Table of Content

20 December 2023, Volume 6 Issue 4
Guest Editorial:Special Issue on Intelligent Interpretation of Remote Sensing Images: Theory, Methods and Applications
Xiangyun HU, Zhi GAO, Jinshui ZHANG, Weiwei SONG
2023, 6(4):  1-2. 
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Development of Integrated and Intelligent Geodetic and Photogrammetry Satellites with Corresponding Key Technologies
Yuanxi YANG, Xia REN, Jianrong WANG
2023, 6(4):  3-12.  doi:10.11947/j.JGGS.2023.0401
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Aerospace surveying and mapping has become the main method of global earth observation. It can be divided into the geodetic observation satellites and the topographic surveying satellites according to the disciplines. In this paper, the geodetic satellites and photographic satellites are introduced respectively. Then, the existing problems in Chinese earth observation satellites are analyzed, and the comprehensive satellite with integrated payloads, the intensive microsatellite constellation and the intelligent observation satellite are proposed as three different development ideas for the future earth observation satellites. The possibility of the three ideas is discussed in detail, as well as the related key technologies.

Disordered Multi-view Registration Method Based on the Soft Trimmed Deep Network
Rui GUO, Yuanlong SONG, Zhengyao WANG
2023, 6(4):  13-26.  doi:10.11947/j.JGGS.2023.0402
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Compared with the pair-wise registration of point clouds, multi-view point cloud registration is much less studied. In this dissertation, a disordered multi-view point cloud registration method based on the soft trimmed deep network is proposed. In this method, firstly, the expression ability of feature extraction module is improved and the registration accuracy is increased by enhancing feature extraction network with the point pair feature. Secondly, neighborhood and angle similarities are used to measure the consistency of candidate points to surrounding neighborhoods. By combining distance consistency and high dimensional feature consistency, our network introduces the confidence estimation module of registration, so the point cloud trimmed problem can be converted to candidate for the degree of confidence estimation problem, achieving the pair-wise registration of partially overlapping point clouds. Thirdly, the results from pair-wise registration are fed into the model fusion to achieve the rough registration of multi-view point clouds. Finally, the hierarchical clustering is used to iteratively optimize the clustering center model by gradually increasing the number of clustering categories and performing clustering and registration alternately. This method achieves rough point cloud registration quickly in the early stage, improves the accuracy of multi-view point cloud registration in the later stage, and makes full use of global information to achieve robust and accurate multi-view registration without initial value.

Multi-task Learning of Semantic Segmentation and Height Estimation for Multi-modal Remote Sensing Images
Mengyu WANG, Zhiyuan YAN, Yingchao FENG, Wenhui DIAO, Xian SUN
2023, 6(4):  27-39.  doi:10.11947/j.JGGS.2023.0403
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Deep learning based methods have been successfully applied to semantic segmentation of optical remote sensing images. However, as more and more remote sensing data is available, it is a new challenge to comprehensively utilize multi-modal remote sensing data to break through the performance bottleneck of single-modal interpretation. In addition, semantic segmentation and height estimation in remote sensing data are two tasks with strong correlation, but existing methods usually study individual tasks separately, which leads to high computational resource overhead. To this end, we propose a Multi-Task learning framework for Multi-Modal remote sensing images (MM_MT). Specifically, we design a Cross-Modal Feature Fusion (CMFF) method, which aggregates complementary information of different modalities to improve the accuracy of semantic segmentation and height estimation. Besides, a dual-stream multi-task learning method is introduced for Joint Semantic Segmentation and Height Estimation (JSSHE), extracting common features in a shared network to save time and resources, and then learning task-specific features in two task branches. Experimental results on the public multi-modal remote sensing image dataset Potsdam show that compared to training two tasks independently, multi-task learning saves 20% of training time and achieves competitive performance with mIoU of 83.02% for semantic segmentation and accuracy of 95.26% for height estimation.

CFM-UNet: A Joint CNN and Transformer Network via Cross Feature Modulation for Remote Sensing Images Segmentation
Min WANG, Peidong WANG
2023, 6(4):  40-47.  doi:10.11947/j.JGGS.2023.0404
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The semantic segmentation methods based on CNN have made great progress, but there are still some shortcomings in the application of remote sensing images segmentation, such as the small receptive field can not effectively capture global context. In order to solve this problem, this paper proposes a hybrid model based on ResNet50 and swin transformer to directly capture long-range dependence, which fuses features through Cross Feature Modulation Module(CFMM). Experimental results on two publicly available datasets, Vaihingen and Potsdam, are mIoU of 70.27% and 76.63%, respectively. Thus, CFM-UNet can maintain a high segmentation performance compared with other competitive networks.

Suppression of Plasma Bubbles over South America under Weak Geomagnetic Perturbations
Xunzhe YIN, Dongjie YUE, Changzhi ZHAI, Yutian CHEN, Xiaoyun CHENG
2023, 6(4):  48-69.  doi:10.11947/j.JGGS.2023.0405
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During a long-term Equatorial Plasma Bubbles (EPBs) occurrence between October 2020 and March 2021, a significant EPB suppression event was identified on November 22 and the observations from multi-instrument have been utilized to investigate this event. Global-scale Observations of the Limb and Disk (GOLD) satellite observed prominent EPBs between 23:40 UT and 23:55 UT during the long-term occurrence days. However, no dark stripes representing EPBs were observed on November 22, and the Equatorial Ionization Anomaly (EIA) structure remained intact. The Total Electron Content (TEC) maps show that these EPBs appeared in the region between 35°W and 65°W longitudes and the magnitudes of the TEC loss in EPBs regions were about 20 TECU. Except for 22 November, the S4 index was consistently greater than 0.6 throughout November, indicating significant ionospheric scintillation. The Rate Of TEC Index (ROTI) maps revealed that the spatial extent and intensity of EPBs increased after their suppression, and the EPBs were locally generated. The swarm electron density measurements indicated that the variation amplitudes of EPBs at 510km altitude were approximately 3 to 5 times larger than that at 460km altitude. The impact region of EPBs at 510km was between 15°S and 20°N latitudes, while at 460km, it was between 0° and 17°N latitudes. During the period of EPB suppression, the average h’f at three ionosonde stations decreased by about 50km, and the vertical drift velocity (Vz) approached ~0m/s while it was more than 20 m/s during the long-term occurrence.

Fault Diagnosis and Separation for a Distributed Rotary-laser Scanning System
Siyang GUO, Yin GUO, Shibin YIN, Hongbo XIE, Jigui ZHU
2023, 6(4):  70-78.  doi:10.11947/j.JGGS.2023.0406
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The wMPS is a laser-based measurement system used for large scale metrology. However, it is susceptible to external factors such as vibrations, which can lead to unreliable measurements. This paper presents a fault diagnosis and separation method which can counter this problem. To begin with, the paper uses simple models to explain the fault diagnosis and separation methods. These methods are then mathematically derived using statistical analysis and the principles of the wMPS. A comprehensive solution for fault diagnosis and separation is proposed, considering the characteristics of the wMPS. The effectiveness of this solution is verified through experimental observations. It can be concluded that this approach can detect and separate false observations, thereby enhancing the reliability of the wMPS.

Evalution of the Accuracy and Performance of Multi-GNSS (MGEX) Positioning for Long Baselines by Using Different Software
Atınç Pırtı, Mehmet Ali Yücel
2023, 6(4):  79-92.  doi:10.11947/j.JGGS.2023.0407
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The Global Positioning System (GPS) is a GNSS constellation, but GNSS is not always GPS. GPS is one of the GNSS constellations used around the world. The GNSS constellations include GPS (US), QZSS (Japan), Beidou/BDS (China), Galileo (EU), and GLONASS (Russia). In 1999, the European Commission (EC) proposed the European Galileo satellite navigation system for the first time. A four-phase development was proposed, including public and private sector finance. Galileo was intended for both civilian and government use, and is managed and controlled by civil authorities. Galileo is made up of 30 satellites, a number of globally distributed ground stations, and a ground control and monitoring system, all of which are extremely similar to the structure, format, and layout of GPS. In this study, we investigate GPS/GLONASS/Galileo/Beidou/IRNSS/QZSS Navigation Satellite System integration algorithm for long baselines ranging from 1500km to 3000km in China, Japan and Mongolia. The positioning performance with GPS/GLONASS/Galileo/BDS/IRNSS/QZSS, GPS-only, Galileo-only, GLONASS-only and BDS-only, etc. is compared in terms of the positioning accuracy. An improvement of positioning accuracy over long baselines can be found with GPS/GLONASS/Galileo/BDS/QZSS/IRNSS compared with that of GPS-only and that of BDS-only. The obtained differences of the two baselines (Topcon Magnet Tools Software (Multi-GNSS)-(CSRS-PPP (GPS/GLONASS), (Trimble-RTX (GPS/GLONASS), (AUSPOS (GPS/GLONASS)) Online Processing Software) by using GPS/GLONASS/Galileo/BDS/QZSS/IRNSS signals is between 40cm and 111.5cm on three days.