- Article
(3733 KB) - Metadata XML
- April 12, 2024
- |
- Home
- |
- Sitemap
- |
- The ISPRS Foundation
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Articles | Volume IV-2/W7
https://doi.org/10.5194/isprs-annals-IV-2-W7-145-2019
© Author(s) 2019. 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-IV-2-W7-145-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
16 Sep 2019
| 16 Sep 2019
AGGREGATING CLOUD-FREE SENTINEL-2 IMAGES WITH GOOGLE EARTH ENGINE
M. Schmitt
Signal Processing in Earth Observation, Technical University of Munich, Munich, Germany
L. H. Hughes
Signal Processing in Earth Observation, Technical University of Munich, Munich, Germany
C. Qiu
Signal Processing in Earth Observation, Technical University of Munich, Munich, Germany
X. X. Zhu
Signal Processing in Earth Observation, Technical University of Munich, Munich, Germany
Remote Sensing Technology Institute, German Aerospace Center (DLR), Oberpfaffenhofen, Wessling, Germany
Related authors
Q. Li, Y. Shi, S. Auer, R. Roschlaub, K. Möst, M. Schmitt, and X. X. Zhu
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-2-2020, 517–524, https://doi.org/10.5194/isprs-annals-V-2-2020-517-2020, https://doi.org/10.5194/isprs-annals-V-2-2020-517-2020, 2020
C. Qiu, P. Gamba, M. Schmitt, and X. X. Zhu
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-3-2020, 787–794, https://doi.org/10.5194/isprs-annals-V-3-2020-787-2020, https://doi.org/10.5194/isprs-annals-V-3-2020-787-2020, 2020
M. Schmitt, J. Prexl, P. Ebel, L. Liebel, and X. X. Zhu
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-3-2020, 795–802, https://doi.org/10.5194/isprs-annals-V-3-2020-795-2020, https://doi.org/10.5194/isprs-annals-V-3-2020-795-2020, 2020
L. H. Hughes and M. Schmitt
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-2-W7, 71–78, https://doi.org/10.5194/isprs-annals-IV-2-W7-71-2019, https://doi.org/10.5194/isprs-annals-IV-2-W7-71-2019, 2019
M. Schmitt, L. H. Hughes, C. Qiu, and X. X. Zhu
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-2-W7, 153–160, https://doi.org/10.5194/isprs-annals-IV-2-W7-153-2019, https://doi.org/10.5194/isprs-annals-IV-2-W7-153-2019, 2019
M. Schmitt, L. H. Hughes, and X. X. Zhu
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-1, 141–146, https://doi.org/10.5194/isprs-annals-IV-1-141-2018, https://doi.org/10.5194/isprs-annals-IV-1-141-2018, 2018
H. Bagheri, M. Schmitt, P. d’Angelo, and X. X. Zhu
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2, 43–48, https://doi.org/10.5194/isprs-archives-XLII-2-43-2018, https://doi.org/10.5194/isprs-archives-XLII-2-43-2018, 2018
C. P. Qiu, M. Schmitt, P. Ghamisi, and X. X. Zhu
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2, 931–936, https://doi.org/10.5194/isprs-archives-XLII-2-931-2018, https://doi.org/10.5194/isprs-archives-XLII-2-931-2018, 2018
M. Schmitt, L. H. Hughes, M. Körner, and X. X. Zhu
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2, 1045–1051, https://doi.org/10.5194/isprs-archives-XLII-2-1045-2018, https://doi.org/10.5194/isprs-archives-XLII-2-1045-2018, 2018
L. H. Hughes, S. Auer, and M. Schmitt
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-2, 129–136, https://doi.org/10.5194/isprs-annals-IV-2-129-2018, https://doi.org/10.5194/isprs-annals-IV-2-129-2018, 2018
C. P. Qiu, M. Schmitt, and X. X. Zhu
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-1-W1, 411–417, https://doi.org/10.5194/isprs-archives-XLII-1-W1-411-2017, https://doi.org/10.5194/isprs-archives-XLII-1-W1-411-2017, 2017
H. Bagheri, M. Schmitt, and X. X. Zhu
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-1-W1, 433–439, https://doi.org/10.5194/isprs-archives-XLII-1-W1-433-2017, https://doi.org/10.5194/isprs-archives-XLII-1-W1-433-2017, 2017
Tian Li, Konrad Heidler, Lichao Mou, Ádám Ignéczi, Xiao Xiang Zhu, and Jonathan L. Bamber
Earth Syst. Sci. Data, 16, 919–939, https://doi.org/10.5194/essd-16-919-2024, https://doi.org/10.5194/essd-16-919-2024, 2024
Short summary
Short summary
Our study uses deep learning to produce a new high-resolution calving front dataset for 149 marine-terminating glaciers in Svalbard from 1985 to 2023, containing 124 919 terminus traces. This dataset offers insights into understanding calving mechanisms and can help improve glacier frontal ablation estimates as a component of the integrated mass balance assessment.
Y. Sun, A. Kruspe, L. Meng, Y. Tian, E. J. Hoffmann, S. Auer, and X. X. Zhu
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-1-W2-2023, 225–232, https://doi.org/10.5194/isprs-archives-XLVIII-1-W2-2023-225-2023, https://doi.org/10.5194/isprs-archives-XLVIII-1-W2-2023-225-2023, 2023
J. Zhao, F. Roth, B. Bauer-Marschallinger, W. Wagner, M. Chini, and X. X. Zhu
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., X-1-W1-2023, 911–918, https://doi.org/10.5194/isprs-annals-X-1-W1-2023-911-2023, https://doi.org/10.5194/isprs-annals-X-1-W1-2023-911-2023, 2023
Yao Sun, Stefan Auer, Liqiu Meng, and Xiao Xiang Zhu
Abstr. Int. Cartogr. Assoc., 6, 250, https://doi.org/10.5194/ica-abs-6-250-2023, https://doi.org/10.5194/ica-abs-6-250-2023, 2023
Erik Loebel, Mirko Scheinert, Martin Horwath, Angelika Humbert, Julia Sohn, Konrad Heidler, Charlotte Liebezeit, and Xiao Xiang Zhu
The Cryosphere Discuss., https://doi.org/10.5194/tc-2023-52, https://doi.org/10.5194/tc-2023-52, 2023
Preprint under review for TC
Short summary
Short summary
Comprehensive data sets of calving front change are essential to study and model outlet glaciers. Current records are limited in temporal resolution as they rely on manual delineation. We apply deep learning to automatically delineate calving fronts of 23 Greenland glaciers. Resulting time series resolve long-term, seasonal and sub-seasonal patterns. We discuss the implications of our results and provide the cryosphere community with a data product and an implementation of our processing system.
Jingliang Hu, Rong Liu, Danfeng Hong, Andrés Camero, Jing Yao, Mathias Schneider, Franz Kurz, Karl Segl, and Xiao Xiang Zhu
Earth Syst. Sci. Data, 15, 113–131, https://doi.org/10.5194/essd-15-113-2023, https://doi.org/10.5194/essd-15-113-2023, 2023
Short summary
Short summary
Multimodal data fusion is an intuitive strategy to break the limitation of individual data in Earth observation. Here, we present a multimodal data set, named MDAS, consisting of synthetic aperture radar (SAR), multispectral, hyperspectral, digital surface model (DSM), and geographic information system (GIS) data for the city of Augsburg, Germany, along with baseline models for resolution enhancement, spectral unmixing, and land cover classification, three typical remote sensing applications.
S. Zhao, S. Saha, and X. X. Zhu
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2022, 1407–1413, https://doi.org/10.5194/isprs-archives-XLIII-B3-2022-1407-2022, https://doi.org/10.5194/isprs-archives-XLIII-B3-2022-1407-2022, 2022
S. Saha, J. Gawlikowski, and X. X. Zhu
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2022, 423–428, https://doi.org/10.5194/isprs-archives-XLIII-B3-2022-423-2022, https://doi.org/10.5194/isprs-archives-XLIII-B3-2022-423-2022, 2022
T. Beker, H. Ansari, S. Montazeri, Q. Song, and X. X. Zhu
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-3-2022, 85–92, https://doi.org/10.5194/isprs-annals-V-3-2022-85-2022, https://doi.org/10.5194/isprs-annals-V-3-2022-85-2022, 2022
K. R. Traoré, A. Camero, and X. X. Zhu
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-3-2022, 217–224, https://doi.org/10.5194/isprs-annals-V-3-2022-217-2022, https://doi.org/10.5194/isprs-annals-V-3-2022-217-2022, 2022
Y. Xie, K. Schindler, J. Tian, and X. X. Zhu
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B2-2021, 247–254, https://doi.org/10.5194/isprs-archives-XLIII-B2-2021-247-2021, https://doi.org/10.5194/isprs-archives-XLIII-B2-2021-247-2021, 2021
P. Ebel, S. Saha, and X. X. Zhu
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2021, 243–249, https://doi.org/10.5194/isprs-archives-XLIII-B3-2021-243-2021, https://doi.org/10.5194/isprs-archives-XLIII-B3-2021-243-2021, 2021
S. Saha, L. Kondmann, and X. X. Zhu
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-3-2021, 311–316, https://doi.org/10.5194/isprs-annals-V-3-2021-311-2021, https://doi.org/10.5194/isprs-annals-V-3-2021-311-2021, 2021
D. Hong, J. Yao, X. Wu, J. Chanussot, and X. Zhu
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2020, 423–428, https://doi.org/10.5194/isprs-archives-XLIII-B3-2020-423-2020, https://doi.org/10.5194/isprs-archives-XLIII-B3-2020-423-2020, 2020
J. Hu, L. Mou, and X. X. Zhu
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B2-2020, 1569–1574, https://doi.org/10.5194/isprs-archives-XLIII-B2-2020-1569-2020, https://doi.org/10.5194/isprs-archives-XLIII-B2-2020-1569-2020, 2020
Q. Li, Y. Shi, S. Auer, R. Roschlaub, K. Möst, M. Schmitt, and X. X. Zhu
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-2-2020, 517–524, https://doi.org/10.5194/isprs-annals-V-2-2020-517-2020, https://doi.org/10.5194/isprs-annals-V-2-2020-517-2020, 2020
L. Mou, Y. Hua, P. Jin, and X. X. Zhu
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-2-2020, 533–540, https://doi.org/10.5194/isprs-annals-V-2-2020-533-2020, https://doi.org/10.5194/isprs-annals-V-2-2020-533-2020, 2020
C. Qiu, P. Gamba, M. Schmitt, and X. X. Zhu
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-3-2020, 787–794, https://doi.org/10.5194/isprs-annals-V-3-2020-787-2020, https://doi.org/10.5194/isprs-annals-V-3-2020-787-2020, 2020
M. Schmitt, J. Prexl, P. Ebel, L. Liebel, and X. X. Zhu
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-3-2020, 795–802, https://doi.org/10.5194/isprs-annals-V-3-2020-795-2020, https://doi.org/10.5194/isprs-annals-V-3-2020-795-2020, 2020
L. H. Hughes and M. Schmitt
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-2-W7, 71–78, https://doi.org/10.5194/isprs-annals-IV-2-W7-71-2019, https://doi.org/10.5194/isprs-annals-IV-2-W7-71-2019, 2019
M. Schmitt, L. H. Hughes, C. Qiu, and X. X. Zhu
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-2-W7, 153–160, https://doi.org/10.5194/isprs-annals-IV-2-W7-153-2019, https://doi.org/10.5194/isprs-annals-IV-2-W7-153-2019, 2019
M. Schmitt, L. H. Hughes, and X. X. Zhu
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-1, 141–146, https://doi.org/10.5194/isprs-annals-IV-1-141-2018, https://doi.org/10.5194/isprs-annals-IV-1-141-2018, 2018
H. Bagheri, M. Schmitt, P. d’Angelo, and X. X. Zhu
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2, 43–48, https://doi.org/10.5194/isprs-archives-XLII-2-43-2018, https://doi.org/10.5194/isprs-archives-XLII-2-43-2018, 2018
C. P. Qiu, M. Schmitt, P. Ghamisi, and X. X. Zhu
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2, 931–936, https://doi.org/10.5194/isprs-archives-XLII-2-931-2018, https://doi.org/10.5194/isprs-archives-XLII-2-931-2018, 2018
M. Schmitt, L. H. Hughes, M. Körner, and X. X. Zhu
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2, 1045–1051, https://doi.org/10.5194/isprs-archives-XLII-2-1045-2018, https://doi.org/10.5194/isprs-archives-XLII-2-1045-2018, 2018
L. H. Hughes, S. Auer, and M. Schmitt
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-2, 129–136, https://doi.org/10.5194/isprs-annals-IV-2-129-2018, https://doi.org/10.5194/isprs-annals-IV-2-129-2018, 2018
C. P. Qiu, M. Schmitt, and X. X. Zhu
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-1-W1, 411–417, https://doi.org/10.5194/isprs-archives-XLII-1-W1-411-2017, https://doi.org/10.5194/isprs-archives-XLII-1-W1-411-2017, 2017
H. Bagheri, M. Schmitt, and X. X. Zhu
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-1-W1, 433–439, https://doi.org/10.5194/isprs-archives-XLII-1-W1-433-2017, https://doi.org/10.5194/isprs-archives-XLII-1-W1-433-2017, 2017
The International Society for Photogrammetry and Remote Sensing is a non-governmental organization devoted to the development of international cooperation for the advancement of photogrammetry and remote sensing and their applications. The Society operates without any discrimination on grounds of race, religion, nationality, or political philosophy.
Useful Links
Useful External Links
Our Contact
c/o
Leibniz University Hannover
Institute of Photogrammetry and GeoInformation
Nienburger Str. 1
D-30167 Hannover
GERMANY
Email: isprs-sg@isprs.org