Cost-effective land cover classification for remote sensing images
Abstract Land cover maps are of vital importance to various fields such as land use policy development, ecosystem services, urban planning and agriculture monitoring, which are mainly generated from remote sensing image classification techniques. Traditional land cover classification usually needs t...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
SpringerOpen
2022-10-01
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| Series: | Journal of Cloud Computing: Advances, Systems and Applications |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s13677-022-00335-0 |
| _version_ | 1811194892013010944 |
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| author | Dongwei Li Shuliang Wang Qiang He Yun Yang |
| author_facet | Dongwei Li Shuliang Wang Qiang He Yun Yang |
| author_sort | Dongwei Li |
| collection | DOAJ |
| description | Abstract Land cover maps are of vital importance to various fields such as land use policy development, ecosystem services, urban planning and agriculture monitoring, which are mainly generated from remote sensing image classification techniques. Traditional land cover classification usually needs tremendous computational resources, which often becomes a huge burden to the remote sensing community. Undoubtedly cloud computing is one of the best choices for land cover classification, however, if not managed properly, the computation cost on the cloud could be surprisingly high. Recently, cutting the unnecessary computation long tail has become a promising solution for saving cost in the cloud. For land cover classification, it is generally not necessary to achieve the best accuracy and 85% can be regarded as a reliable land cover classification. Therefore, in this paper, we propose a framework for cost-effective remote sensing classification. Given the desired accuracy, the clustering algorithm can stop early for cost-saving whilst achieving sufficient accuracy for land cover image classification. Experimental results show that achieving 85%-99.9% accuracy needs only 27.34%-60.83% of the total cloud computation cost for achieving a 100% accuracy. To put it into perspective, for the US land cover classification example, the proposed approach can save over $1,593,490.18 for the government in each single-use when the desired accuracy is 90%. |
| first_indexed | 2024-04-12T00:34:58Z |
| format | Article |
| id | doaj.art-2e966711f0f14c9ca3421d83ba7da8e6 |
| institution | Directory Open Access Journal |
| issn | 2192-113X |
| language | English |
| last_indexed | 2024-04-12T00:34:58Z |
| publishDate | 2022-10-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Journal of Cloud Computing: Advances, Systems and Applications |
| spelling | doaj.art-2e966711f0f14c9ca3421d83ba7da8e62022-12-22T03:55:11ZengSpringerOpenJournal of Cloud Computing: Advances, Systems and Applications2192-113X2022-10-0111111210.1186/s13677-022-00335-0Cost-effective land cover classification for remote sensing imagesDongwei Li0Shuliang Wang1Qiang He2Yun Yang3School of Computer Science and Technology, Beijing Institute of TechnologySchool of Computer Science and Technology, Beijing Institute of TechnologyDepartment of Computing Technologies, Swinburne University of TechnologyDepartment of Computing Technologies, Swinburne University of TechnologyAbstract Land cover maps are of vital importance to various fields such as land use policy development, ecosystem services, urban planning and agriculture monitoring, which are mainly generated from remote sensing image classification techniques. Traditional land cover classification usually needs tremendous computational resources, which often becomes a huge burden to the remote sensing community. Undoubtedly cloud computing is one of the best choices for land cover classification, however, if not managed properly, the computation cost on the cloud could be surprisingly high. Recently, cutting the unnecessary computation long tail has become a promising solution for saving cost in the cloud. For land cover classification, it is generally not necessary to achieve the best accuracy and 85% can be regarded as a reliable land cover classification. Therefore, in this paper, we propose a framework for cost-effective remote sensing classification. Given the desired accuracy, the clustering algorithm can stop early for cost-saving whilst achieving sufficient accuracy for land cover image classification. Experimental results show that achieving 85%-99.9% accuracy needs only 27.34%-60.83% of the total cloud computation cost for achieving a 100% accuracy. To put it into perspective, for the US land cover classification example, the proposed approach can save over $1,593,490.18 for the government in each single-use when the desired accuracy is 90%.https://doi.org/10.1186/s13677-022-00335-0Remote sensingLand cover classificationCloud computingFCM algorithm |
| spellingShingle | Dongwei Li Shuliang Wang Qiang He Yun Yang Cost-effective land cover classification for remote sensing images Journal of Cloud Computing: Advances, Systems and Applications Remote sensing Land cover classification Cloud computing FCM algorithm |
| title | Cost-effective land cover classification for remote sensing images |
| title_full | Cost-effective land cover classification for remote sensing images |
| title_fullStr | Cost-effective land cover classification for remote sensing images |
| title_full_unstemmed | Cost-effective land cover classification for remote sensing images |
| title_short | Cost-effective land cover classification for remote sensing images |
| title_sort | cost effective land cover classification for remote sensing images |
| topic | Remote sensing Land cover classification Cloud computing FCM algorithm |
| url | https://doi.org/10.1186/s13677-022-00335-0 |
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