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
,
Jagati Tata,
Bo Tan
Proceedings of the 8th International Conference on Data Science, Technology and Applications
Swansea University Author:
Sara Sharifzadeh
Full text not available from this repository: check for access using links below.
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 |
| Published: |
SCITEPRESS - Science and Technology Publications
2019
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| Online Access: |
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| URI: | https://cronfa.swan.ac.uk/Record/cronfa65605 |
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| 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-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. |
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| Keywords: |
Classification, Supervised Feature Extraction, Convolutional Neural Nets (CNNs), Satellite Image, Digital Agriculture |
| College: |
Faculty of Science and Engineering |