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ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Articles | Volume V-3-2020
https://doi.org/10.5194/isprs-annals-V-3-2020-603-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/isprs-annals-V-3-2020-603-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
03 Aug 2020
| 03 Aug 2020
CRATER DETECTION USING TEXTURE FEATURE AND RANDOM PROJECTION DEPTH FUNCTION
Y. Wang
Shanghai Key Laboratory for Planetary Mapping and Remote Sensing for Deep Space Exploration1239 Siping Road, Shanghai 200092, P. R. China
College of Surveying and Geo-informatics, Tongji University, 1239 Siping Road, Shanghai 200092, P. R. China
X. Tong
Shanghai Key Laboratory for Planetary Mapping and Remote Sensing for Deep Space Exploration1239 Siping Road, Shanghai 200092, P. R. China
College of Surveying and Geo-informatics, Tongji University, 1239 Siping Road, Shanghai 200092, P. R. China
Shanghai Key Laboratory for Planetary Mapping and Remote Sensing for Deep Space Exploration1239 Siping Road, Shanghai 200092, P. R. China
College of Surveying and Geo-informatics, Tongji University, 1239 Siping Road, Shanghai 200092, P. R. China
M. Jiang
Shanghai Key Laboratory for Planetary Mapping and Remote Sensing for Deep Space Exploration1239 Siping Road, Shanghai 200092, P. R. China
College of Civil Engineering, Tongji University, 1239 Siping Road, Shanghai 200092, P. R. China
Y. Huang
Shanghai Key Laboratory for Planetary Mapping and Remote Sensing for Deep Space Exploration1239 Siping Road, Shanghai 200092, P. R. China
College of Civil Engineering, Tongji University, 1239 Siping Road, Shanghai 200092, P. R. China
S. Liu
Shanghai Key Laboratory for Planetary Mapping and Remote Sensing for Deep Space Exploration1239 Siping Road, Shanghai 200092, P. R. China
College of Surveying and Geo-informatics, Tongji University, 1239 Siping Road, Shanghai 200092, P. R. China
X. Xu
Shanghai Key Laboratory for Planetary Mapping and Remote Sensing for Deep Space Exploration1239 Siping Road, Shanghai 200092, P. R. China
College of Surveying and Geo-informatics, Tongji University, 1239 Siping Road, Shanghai 200092, P. R. China
Q. Du
School of Electronic and Computer Engineering, Mississippi State University, USA
Q. Wang
Shanghai Key Laboratory for Planetary Mapping and Remote Sensing for Deep Space Exploration1239 Siping Road, Shanghai 200092, P. R. China
College of Surveying and Geo-informatics, Tongji University, 1239 Siping Road, Shanghai 200092, P. R. China
C. Wang
Shanghai Key Laboratory for Planetary Mapping and Remote Sensing for Deep Space Exploration1239 Siping Road, Shanghai 200092, P. R. China
College of Surveying and Geo-informatics, Tongji University, 1239 Siping Road, Shanghai 200092, P. R. China
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Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-1-W2-2023, 771–776, https://doi.org/10.5194/isprs-archives-XLVIII-1-W2-2023-771-2023, https://doi.org/10.5194/isprs-archives-XLVIII-1-W2-2023-771-2023, 2023
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A random forest (RF) model was proposed to extend the superior SMAP dataset (named RF_SMAP) from 1979 to 2015, using the corresponding CCI time-series. The new long time-series RF_SMAP dataset, which will be available to download, will be of great value for a range of research in applications such as climate assessment, agricultural planning, food insecurity monitoring and drought assessment and monitoring.
Haoxuan Yang, Qunming Wang, Wei Zhao, and Peter M. Atkinson
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2022-137, https://doi.org/10.5194/essd-2022-137, 2022
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A random forest (RF) model was proposed to extend the superior SMAP dataset (named RF_SMAP) from 1979 to 2015, using the corresponding CCI time-series. The new long time-series RF_SMAP dataset, which will be available to download, will be of great value for a range of research in applications such as climate assessment, agricultural planning, food insecurity monitoring and drought assessment and monitoring.
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Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B1-2022, 79–84, https://doi.org/10.5194/isprs-archives-XLIII-B1-2022-79-2022, https://doi.org/10.5194/isprs-archives-XLIII-B1-2022-79-2022, 2022
Q. Xu, H. Xie, Y. Sun, X. Liu, Y. Guo, P. Huang, B. Li, and X. Tong
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B2-2022, 309–314, https://doi.org/10.5194/isprs-archives-XLIII-B2-2022-309-2022, https://doi.org/10.5194/isprs-archives-XLIII-B2-2022-309-2022, 2022
A. Zhao, Y. Cheng, D. Lv, M. Xia, R. Li, L. An, S. Liu, and Y. Tian
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H. Zhang, Y. Shang, X. Tong, J. Chen, W. Ma, M. Li, Y. Lu, and H. Chen
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-3-2022, 619–625, https://doi.org/10.5194/isprs-annals-V-3-2022-619-2022, https://doi.org/10.5194/isprs-annals-V-3-2022-619-2022, 2022
Rongxing Li, Hongwei Li, Tong Hao, Gang Qiao, Haotian Cui, Youquan He, Gang Hai, Huan Xie, Yuan Cheng, and Bofeng Li
The Cryosphere, 15, 3083–3099, https://doi.org/10.5194/tc-15-3083-2021, https://doi.org/10.5194/tc-15-3083-2021, 2021
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We present the results of an assessment of ICESat-2 surface elevations along the 520 km CHINARE route in East Antarctica. The assessment was performed based on coordinated multi-sensor observations from a global navigation satellite system, corner cube retroreflectors, retroreflective target sheets, and UAVs. The validation results demonstrate that ICESat-2 elevations are accurate to 1.5–2.5 cm and can potentially overcome the uncertainties in the estimation of mass balance in East Antarctica.
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S. Luo, Y. Cheng, Z. Li, Y. Wang, K. Wang, X. Wang, G. Qiao, W. Ye, Y. Li, M. Xia, X. Yuan, Y. Tian, X. Tong, and R. Li
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2021, 491–496, https://doi.org/10.5194/isprs-archives-XLIII-B3-2021-491-2021, https://doi.org/10.5194/isprs-archives-XLIII-B3-2021-491-2021, 2021
Y. Gong, H. Xie, X. Tong, Y. Jin, X. Xv, and Q. Wang
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H. Zhang, S. Liu, Z. Ye, X. Tong, H. Xie, S. Zheng, and Q. Du
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Y. Lu, J. Zhang, X. Tong, W. Han, and H. Zhao
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Y. Cheng, X. Li, G. Qiao, W. Ye, Y. Huang, Y. Li, K. Wang, Y. Tian, X. Tong, and R. Li
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2-W13, 1735–1739, https://doi.org/10.5194/isprs-archives-XLII-2-W13-1735-2019, https://doi.org/10.5194/isprs-archives-XLII-2-W13-1735-2019, 2019
R. Li, D. Lv, H. Xiao, S. Liu, Y. Cheng, G. Hai, and X. Tong
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2-W13, 1759–1763, https://doi.org/10.5194/isprs-archives-XLII-2-W13-1759-2019, https://doi.org/10.5194/isprs-archives-XLII-2-W13-1759-2019, 2019
R. Li, H. Xie, Y. Tian, W. Du, J. Chen, G. Hai, S. Zhang, and X. Tong
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2-W13, 1765–1769, https://doi.org/10.5194/isprs-archives-XLII-2-W13-1765-2019, https://doi.org/10.5194/isprs-archives-XLII-2-W13-1765-2019, 2019
Z. Ye, Y. Xu, L. Hoegner, X. Tong, and U. Stilla
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2-W13, 147–153, https://doi.org/10.5194/isprs-archives-XLII-2-W13-147-2019, https://doi.org/10.5194/isprs-archives-XLII-2-W13-147-2019, 2019
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Flow-like landslides, such as flow slides and debris avalanches, have caused serious infrastructure damage and casualties for centuries. Effective numerical simulation incorporating accurate soil mechanical parameters is essential for predicting post-failure landslide mobility. In this study, smoothed particle hydrodynamics (SPH) incorporating soil ring shear test results were used to forecast the long-runout mobility for a landslide on an unstable slope in China.
Y. Tian, S. Zhang, W. Du, J. Chen, H. Xie, X. Tong, and R. Li
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3, 1657–1660, https://doi.org/10.5194/isprs-archives-XLII-3-1657-2018, https://doi.org/10.5194/isprs-archives-XLII-3-1657-2018, 2018
Wenping Song, Shijie Liu, Xiaohua Tong, Changling Niu, Zhen Ye, Han Zhang, and Yanmin Jin
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-3, 193–196, https://doi.org/10.5194/isprs-annals-IV-3-193-2018, https://doi.org/10.5194/isprs-annals-IV-3-193-2018, 2018
Xin Zhang, Shijie Liu, Haifeng Yu, Xiaohua Tong, and Guoman Huang
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-3, 267–271, https://doi.org/10.5194/isprs-annals-IV-3-267-2018, https://doi.org/10.5194/isprs-annals-IV-3-267-2018, 2018
W. Du, L. Chen, H. Xie, G. Hai, S. Zhang, and X. Tong
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2-W7, 1513–1516, https://doi.org/10.5194/isprs-archives-XLII-2-W7-1513-2017, https://doi.org/10.5194/isprs-archives-XLII-2-W7-1513-2017, 2017
G. Hai, H. Xie, J. Chen, L. Chen, R. Li, and X. Tong
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2-W7, 1517–1520, https://doi.org/10.5194/isprs-archives-XLII-2-W7-1517-2017, https://doi.org/10.5194/isprs-archives-XLII-2-W7-1517-2017, 2017
M. Xia, G. Tang, Y. Tian, W. Ye, R. Li, and X. Tong
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2-W7, 1569–1573, https://doi.org/10.5194/isprs-archives-XLII-2-W7-1569-2017, https://doi.org/10.5194/isprs-archives-XLII-2-W7-1569-2017, 2017
H. Xiao, S. Liu, R. Li, and X. Tong
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2-W7, 1575–1577, https://doi.org/10.5194/isprs-archives-XLII-2-W7-1575-2017, https://doi.org/10.5194/isprs-archives-XLII-2-W7-1575-2017, 2017
Q. Wang, V. Rodriguez-Galiano, and P. M. Atkinson
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2-W7, 913–917, https://doi.org/10.5194/isprs-archives-XLII-2-W7-913-2017, https://doi.org/10.5194/isprs-archives-XLII-2-W7-913-2017, 2017
Rongxing Li, Haifeng Xiao, Shijie Liu, and Xiaohua Tong
The Cryosphere Discuss., https://doi.org/10.5194/tc-2017-178, https://doi.org/10.5194/tc-2017-178, 2017
Revised manuscript not accepted
Short summary
Short summary
Fracturing in the RFIS was slightly increased, particularly at its front, from 2003 to 2015. They do not seem to suggest an immediate significant impact on the stability of the shelf. However, with the rapid changes and 3D measurements of Rifts 1 and 2, the most active activities occurred at the front of the FIS from 2001 to 2016. A potential upcoming major calving event in FIS is estimated to occur in 2051. The stability of the ice shelf, particularly Rifts 1 and 2, should be closely monitored.
C. Guo, X. Tong, S. Liu, S. Liu, X. Lu, P. Chen, Y. Jin, and H. Xie
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3-W1, 49–53, https://doi.org/10.5194/isprs-archives-XLII-3-W1-49-2017, https://doi.org/10.5194/isprs-archives-XLII-3-W1-49-2017, 2017
W. Zhao, X. Tong, H. Xie, Y. Jin, S. Liu, D. Wu, X. Liu, L. Guo, and Q. Zhou
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3-W1, 213–218, https://doi.org/10.5194/isprs-archives-XLII-3-W1-213-2017, https://doi.org/10.5194/isprs-archives-XLII-3-W1-213-2017, 2017
Q. Zhou, X. Tong, S. Liu, X. Lu, S. Liu, P. Chen, Y. Jin, and H. Xie
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3-W1, 219–224, https://doi.org/10.5194/isprs-archives-XLII-3-W1-219-2017, https://doi.org/10.5194/isprs-archives-XLII-3-W1-219-2017, 2017
W. Cao, X. H. Tong, S. C. Liu, and D. Wang
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B8, 25–31, https://doi.org/10.5194/isprs-archives-XLI-B8-25-2016, https://doi.org/10.5194/isprs-archives-XLI-B8-25-2016, 2016
T. Feng, Z. Hong, Q. Fu, S. Ma, X. Jie, H. Wu, C. Jiang, and X. Tong
Nat. Hazards Earth Syst. Sci., 14, 2165–2178, https://doi.org/10.5194/nhess-14-2165-2014, https://doi.org/10.5194/nhess-14-2165-2014, 2014
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