Dealing with clouds and seasonal changes for center pivot irrigation systems detection using Instance segmentation in sentinel-2 time series
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| Autor Principal: | |
|---|---|
| Data de Publicación: | 2021 |
| Outros autores: | , , , , , , |
| Formato: | Article |
| Idioma: | eng |
| Fonte: | Repositório Institucional da UnB |
| Download full: | http://repositorio2.unb.br/jspui/handle/10482/46698 https://orcid.org/0000-0003-1561-7583 https://orcid.org/0000-0002-5619-8525 https://orcid.org/0000-0003-1189-3337 https://orcid.org/0000-0003-2738-465X https://orcid.org/0000-0003-3868-8704 https://orcid.org/0000-0003-4724-4064 https://orcid.org/0000-0002-9555-043X https://orcid.org/0000-0002-0346-1684 |
Summary: | The automatic detection of Center Pivot Irrigation Systems (CPIS) is fundamental for establishing public policies, especially in countries with a growth perspective in this technology, like Brazil. Previous studies to detect CPIS using deep learning used single-date optical images, containing limitations due to seasonal changes and cloud cover. Therefore, this research aimed to detect CPIS using Sentinel-2 multitemporal images (containing six dates) and instance segmentation, considering seasonal variations and different proportions of cloudy images, generalizing the models to detect CPIS in diverse situations. We used a novel augmentation strategy, in which, for each iteration, six images were randomly selected from the time series (from a total of 11 dates) in random order. We evaluated the Mask-RCNN model with the ResNext-101 backbone considering the COCO metrics on six testing sets with different ratios of cloudless (< 20%) and cloudy images (> 75%), from six cloudless images and zero cloudy images (6:0) up to one cloudless image and five cloudy images (1:5). We found that using six cloudless images provided the best metrics [80% average precision (AP), 93% AP with a 0.5 intersection over union threshold (AP50)]. However, results were similar (74% AP, 88% AP50) even in extreme scenarios with abundant cloud presence (1:5 ratio). Our method provides a more adaptive and automatic way to map CPIS from time series, significantly reducing interference such as cloud cover, atmospheric effects, shadow, missing data, and lack of contrast with the surrounding vegetation. |
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http://repositorio2.unb.br/jspui/handle/10482/46698https://orcid.org/0000-0003-1561-7583
https://orcid.org/0000-0002-5619-8525
https://orcid.org/0000-0003-1189-3337
https://orcid.org/0000-0003-2738-465X
https://orcid.org/0000-0003-3868-8704
https://orcid.org/0000-0003-4724-4064
https://orcid.org/0000-0002-9555-043X
https://orcid.org/0000-0002-0346-1684