A Cloud and Cloud Shadow Detection Method Based on Fuzzy c-Means Algorithm
Cloud and cloud shadow detection is an important preprocess before using satellite images for different applications. It can be considered as a classification process, in which the objective pixels are partitioned into cloud/cloud shadow or non-cloud/non-cloud shadow classes. However, some cloud pix...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2020-01-01
|
| Series: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9076268/ |
| _version_ | 1819011825736351744 |
|---|---|
| author | Ping Bo Su Fenzhen Meng Yunshan |
| author_facet | Ping Bo Su Fenzhen Meng Yunshan |
| author_sort | Ping Bo |
| collection | DOAJ |
| description | Cloud and cloud shadow detection is an important preprocess before using satellite images for different applications. It can be considered as a classification process, in which the objective pixels are partitioned into cloud/cloud shadow or non-cloud/non-cloud shadow classes. However, some cloud pixels, especially the thin cloud pixels, can be considered as a mixture of reflectances of clouds and land objects. In fuzzy clustering, the data points can belong to two or more clusters; hence, fuzzy clustering may better characterize the status of one given pixel belonging to clouds or non-clouds. The fuzzy c-means method (FCM), one typical fuzzy clustering method, was utilized in this study for cloud and cloud shadow detection. In addition, the “flood-fill” morphological transformation may misclassify some clear-sky areas surrounded by clouds as cloud shadows as a whole, so a modified cloud shadow index calculation was proposed. Moreover, a cloud and cloud shadow spatial matching strategy based on the projection direction and spatial coexistence was used to exclude some pseudo cloud shadows. Fewer predefined parameters and spectral bands are needed is one characteristic of the proposed method. In this study, 41 scenes including 27 Landsat ETM+ images in eight latitude zones and 14 Landsat OLI images comprising seven land cover types, including barren, forest, grass, shrubland, urban, water, and wetlands areas, with percentages of cloud cover from 4.99% to 97.63%, were utilized to confirm the validity of the FCM. The detected results demonstrate that the thick and thin clouds along with their associated cloud shadows can be precisely extracted by using the FCM. Compared with the function of mask (Fmask) method, the FCM has relatively lower producer agreement rates, but it misclassifies as clouds fewer clear-sky pixels; compared with the support vector machine (SVM) method, the FCM can achieve better cloud detection accuracy. The results demonstrate that the FCM can attain a better balance between cloud pixel detection and non-cloud pixel exclusion. |
| first_indexed | 2024-12-21T01:34:19Z |
| format | Article |
| id | doaj.art-8db48ea0415446fbaae2eeb8be86f656 |
| institution | Directory Open Access Journal |
| issn | 2151-1535 |
| language | English |
| last_indexed | 2024-12-21T01:34:19Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| spelling | doaj.art-8db48ea0415446fbaae2eeb8be86f6562022-12-21T19:20:18ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing2151-15352020-01-01131714172710.1109/JSTARS.2020.29878449076268A Cloud and Cloud Shadow Detection Method Based on Fuzzy c-Means AlgorithmPing Bo0https://orcid.org/0000-0002-2854-2261Su Fenzhen1Meng Yunshan2Institute of Surface-Earth System Science, Tianjin University, Tianjin, ChinaLREIS, University of the Chinese Academy of Sciences, Beijing, ChinaNational Marine Data and Information Service, Tianjin, ChinaCloud and cloud shadow detection is an important preprocess before using satellite images for different applications. It can be considered as a classification process, in which the objective pixels are partitioned into cloud/cloud shadow or non-cloud/non-cloud shadow classes. However, some cloud pixels, especially the thin cloud pixels, can be considered as a mixture of reflectances of clouds and land objects. In fuzzy clustering, the data points can belong to two or more clusters; hence, fuzzy clustering may better characterize the status of one given pixel belonging to clouds or non-clouds. The fuzzy c-means method (FCM), one typical fuzzy clustering method, was utilized in this study for cloud and cloud shadow detection. In addition, the “flood-fill” morphological transformation may misclassify some clear-sky areas surrounded by clouds as cloud shadows as a whole, so a modified cloud shadow index calculation was proposed. Moreover, a cloud and cloud shadow spatial matching strategy based on the projection direction and spatial coexistence was used to exclude some pseudo cloud shadows. Fewer predefined parameters and spectral bands are needed is one characteristic of the proposed method. In this study, 41 scenes including 27 Landsat ETM+ images in eight latitude zones and 14 Landsat OLI images comprising seven land cover types, including barren, forest, grass, shrubland, urban, water, and wetlands areas, with percentages of cloud cover from 4.99% to 97.63%, were utilized to confirm the validity of the FCM. The detected results demonstrate that the thick and thin clouds along with their associated cloud shadows can be precisely extracted by using the FCM. Compared with the function of mask (Fmask) method, the FCM has relatively lower producer agreement rates, but it misclassifies as clouds fewer clear-sky pixels; compared with the support vector machine (SVM) method, the FCM can achieve better cloud detection accuracy. The results demonstrate that the FCM can attain a better balance between cloud pixel detection and non-cloud pixel exclusion.https://ieeexplore.ieee.org/document/9076268/Cloud and cloud shadow detectionfuzzy c-means algorithmmultiple featuresmultispectral sensors |
| spellingShingle | Ping Bo Su Fenzhen Meng Yunshan A Cloud and Cloud Shadow Detection Method Based on Fuzzy c-Means Algorithm IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Cloud and cloud shadow detection fuzzy c-means algorithm multiple features multispectral sensors |
| title | A Cloud and Cloud Shadow Detection Method Based on Fuzzy c-Means Algorithm |
| title_full | A Cloud and Cloud Shadow Detection Method Based on Fuzzy c-Means Algorithm |
| title_fullStr | A Cloud and Cloud Shadow Detection Method Based on Fuzzy c-Means Algorithm |
| title_full_unstemmed | A Cloud and Cloud Shadow Detection Method Based on Fuzzy c-Means Algorithm |
| title_short | A Cloud and Cloud Shadow Detection Method Based on Fuzzy c-Means Algorithm |
| title_sort | cloud and cloud shadow detection method based on fuzzy c means algorithm |
| topic | Cloud and cloud shadow detection fuzzy c-means algorithm multiple features multispectral sensors |
| url | https://ieeexplore.ieee.org/document/9076268/ |
| work_keys_str_mv | AT pingbo acloudandcloudshadowdetectionmethodbasedonfuzzycmeansalgorithm AT sufenzhen acloudandcloudshadowdetectionmethodbasedonfuzzycmeansalgorithm AT mengyunshan acloudandcloudshadowdetectionmethodbasedonfuzzycmeansalgorithm AT pingbo cloudandcloudshadowdetectionmethodbasedonfuzzycmeansalgorithm AT sufenzhen cloudandcloudshadowdetectionmethodbasedonfuzzycmeansalgorithm AT mengyunshan cloudandcloudshadowdetectionmethodbasedonfuzzycmeansalgorithm |