Abstract
Information on cultivated crops is relevant for a large number of food security studies. Different scientific efforts are dedicated to generate this information from remote sensing images by means of machine learning methods. Unfortunately, these methods do not take account of the spatial-temporal relationships inherent in remote sensing images. In our paper, we explore the capability of a 3D Fully Convolutional Neural Network (FCN) to map crop types from multi-temporal images. In addition, we propose the Intersection Over Union (IOU) loss function for increasing the overlap between the predicted classes and ground reference data. The proposed method was applied to identify soybean and corn from a study area situated in the US corn belt using multi-temporal Landsat images. The study shows that our method outperforms related methods, obtaining a Kappa coefficient of 91.8%. We conclude that using the IOU loss function provides a superior choice to learn individual crop types.
Original language | English |
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Pages | 5834-5837 |
Number of pages | 4 |
DOIs | |
Publication status | Published - 2021 |
Event | 2021 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2021 - Brussels, Brussels, Belgium Duration: 12 Jul 2021 → 16 Jul 2021 https://igarss2021.com |
Conference
Conference | 2021 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2021 |
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Abbreviated title | IGARSS 2021 |
Country/Territory | Belgium |
City | Brussels |
Period | 12/07/21 → 16/07/21 |
Internet address |
Keywords
- Crop mapping
- deep learning
- fully convolutional neural networks
- time series
- ITC-GREEN
- 2024 OA procedure