Conference Paper/Proceeding/Abstract 185 views
Farm Detection based on Deep Convolutional Neural Nets and Semi-supervised Green Texture Detection using VIS-NIR Satellite Image
Proceedings of the 8th International Conference on Data Science, Technology and Applications
Swansea University Author: Sara Sharifzadeh
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DOI (Published version): 10.5220/0007954901000108
Abstract
Farm detection using low resolution satellite images is an important topic in digital agriculture. However, it has not received enough attention compared to high-resolution images. Although high resolution images are more efficient for detection of land cover components, the analysis of low-resoluti...
Published in: | Proceedings of the 8th International Conference on Data Science, Technology and Applications |
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ISBN: | 978-989-758-377-3 |
ISSN: | 2184-285X |
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SCITEPRESS - Science and Technology Publications
2019
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URI: | https://cronfa.swan.ac.uk/Record/cronfa65605 |
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v2 65605 2024-02-09 Farm Detection based on Deep Convolutional Neural Nets and Semi-supervised Green Texture Detection using VIS-NIR Satellite Image a4e15f304398ecee3f28c7faec69c1b0 0000-0003-4621-2917 Sara Sharifzadeh Sara Sharifzadeh true false 2024-02-09 SCS Farm detection using low resolution satellite images is an important topic in digital agriculture. However, it has not received enough attention compared to high-resolution images. Although high resolution images are more efficient for detection of land cover components, the analysis of low-resolution images are yet important due to the low-resolution repositories of the past satellite images used for timeseries analysis, free availability and economic concerns. The current paper addresses the problem of farm detection using low resolution satellite images. In digital agriculture, farm detection has significant role for key applications such as crop yield monitoring. Two main categories of object detection strategies are studied and compared in this paper; First, a two-step semi-supervised methodology is developed using traditional manual feature extraction and modelling techniques; the developed methodology uses the Normalized Difference Moisture Index (NDMI), Grey Level Co-occurren ce Matrix (GLCM), 2-D Discrete Cosine Transform (DCT) and morphological features and Support Vector Machine (SVM) for classifier modelling. In the second strategy, high-level features learnt from the massive filter banks of deep Convolutional Neural Networks (CNNs) are utilised. Transfer learning strategies are employed for pretrained Visual Geometry Group Network (VGG-16) networks. Results show the superiority of the high-level features for classification of farm regions. Conference Paper/Proceeding/Abstract Proceedings of the 8th International Conference on Data Science, Technology and Applications SCITEPRESS - Science and Technology Publications 978-989-758-377-3 2184-285X Classification, Supervised Feature Extraction, Convolutional Neural Nets (CNNs), Satellite Image, Digital Agriculture 1 1 2019 2019-01-01 10.5220/0007954901000108 COLLEGE NANME Computer Science COLLEGE CODE SCS Swansea University 2024-03-23T15:12:38.5298426 2024-02-09T01:16:19.5388472 Faculty of Science and Engineering School of Mathematics and Computer Science - Computer Science Sara Sharifzadeh 0000-0003-4621-2917 1 Jagati Tata 2 Bo Tan 3 |
title |
Farm Detection based on Deep Convolutional Neural Nets and Semi-supervised Green Texture Detection using VIS-NIR Satellite Image |
spellingShingle |
Farm Detection based on Deep Convolutional Neural Nets and Semi-supervised Green Texture Detection using VIS-NIR Satellite Image Sara Sharifzadeh |
title_short |
Farm Detection based on Deep Convolutional Neural Nets and Semi-supervised Green Texture Detection using VIS-NIR Satellite Image |
title_full |
Farm Detection based on Deep Convolutional Neural Nets and Semi-supervised Green Texture Detection using VIS-NIR Satellite Image |
title_fullStr |
Farm Detection based on Deep Convolutional Neural Nets and Semi-supervised Green Texture Detection using VIS-NIR Satellite Image |
title_full_unstemmed |
Farm Detection based on Deep Convolutional Neural Nets and Semi-supervised Green Texture Detection using VIS-NIR Satellite Image |
title_sort |
Farm Detection based on Deep Convolutional Neural Nets and Semi-supervised Green Texture Detection using VIS-NIR Satellite Image |
author_id_str_mv |
a4e15f304398ecee3f28c7faec69c1b0 |
author_id_fullname_str_mv |
a4e15f304398ecee3f28c7faec69c1b0_***_Sara Sharifzadeh |
author |
Sara Sharifzadeh |
author2 |
Sara Sharifzadeh Jagati Tata Bo Tan |
format |
Conference Paper/Proceeding/Abstract |
container_title |
Proceedings of the 8th International Conference on Data Science, Technology and Applications |
publishDate |
2019 |
institution |
Swansea University |
isbn |
978-989-758-377-3 |
issn |
2184-285X |
doi_str_mv |
10.5220/0007954901000108 |
publisher |
SCITEPRESS - Science and Technology Publications |
college_str |
Faculty of Science and Engineering |
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facultyofscienceandengineering |
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Faculty of Science and Engineering |
hierarchy_parent_id |
facultyofscienceandengineering |
hierarchy_parent_title |
Faculty of Science and Engineering |
department_str |
School of Mathematics and Computer Science - Computer Science{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Mathematics and Computer Science - Computer Science |
document_store_str |
0 |
active_str |
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description |
Farm detection using low resolution satellite images is an important topic in digital agriculture. However, it has not received enough attention compared to high-resolution images. Although high resolution images are more efficient for detection of land cover components, the analysis of low-resolution images are yet important due to the low-resolution repositories of the past satellite images used for timeseries analysis, free availability and economic concerns. The current paper addresses the problem of farm detection using low resolution satellite images. In digital agriculture, farm detection has significant role for key applications such as crop yield monitoring. Two main categories of object detection strategies are studied and compared in this paper; First, a two-step semi-supervised methodology is developed using traditional manual feature extraction and modelling techniques; the developed methodology uses the Normalized Difference Moisture Index (NDMI), Grey Level Co-occurren ce Matrix (GLCM), 2-D Discrete Cosine Transform (DCT) and morphological features and Support Vector Machine (SVM) for classifier modelling. In the second strategy, high-level features learnt from the massive filter banks of deep Convolutional Neural Networks (CNNs) are utilised. Transfer learning strategies are employed for pretrained Visual Geometry Group Network (VGG-16) networks. Results show the superiority of the high-level features for classification of farm regions. |
published_date |
2019-01-01T15:12:34Z |
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1794330334227070976 |
score |
11.0267 |