Detection and Classification of Land Crude Oil Spills Using Color Segmentation and Texture Analysis
Crude oil spills have negative consequences on the economy, environment, health and society in which they occur, and the severity of the consequences depends on how quickly these spills are detected once they begin. Several methods have been employed for spill detection, including real time remote s...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2017-10-01
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| Series: | Journal of Imaging |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2313-433X/3/4/47 |
| _version_ | 1828489744597647360 |
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| author | O’tega Ejofodomi Godswill Ofualagba |
| author_facet | O’tega Ejofodomi Godswill Ofualagba |
| author_sort | O’tega Ejofodomi |
| collection | DOAJ |
| description | Crude oil spills have negative consequences on the economy, environment, health and society in which they occur, and the severity of the consequences depends on how quickly these spills are detected once they begin. Several methods have been employed for spill detection, including real time remote surveillance by flying aircrafts with surveillance teams. Other methods employ various sensors, including visible sensors. This paper presents an algorithm to automatically detect the presence of crude oil spills in images acquired using visible light sensors. Images of crude oil spills used in the development of the algorithm were obtained from the Shell Petroleum Development Company (SPDC) Nigeria website The major steps of the detection algorithm are image preprocessing, crude oil color segmentation, sky elimination segmentation, Region of Interest (ROI) extraction, ROI texture feature extraction, and ROI texture feature analysis and classification. The algorithm was developed using 25 sample images containing crude oil spills and demonstrated a sensitivity of 92% and an FPI of 1.43. The algorithm was further tested on a set of 56 case images and demonstrated a sensitivity of 82% and an FPI of 0.66. This algorithm can be incorporated into spill detection systems that utilize visible sensors for early detection of crude oil spills. |
| first_indexed | 2024-12-11T10:31:24Z |
| format | Article |
| id | doaj.art-e5d4f96f8f6941b39ddd36933f6f7dbb |
| institution | Directory Open Access Journal |
| issn | 2313-433X |
| language | English |
| last_indexed | 2024-12-11T10:31:24Z |
| publishDate | 2017-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Imaging |
| spelling | doaj.art-e5d4f96f8f6941b39ddd36933f6f7dbb2022-12-22T01:10:56ZengMDPI AGJournal of Imaging2313-433X2017-10-01344710.3390/jimaging3040047jimaging3040047Detection and Classification of Land Crude Oil Spills Using Color Segmentation and Texture AnalysisO’tega Ejofodomi0Godswill Ofualagba1Racett Canada, Inc., 404 St. George Street, Moncton, NB E1C 1X0, CanadaRacett Canada, Inc., 404 St. George Street, Moncton, NB E1C 1X0, CanadaCrude oil spills have negative consequences on the economy, environment, health and society in which they occur, and the severity of the consequences depends on how quickly these spills are detected once they begin. Several methods have been employed for spill detection, including real time remote surveillance by flying aircrafts with surveillance teams. Other methods employ various sensors, including visible sensors. This paper presents an algorithm to automatically detect the presence of crude oil spills in images acquired using visible light sensors. Images of crude oil spills used in the development of the algorithm were obtained from the Shell Petroleum Development Company (SPDC) Nigeria website The major steps of the detection algorithm are image preprocessing, crude oil color segmentation, sky elimination segmentation, Region of Interest (ROI) extraction, ROI texture feature extraction, and ROI texture feature analysis and classification. The algorithm was developed using 25 sample images containing crude oil spills and demonstrated a sensitivity of 92% and an FPI of 1.43. The algorithm was further tested on a set of 56 case images and demonstrated a sensitivity of 82% and an FPI of 0.66. This algorithm can be incorporated into spill detection systems that utilize visible sensors for early detection of crude oil spills.https://www.mdpi.com/2313-433X/3/4/47crude oil spillscrude oil spill detectionimage segmentationtexture feature extractionautomated spill detection |
| spellingShingle | O’tega Ejofodomi Godswill Ofualagba Detection and Classification of Land Crude Oil Spills Using Color Segmentation and Texture Analysis Journal of Imaging crude oil spills crude oil spill detection image segmentation texture feature extraction automated spill detection |
| title | Detection and Classification of Land Crude Oil Spills Using Color Segmentation and Texture Analysis |
| title_full | Detection and Classification of Land Crude Oil Spills Using Color Segmentation and Texture Analysis |
| title_fullStr | Detection and Classification of Land Crude Oil Spills Using Color Segmentation and Texture Analysis |
| title_full_unstemmed | Detection and Classification of Land Crude Oil Spills Using Color Segmentation and Texture Analysis |
| title_short | Detection and Classification of Land Crude Oil Spills Using Color Segmentation and Texture Analysis |
| title_sort | detection and classification of land crude oil spills using color segmentation and texture analysis |
| topic | crude oil spills crude oil spill detection image segmentation texture feature extraction automated spill detection |
| url | https://www.mdpi.com/2313-433X/3/4/47 |
| work_keys_str_mv | AT otegaejofodomi detectionandclassificationoflandcrudeoilspillsusingcolorsegmentationandtextureanalysis AT godswillofualagba detectionandclassificationoflandcrudeoilspillsusingcolorsegmentationandtextureanalysis |