Green cover change detection using a modified adaptive ensemble of extreme learning machines for North-Western India
Climate change is the biggest challenge faced by the world. It has already started affecting the weather patterns leading to disruption of normal life. Detecting change helps to monitor and plan the Earth’s resources in an efficient manner. An important factor in climate change is the change in the...
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| Format: | Article |
| Language: | English |
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Elsevier
2021-12-01
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| Series: | Journal of King Saud University: Computer and Information Sciences |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1319157818303665 |
| _version_ | 1818823013757353984 |
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| author | Madhu Khurana Vikas Saxena |
| author_facet | Madhu Khurana Vikas Saxena |
| author_sort | Madhu Khurana |
| collection | DOAJ |
| description | Climate change is the biggest challenge faced by the world. It has already started affecting the weather patterns leading to disruption of normal life. Detecting change helps to monitor and plan the Earth’s resources in an efficient manner. An important factor in climate change is the change in the green cover. Non-availability of standard datasets and limited labeled data points makes it difficult to attain high accuracies in change detection. In this paper, we have proposed a modified ensemble of extreme learning machines (mAEELM) for change detection in green cover to increase the accuracy of the change detection process. It uses Extreme Learning Machine (ELM) as the base classifier. Different ELMs are trained with different configuration so that they have different learning capabilities. These ELMs are combined to create an ensemble and it is then adapted based on the accuracy of the individual ELMs. The ensemble is then pruned to eliminate the ELMs which are not contributing towards the overall result of the ensemble, to make it more efficient. The proposed algorithm has been applied for detecting change on two areas of Gandhuan, Punjab and Chaparkaura Kham, Uttar Pradesh, India. The algorithm shows an average accuracy of 97.8% on both the datasets. |
| first_indexed | 2024-12-18T23:33:14Z |
| format | Article |
| id | doaj.art-24bc244b8143481eb404706cf795cdf0 |
| institution | Directory Open Access Journal |
| issn | 1319-1578 |
| language | English |
| last_indexed | 2024-12-18T23:33:14Z |
| publishDate | 2021-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Journal of King Saud University: Computer and Information Sciences |
| spelling | doaj.art-24bc244b8143481eb404706cf795cdf02022-12-21T20:47:37ZengElsevierJournal of King Saud University: Computer and Information Sciences1319-15782021-12-01331012651273Green cover change detection using a modified adaptive ensemble of extreme learning machines for North-Western IndiaMadhu Khurana0Vikas Saxena1Corresponding author.; Dept of CSE & IT, Jaypee Institute of Information Technology, A-10, Sector-62, Noida, Uttar Pradesh 201309, IndiaDept of CSE & IT, Jaypee Institute of Information Technology, A-10, Sector-62, Noida, Uttar Pradesh 201309, IndiaClimate change is the biggest challenge faced by the world. It has already started affecting the weather patterns leading to disruption of normal life. Detecting change helps to monitor and plan the Earth’s resources in an efficient manner. An important factor in climate change is the change in the green cover. Non-availability of standard datasets and limited labeled data points makes it difficult to attain high accuracies in change detection. In this paper, we have proposed a modified ensemble of extreme learning machines (mAEELM) for change detection in green cover to increase the accuracy of the change detection process. It uses Extreme Learning Machine (ELM) as the base classifier. Different ELMs are trained with different configuration so that they have different learning capabilities. These ELMs are combined to create an ensemble and it is then adapted based on the accuracy of the individual ELMs. The ensemble is then pruned to eliminate the ELMs which are not contributing towards the overall result of the ensemble, to make it more efficient. The proposed algorithm has been applied for detecting change on two areas of Gandhuan, Punjab and Chaparkaura Kham, Uttar Pradesh, India. The algorithm shows an average accuracy of 97.8% on both the datasets.http://www.sciencedirect.com/science/article/pii/S1319157818303665Change detectionExtreme learning machineAdaptive ensemble of extreme learning machinesGreen coverNDVI |
| spellingShingle | Madhu Khurana Vikas Saxena Green cover change detection using a modified adaptive ensemble of extreme learning machines for North-Western India Journal of King Saud University: Computer and Information Sciences Change detection Extreme learning machine Adaptive ensemble of extreme learning machines Green cover NDVI |
| title | Green cover change detection using a modified adaptive ensemble of extreme learning machines for North-Western India |
| title_full | Green cover change detection using a modified adaptive ensemble of extreme learning machines for North-Western India |
| title_fullStr | Green cover change detection using a modified adaptive ensemble of extreme learning machines for North-Western India |
| title_full_unstemmed | Green cover change detection using a modified adaptive ensemble of extreme learning machines for North-Western India |
| title_short | Green cover change detection using a modified adaptive ensemble of extreme learning machines for North-Western India |
| title_sort | green cover change detection using a modified adaptive ensemble of extreme learning machines for north western india |
| topic | Change detection Extreme learning machine Adaptive ensemble of extreme learning machines Green cover NDVI |
| url | http://www.sciencedirect.com/science/article/pii/S1319157818303665 |
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