Satellite Image Classification Using a Hybrid Manta Ray Foraging Optimization Neural Network
A semi supervised image classification method for satellite images is proposed in this paper. The satellite images contain enormous data that can be used in various applications. The analysis of the data is a tedious task due to the amount of data and the heterogeneity of the data. Thus, in this pap...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Tsinghua University Press
2023-03-01
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| Series: | Big Data Mining and Analytics |
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| Online Access: | https://www.sciopen.com/article/10.26599/BDMA.2022.9020027 |
| _version_ | 1811198117134991360 |
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| author | Amit Kumar Rai Nirupama Mandal Krishna Kant Singh Ivan Izonin |
| author_facet | Amit Kumar Rai Nirupama Mandal Krishna Kant Singh Ivan Izonin |
| author_sort | Amit Kumar Rai |
| collection | DOAJ |
| description | A semi supervised image classification method for satellite images is proposed in this paper. The satellite images contain enormous data that can be used in various applications. The analysis of the data is a tedious task due to the amount of data and the heterogeneity of the data. Thus, in this paper, a Radial Basis Function Neural Network (RBFNN) trained using Manta Ray Foraging Optimization algorithm (MRFO) is proposed. RBFNN is a three-layer network comprising of input, output, and hidden layers that can process large amounts. The trained network can discover hidden data patterns in unseen data. The learning algorithm and seed selection play a vital role in the performance of the network. The seed selection is done using the spectral indices to further improve the performance of the network. The manta ray foraging optimization algorithm is inspired by the intelligent behaviour of manta rays. It emulates three unique foraging behaviours namelys chain, cyclone, and somersault foraging. The satellite images contain enormous amount of data and thus require exploration in large search space. The spiral movement of the MRFO algorithm enables it to explore large search spaces effectively. The proposed method is applied on pre and post flooding Landsat 8 Operational Land Imager (OLI) images of New Brunswick area. The method was applied to identify and classify the land cover changes in the area induced by flooding. The images are classified using the proposed method and a change map is developed using post classification comparison. The change map shows that a large amount of agricultural area was washed away due to flooding. The measurement of the affected area in square kilometres is also performed for mitigation activities. The results show that post flooding the area covered by water is increased whereas the vegetated area is decreased. The performance of the proposed method is done with existing state-of-the-art methods. |
| first_indexed | 2024-04-12T01:25:27Z |
| format | Article |
| id | doaj.art-79c355bed71d4a8bad89d3a3a9d94c73 |
| institution | Directory Open Access Journal |
| issn | 2096-0654 |
| language | English |
| last_indexed | 2024-04-12T01:25:27Z |
| publishDate | 2023-03-01 |
| publisher | Tsinghua University Press |
| record_format | Article |
| series | Big Data Mining and Analytics |
| spelling | doaj.art-79c355bed71d4a8bad89d3a3a9d94c732022-12-22T03:53:40ZengTsinghua University PressBig Data Mining and Analytics2096-06542023-03-0161445410.26599/BDMA.2022.9020027Satellite Image Classification Using a Hybrid Manta Ray Foraging Optimization Neural NetworkAmit Kumar Rai0Nirupama Mandal1Krishna Kant Singh2Ivan Izonin3Department of Electronics Engineering, Indian Institute of Technology, Dhanbad (ISM, Dhanbad), Dhanbad 826004, IndiaDepartment of Electronics Engineering, Indian Institute of Technology, Dhanbad (ISM, Dhanbad), Dhanbad 826004, IndiaDepartment of CSE, ASET, Amity University, Noida 201301, IndiaDepartment of Artificial Intelligence, Lviv Polytechnic National University, Lviv 79000, UkraineA semi supervised image classification method for satellite images is proposed in this paper. The satellite images contain enormous data that can be used in various applications. The analysis of the data is a tedious task due to the amount of data and the heterogeneity of the data. Thus, in this paper, a Radial Basis Function Neural Network (RBFNN) trained using Manta Ray Foraging Optimization algorithm (MRFO) is proposed. RBFNN is a three-layer network comprising of input, output, and hidden layers that can process large amounts. The trained network can discover hidden data patterns in unseen data. The learning algorithm and seed selection play a vital role in the performance of the network. The seed selection is done using the spectral indices to further improve the performance of the network. The manta ray foraging optimization algorithm is inspired by the intelligent behaviour of manta rays. It emulates three unique foraging behaviours namelys chain, cyclone, and somersault foraging. The satellite images contain enormous amount of data and thus require exploration in large search space. The spiral movement of the MRFO algorithm enables it to explore large search spaces effectively. The proposed method is applied on pre and post flooding Landsat 8 Operational Land Imager (OLI) images of New Brunswick area. The method was applied to identify and classify the land cover changes in the area induced by flooding. The images are classified using the proposed method and a change map is developed using post classification comparison. The change map shows that a large amount of agricultural area was washed away due to flooding. The measurement of the affected area in square kilometres is also performed for mitigation activities. The results show that post flooding the area covered by water is increased whereas the vegetated area is decreased. The performance of the proposed method is done with existing state-of-the-art methods.https://www.sciopen.com/article/10.26599/BDMA.2022.9020027radial basis function neural network (rbfnn)manta ray foraging optimization algorithm (mrfo)landsat 8classificationchange detectiondisaster mitigationplanning |
| spellingShingle | Amit Kumar Rai Nirupama Mandal Krishna Kant Singh Ivan Izonin Satellite Image Classification Using a Hybrid Manta Ray Foraging Optimization Neural Network Big Data Mining and Analytics radial basis function neural network (rbfnn) manta ray foraging optimization algorithm (mrfo) landsat 8 classification change detection disaster mitigation planning |
| title | Satellite Image Classification Using a Hybrid Manta Ray Foraging Optimization Neural Network |
| title_full | Satellite Image Classification Using a Hybrid Manta Ray Foraging Optimization Neural Network |
| title_fullStr | Satellite Image Classification Using a Hybrid Manta Ray Foraging Optimization Neural Network |
| title_full_unstemmed | Satellite Image Classification Using a Hybrid Manta Ray Foraging Optimization Neural Network |
| title_short | Satellite Image Classification Using a Hybrid Manta Ray Foraging Optimization Neural Network |
| title_sort | satellite image classification using a hybrid manta ray foraging optimization neural network |
| topic | radial basis function neural network (rbfnn) manta ray foraging optimization algorithm (mrfo) landsat 8 classification change detection disaster mitigation planning |
| url | https://www.sciopen.com/article/10.26599/BDMA.2022.9020027 |
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