economic development in many countries. The function of land consolidation is to prevent the dysfunction or degradation of land space, which is significant in promoting the construction of ecological civilization. With the advancement of China's industrialization and urbanization, the contradiction among production,living and ecology has become increasingly prominent. As a key solution, land consolidation projects have been carried out extensively throughout the country, especially based on 3D visualizations technology. However, due to the large volume, complexity and fragmented characteristics of the data, the 3D visualization data storage and loading is too difficult to improve the application of 3D model in land consolidation. This paper supposed storage structure optimization method, model fragment loading optimization method and model scheduling optimization method, validated the methods through experiments and application demonstration. The validation and application proved that the proposed method can effectively support the 3D visualization related business in land consolidation projects.

Research and application outlook of land satellite remote sensing monitoring system

WANG Quan, YOU Shucheng

2022, 51(4):  534-543.  doi:10.11947/j.AGCS.2022.20210714

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In recent years, new requirements have been put forward to land remote sensing monitoring generated from the natural resources conservation, the national land spatial planning, implementation and supervision, the ecological restoration and the studies of global change. Benefiting from the rapid developments of land satellites and the widely applications of advanced technologies such as artificial intelligence, the important foundation of the reconstruction of land satellite remote sensing monitoring system has been established. Based on the observation capability of domestic land satellites, this paper proposed the construction of the land satellite remote sensing monitoring system by making full use of such high and cutting-age technologies such as big data, artificial intelligence and cloud computing, while the technical flow of nation-wide operational application was also designed. Moreover, by breaking through a series of key technologies of remote sensing monitoring, the quarterly monitoring of all types of land use in China, high-frequency and accurate monitoring in key areas, and real-time monitoring of specific targets have been realized, which has been adopted in applications such as the law enforcement inspector of natural resources and surface water change monitoring.

The GBM rapid product and the improvement from undifferenced ambiguity resolution

DENG Zhiguo, WANG Jungang, GE Maorong

2022, 51(4):  544-555.  doi:10.11947/j.AGCS.2022.20220022

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Global navigation satellite systems (GNSS) plays a critical role for providing real-time position-ing and navigation services, and the precise satellite orbit and clock products are essential for the high-precision GNSS applications. The international GNSS service (IGS) and its analysis centers (ACs) have been working on the study on precise GNSS data processing and provision of the precise products. The German research center for geosciences (GFZ), as one of the ACs, also provides the multi-GNSS rapid product: the GBM product. We introduce the GBM data processing strategy, analyze the precision of GBM multi-GNSS orbits from 2015 to 2021, and present the impact of applying the undifferenced ambiguity resolution on satellite orbits. The GPS orbits of GBM products agree with the IGS final orbits at the level of 11~13 mm in the three directions, and the GPS orbit 6 hour prediction precision is around 6 cm. The 6 hour prediction precision of GLONASS is around 12 cm, slightly worse than that of Galileo, which holds an average value of 10 cm in the same period but shows a significant improvement to around 5 cm after the end of 2016. The prediction precision of BDS medium earth orbit (MEO) satellites are around 10 cm, and that of the BDS geostationary earth orbit, (GEO) satellites and QZSS satellites are at the level of 1 to 3 meter. The satellite laser ranging (SLR) residuals show that the orbit precision of Galileo, GLONASS, and BDS3-MEO are 23, 41, and 47 mm, respectively. Moreover, comparing the double-differenced ambiguity resolution, adopting the undifferenced ambiguity resolution improves the 6 hour orbit prediction precision by 9%~15%,15%~18%,11%~13%,6%~17% and 14%~25% for the GPS, GLONASS, Galileo, BDS-2 and BDS-3 MEO satellites, respectively.

Scene cognition pattern of point cloud-generalization point cloud

LIU Chun, JIA Shoujun, WU Hangbin, HUANG Wei, ZHENG Ning, AKRAM Akbar

2022, 51(4):  556-567.  doi:10.11947/j.AGCS.2022.20220019

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With the rapid development of sensor technology and observation platform, point cloud data that is viewed as primary data of remote sensing, has gradually become an important information carrier. Moreover, it plays an increasingly significant role in the national major strategic needs such as geological disaster situation awareness, natural resources quantitative investigation and road traffic safety services. At the same time, driven by point cloud observation equipment and national major strategic needs, spatial scenes have changed from perception to cognition, and new requirements for cognitive processing algorithms and computing power have also been put forward. Therefore, based on the basic framework of point cloud scene cognition, this paper analyzes the research status of multi-source point cloud coupled observation, summarizes the key progress of point cloud scene cognition and typical applications in major national strategic needs, and summarizes the main problems facing point cloud scene cognition at present. On this basis, this paper focuses on the cutting-edge challenges of cloud scene cognition, avoids the traditional Euclidean space and turns to the high-dimensional tensor manifold space for point cloud data processing, proposes the scientific concept and technical framework of generalized point cloud, and provides a new research idea for the algorithm and computing power of cognitive processing of point cloud scene.

Smartphone photo based water quality monitoring algorithm and application

LI Junsheng, GAO Min, ZHANG Bing, ZHANG Fangfang, WANG Shenglei, YIN Ziyao, XIE Ya

2022, 51(4):  568-576.  doi:10.11947/j.AGCS.2022.20220049

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Smartphones provide a new means for water quality monitoring based on citizen science, but there is still a lack of systematic research on water quality parameter inversion based on water surface photos taken by smartphones. In view of this situation, this study firstly developed models to retrieve typical water quality parameters and identify typical water pollution, including Forel-Ule index, water clarity, trophic state, cyanobacterial bloom, and black and odorous water. Then, based on these models, the “Water Color Watch” app based on smartphone Android system was developed. The app was applied and evaluated in several typical study areas, and obtained good accuracy in monitoring of water quality. Finally, the impacts of white balance, different digital cameras and photo storage formats on water quality monitoring based on smartphone and their coping strategies were analyzed. The results of this study will help to promote the wide application of smartphones in water quality monitoring.

Key technologies for remote sensing intelligent monitoring and simulation of urban spatial elements

FENG Yongjiu, LI Pengshuo, TONG Xiaohua, XI Mengrong, LIU Sicong, XU Xiong

2022, 51(4):  577-586.  doi:10.11947/j.AGCS.2022.20220086

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For various urban spatial elements, the method development and practical applications are in the center of the intelligent monitoring and spatial deduction simulation using multi-source remote sensing and GIS. The monitoring and simulation are of great significance to territorial and spatial planning and management, urban planning and comprehensive control, and regional decision-making and management. The coverage and driving elements in urban areas are complex and nonlinear, thus we have developed a few intelligent identification methods (e.g. the intelligent adaptive decision tree classifier) that use multi-source remote sensing data and can derive highly accurate and reliable coverage element results. By integrating multi-source remote sensing, POI, and spatiotemporal big data, we have developed new methods that can effectively detect and identify the driving forces of urban element changes. Urban simulation and deduction are advanced modeling based on the spatial monitoring of remote sensing for urban management and decision-making. We systematically have studied the urban deduction and prediction method based on urban spatial evolution mechanisms, spatial statistical modeling, and heuristic intelligent modeling, and applied these methods to simulate complex land use, urban expansion, ecological evolution, and carbon storage. Among the platforms available, we have developed two state-of-art software packages (i.e. UrbanCA and Futureland) in which the former focuses on urban growth and the latter focuses on multiple types of land-use change, and both integrate a variety of advanced methods, which have been successfully verified in the Yangtze River Delta.

Spatial-spectral collaborative multi-scale vertex component analysis for hyperspectral image endmember extraction

SUN Weiwei, CHANG Minghui, MENG Xiangchao, YANG Gang, REN Kai

2022, 51(4):  587-598.  doi:10.11947/j.AGCS.2022.20210718

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Current endmember extraction methods cannot accurately extract the endmembers of complicated ground features, and therefore this paper proposed a spatial-spectral collaborative multi-scale vertex component analysis (VCA) method. Hyperspectral images are firstly jointly clustered and segmented based on multi-feature fusion using spectral features, texture features, and shape features, which makes full use of the spatial heterogeneity information of ground features. Then, multi-scale low-rank matrix decomposition is used to decompose the segmented images and reduce the influence of noise on endmember extraction. Meanwhile, VCA is used to extract endmembers from low-resolution images, coordinate mapping is implemented to search these endmembers of high-resolution images, and vertex component analysis is used to extract endmember from low resolution image. After that, coordinate mapping is used to ferret about the corresponding endmembers in the high-resolution image, and the spectral angle between them is calculated to help accurately decide the pure endmembers. Finally, the proposed method is traversed into all segmented images to obtain the final pure endmembers. The proposed method is verified experimentally by using simulated and real GF-5 hyperspectral data. Experimental results show that the CVCA method can extract high-precision pure endmembers and has high calculation efficiency.

Remote sensing and observation validation of key parameters of the polar ice sheet in the context of global climate change

QIAO Gang, HAO Tong, LI Hongwei, LU Ping, AN Lu, CHEN Qiujie, LI Rongxing

2022, 51(4):  599-611.  doi:10.11947/j.AGCS.2022.20220116

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In the context of current global climate change, the study of key processes and parameters of polar regions is a key area of global change research, and is crucial to reveal the influence of polar ice sheets on global sea level change and improve the prediction accuracy of sea level rise contribution. Polar scientific expeditions can provide in situ data sets that could be used in calibration and validation of remote sensing observations and reduce the uncertainty of remote sensing inversions. Based on the remote sensing and fieldwork of the global change research team of Tongji University on key parameters of the polar ice sheet, this paper focuses on the team's research on the validation of polar scientific research observations and data processing, including the satellite-ground synoptic validation of altimetric satellites on the Antarctic ice sheet, the deployment of satellite angular reflectors, the observation and model validation of the internal temperature of the granular snow layer, the multi-platform UAV (unmanned aerial vehicle) sea ice detection and snow-ice environment investigation, and mass change assessment of the Greenland Ice Sheet, etc. Finally, an outlook on the future polar science research validation program is provided.

Thick cloud removal of hyperspectral images by fusing with multispectral images

WANG Lanxing, WANG Qunming, TONG Xiaohua

2022, 51(4):  612-621.  doi:10.11947/j.AGCS.2022.20220017

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The cloud contamination issue poses a significant obstacle to the application of hyperspectral images. Existing cloud removal methods usually use temporally close images from the same sensors as cloudy images to provide auxiliary information. Unfortunately, the coarse temporal resolution of hyperspectral images (GF-5 and EO-1 hyperspectral images) may result in great land cover changes. The finer temporal resolution of multispectral images (Landsat 8 OLI images) allows to provide auxiliary information temporally closer to the hyperspectral cloudy images, thus reducing the effect uncertainty caused by land cover changes. To deal with the large spectral differences between auxiliary multispectral bands and cloud-contaminated hyperspectral bands, this paper applied the spatial-spectral-based random forest (SSRF) method to use multispectral images (Landsat 8 OLI images) for cloud removal of hyperspectral images, namely, the SSRF_M method. Benefiting from the strong nonlinear fitting ability, the proposed SSRF_M method can use simultaneously the effective information from multiple bands of the auxiliary multispectral image for cloud removal of each hyperspectral band. In this paper, the GF-5 and EO-1 hyperspectral images were used for cloud simulation experiments. The visual and quantitative evaluation results show that compared with the strategy using homologous auxiliary images, the proposed SSRF_M method can reconstruct the information under clouds more accurately.

Analysis of terrestrial water storage change and its driving factors of Hongliu River region

ZHANG Lin, SHEN Yunzhong, CHEN Qiujie, WANG Fengwei

2022, 51(4):  622-630.  doi:10.11947/j.AGCS.2022.20220030

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The terrestrial water storage change (TWSC) from January 2003 to December 2016 over the Hongliu River region (HLRR) is derived from the CSR-RL06 and Tongji-Grace2018 time-variable gravity field models. In addition, the major influencing factors of the regional TWSC are quantified via the water balance equations using precipitation, evapotranspiration, runoff, and human-induced water consumption. The results show that: ① When the water consumption is considered, the root-mean-square error of the water balance is reduced by 39%. ② The annual positive average contribution rate of meteorological and hydrological factors to the HLRR TWSC is 47.7% except for 2009, 2011 and 2016 with the mean negative contribution rate of -22.5%, while the annual negative average contribution rate of the human-related water consumption is -34.8%. ③ The correlation coefficient between water storage changes in the Longtan Reservoir and the TWSC over the Hongliu River region is 0.66, and there are one-year and two-year main cycle signals.