Cloud filtering for Landsat TM satellite images using multiple temporal mosaicing

Yi Guo; Feng Li; Peter Caccetta; Drew Devereux; Mark Berman

doi:10.1109/IGARSS.2016.7730888

Cloud filtering for Landsat TM satellite images using multiple temporal mosaicing

Yi Guo, Feng Li, Peter Caccetta, Drew Devereux, Mark Berman

Western Sydney University

Research output: Chapter in Book / Conference Paper › Conference Paper › peer-review

8 Citations (Scopus)

Abstract

Cloud removal is a very important preprocessing step in remote sensing image analysis. In some remote sensing applications, a clean image free of cloud composite from a series of images taken in a short period of time will suffice for further analysis. This task is primarily carried out manually and time consuming. It is highly desirable to have some fully automated method to solve this problem efficiently. To this end, we propose a method called multiple temporal mosaicing as it is mimicking the process of mosacing pieces from images to obtain a whole image. We tested this method on Landsat TM 5 and 7 scenes. The resulting images show the effectiveness of this method clearly.

Original language	English
Title of host publication	Proceedings of the 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2016): Advancing the Understanding of our Living Planet, 10-15 July 2016, Beijing, China
Publisher	IEEE
Pages	7240-7243
Number of pages	4
ISBN (Print)	9781509033324
DOIs	https://doi.org/10.1109/IGARSS.2016.7730888
Publication status	Published - 2016
Event	International Geoscience and Remote Sensing Symposium - Duration: 10 Jul 2016 → …

Publication series

Name
ISSN (Print)	2153-7003

Conference

Conference	International Geoscience and Remote Sensing Symposium
Period	10/07/16 → …

Keywords

image segmentation
remote sensing

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10.1109/IGARSS.2016.7730888

Cite this

Guo, Y., Li, F., Caccetta, P., Devereux, D., & Berman, M. (2016). Cloud filtering for Landsat TM satellite images using multiple temporal mosaicing. In Proceedings of the 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2016): Advancing the Understanding of our Living Planet, 10-15 July 2016, Beijing, China (pp. 7240-7243). IEEE. https://doi.org/10.1109/IGARSS.2016.7730888

@inproceedings{2152c896b12a426a9c629ef0ce0707a0,

title = "Cloud filtering for Landsat TM satellite images using multiple temporal mosaicing",

abstract = "Cloud removal is a very important preprocessing step in remote sensing image analysis. In some remote sensing applications, a clean image free of cloud composite from a series of images taken in a short period of time will suffice for further analysis. This task is primarily carried out manually and time consuming. It is highly desirable to have some fully automated method to solve this problem efficiently. To this end, we propose a method called multiple temporal mosaicing as it is mimicking the process of mosacing pieces from images to obtain a whole image. We tested this method on Landsat TM 5 and 7 scenes. The resulting images show the effectiveness of this method clearly.",

keywords = "image segmentation, remote sensing",

author = "Yi Guo and Feng Li and Peter Caccetta and Drew Devereux and Mark Berman",

year = "2016",

doi = "10.1109/IGARSS.2016.7730888",

language = "English",

isbn = "9781509033324",

publisher = "IEEE",

pages = "7240--7243",

booktitle = "Proceedings of the 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2016): Advancing the Understanding of our Living Planet, 10-15 July 2016, Beijing, China",

note = "International Geoscience and Remote Sensing Symposium ; Conference date: 10-07-2016",

}

Guo, Y, Li, F, Caccetta, P, Devereux, D & Berman, M 2016, Cloud filtering for Landsat TM satellite images using multiple temporal mosaicing. in Proceedings of the 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2016): Advancing the Understanding of our Living Planet, 10-15 July 2016, Beijing, China. IEEE, pp. 7240-7243, International Geoscience and Remote Sensing Symposium, 10/07/16. https://doi.org/10.1109/IGARSS.2016.7730888

Cloud filtering for Landsat TM satellite images using multiple temporal mosaicing. / Guo, Yi; Li, Feng; Caccetta, Peter et al.
Proceedings of the 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2016): Advancing the Understanding of our Living Planet, 10-15 July 2016, Beijing, China. IEEE, 2016. p. 7240-7243.

Research output: Chapter in Book / Conference Paper › Conference Paper › peer-review

TY - GEN

T1 - Cloud filtering for Landsat TM satellite images using multiple temporal mosaicing

AU - Guo, Yi

AU - Li, Feng

AU - Caccetta, Peter

AU - Devereux, Drew

AU - Berman, Mark

PY - 2016

Y1 - 2016

N2 - Cloud removal is a very important preprocessing step in remote sensing image analysis. In some remote sensing applications, a clean image free of cloud composite from a series of images taken in a short period of time will suffice for further analysis. This task is primarily carried out manually and time consuming. It is highly desirable to have some fully automated method to solve this problem efficiently. To this end, we propose a method called multiple temporal mosaicing as it is mimicking the process of mosacing pieces from images to obtain a whole image. We tested this method on Landsat TM 5 and 7 scenes. The resulting images show the effectiveness of this method clearly.

AB - Cloud removal is a very important preprocessing step in remote sensing image analysis. In some remote sensing applications, a clean image free of cloud composite from a series of images taken in a short period of time will suffice for further analysis. This task is primarily carried out manually and time consuming. It is highly desirable to have some fully automated method to solve this problem efficiently. To this end, we propose a method called multiple temporal mosaicing as it is mimicking the process of mosacing pieces from images to obtain a whole image. We tested this method on Landsat TM 5 and 7 scenes. The resulting images show the effectiveness of this method clearly.

KW - image segmentation

KW - remote sensing

UR - http://handle.westernsydney.edu.au:8081/1959.7/uws:38117

U2 - 10.1109/IGARSS.2016.7730888

DO - 10.1109/IGARSS.2016.7730888

M3 - Conference Paper

SN - 9781509033324

SP - 7240

EP - 7243

BT - Proceedings of the 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2016): Advancing the Understanding of our Living Planet, 10-15 July 2016, Beijing, China

PB - IEEE

T2 - International Geoscience and Remote Sensing Symposium

Y2 - 10 July 2016

ER -

Guo Y, Li F, Caccetta P, Devereux D, Berman M. Cloud filtering for Landsat TM satellite images using multiple temporal mosaicing. In Proceedings of the 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2016): Advancing the Understanding of our Living Planet, 10-15 July 2016, Beijing, China. IEEE. 2016. p. 7240-7243 doi: 10.1109/IGARSS.2016.7730888

Cloud filtering for Landsat TM satellite images using multiple temporal mosaicing

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