An Improved Cloud Detection Method for GF-4 Imagery
Clouds are significant barriers to the application of optical remote sensing images. Accurate cloud detection can help to remove contaminated pixels and improve image quality. Many cloud detection methods have been developed. However, traditional methods either rely heavily on thermal infrared bands...
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2020-05-01
|
| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/12/9/1525 |
| _version_ | 1827717051789082624 |
|---|---|
| author | Ming Lu Feng Li Bangcheng Zhan He Li Xue Yang Xiaotian Lu Huachao Xiao |
| author_facet | Ming Lu Feng Li Bangcheng Zhan He Li Xue Yang Xiaotian Lu Huachao Xiao |
| author_sort | Ming Lu |
| collection | DOAJ |
| description | Clouds are significant barriers to the application of optical remote sensing images. Accurate cloud detection can help to remove contaminated pixels and improve image quality. Many cloud detection methods have been developed. However, traditional methods either rely heavily on thermal infrared bands or clear-sky images. When traditional cloud detection methods are used with Gaofen 4 (GF-4) imagery, it is very difficult to separate objects with similar spectra, such as ice, snow, and bright sand, from clouds. In this paper, we propose a new method, named Real-Time-Difference (RTD), to detect clouds using a pair of images obtained by the GF-4 satellite. The RTD method has four main steps: (1) data preprocessing, including transforming digital value (DN) to Top of Atmosphere (TOA) reflectance, and orthographic and geometric correction; (2) the computation of a series of cloud indexes for a single image to highlight clouds; (3) the calculation of the difference between a pair of real-time images in order to obtain moved clouds; and (4) confirming the clouds and background by analyzing their physical and dynamic features. The RTD method was validated in three sites located in the Hainan, Liaoning, and Xinjiang areas of China. The results were compared with those of a popular classifier, Support Vector Machine (SVM). The results showed that RTD outperformed SVM; for the Hainan, Liaoning, and Xinjiang areas, respectively, the overall accuracy of RTD reached 95.9%, 94.1%, and 93.9%, and its Kappa coefficient reached 0.92, 0.88, and 0.88. In the future, we expect RTD to be developed into an important means for the rapid detection of clouds that can be used on images from geostationary orbit satellites. |
| first_indexed | 2024-03-10T19:54:21Z |
| format | Article |
| id | doaj.art-3e06cccb5240488abc3d98db76e9a9af |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T19:54:21Z |
| publishDate | 2020-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-3e06cccb5240488abc3d98db76e9a9af2023-11-20T00:02:41ZengMDPI AGRemote Sensing2072-42922020-05-01129152510.3390/rs12091525An Improved Cloud Detection Method for GF-4 ImageryMing Lu0Feng Li1Bangcheng Zhan2He Li3Xue Yang4Xiaotian Lu5Huachao Xiao6Qian Xuesen Laboratory of Space Technology, Beijing 100094, ChinaQian Xuesen Laboratory of Space Technology, Beijing 100094, ChinaQian Xuesen Laboratory of Space Technology, Beijing 100094, ChinaState Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, ChinaQian Xuesen Laboratory of Space Technology, Beijing 100094, ChinaQian Xuesen Laboratory of Space Technology, Beijing 100094, ChinaAcademy of Space information System, Xi’an 710100, ChinaClouds are significant barriers to the application of optical remote sensing images. Accurate cloud detection can help to remove contaminated pixels and improve image quality. Many cloud detection methods have been developed. However, traditional methods either rely heavily on thermal infrared bands or clear-sky images. When traditional cloud detection methods are used with Gaofen 4 (GF-4) imagery, it is very difficult to separate objects with similar spectra, such as ice, snow, and bright sand, from clouds. In this paper, we propose a new method, named Real-Time-Difference (RTD), to detect clouds using a pair of images obtained by the GF-4 satellite. The RTD method has four main steps: (1) data preprocessing, including transforming digital value (DN) to Top of Atmosphere (TOA) reflectance, and orthographic and geometric correction; (2) the computation of a series of cloud indexes for a single image to highlight clouds; (3) the calculation of the difference between a pair of real-time images in order to obtain moved clouds; and (4) confirming the clouds and background by analyzing their physical and dynamic features. The RTD method was validated in three sites located in the Hainan, Liaoning, and Xinjiang areas of China. The results were compared with those of a popular classifier, Support Vector Machine (SVM). The results showed that RTD outperformed SVM; for the Hainan, Liaoning, and Xinjiang areas, respectively, the overall accuracy of RTD reached 95.9%, 94.1%, and 93.9%, and its Kappa coefficient reached 0.92, 0.88, and 0.88. In the future, we expect RTD to be developed into an important means for the rapid detection of clouds that can be used on images from geostationary orbit satellites.https://www.mdpi.com/2072-4292/12/9/1525cloud detectionGF-4real-time differenceremote sensing |
| spellingShingle | Ming Lu Feng Li Bangcheng Zhan He Li Xue Yang Xiaotian Lu Huachao Xiao An Improved Cloud Detection Method for GF-4 Imagery Remote Sensing cloud detection GF-4 real-time difference remote sensing |
| title | An Improved Cloud Detection Method for GF-4 Imagery |
| title_full | An Improved Cloud Detection Method for GF-4 Imagery |
| title_fullStr | An Improved Cloud Detection Method for GF-4 Imagery |
| title_full_unstemmed | An Improved Cloud Detection Method for GF-4 Imagery |
| title_short | An Improved Cloud Detection Method for GF-4 Imagery |
| title_sort | improved cloud detection method for gf 4 imagery |
| topic | cloud detection GF-4 real-time difference remote sensing |
| url | https://www.mdpi.com/2072-4292/12/9/1525 |
| work_keys_str_mv | AT minglu animprovedclouddetectionmethodforgf4imagery AT fengli animprovedclouddetectionmethodforgf4imagery AT bangchengzhan animprovedclouddetectionmethodforgf4imagery AT heli animprovedclouddetectionmethodforgf4imagery AT xueyang animprovedclouddetectionmethodforgf4imagery AT xiaotianlu animprovedclouddetectionmethodforgf4imagery AT huachaoxiao animprovedclouddetectionmethodforgf4imagery AT minglu improvedclouddetectionmethodforgf4imagery AT fengli improvedclouddetectionmethodforgf4imagery AT bangchengzhan improvedclouddetectionmethodforgf4imagery AT heli improvedclouddetectionmethodforgf4imagery AT xueyang improvedclouddetectionmethodforgf4imagery AT xiaotianlu improvedclouddetectionmethodforgf4imagery AT huachaoxiao improvedclouddetectionmethodforgf4imagery |