Dealing With Clouds and Seasonal Changes for Center Pivot Irrigation Systems Detection Using Instance Segmentation in Sentinel-2 Time Series

• Main Authors: Anesmar Olino de Albuquerque, Osmar Luiz Ferreira de Carvalho, Cristiano Rosa e Silva, Argelica Saiaka Luiz, Pablo P. de Bem, Roberto Arnaldo Trancoso Gomes, Renato Fontes Guimaraes, Osmar Abilio de Carvalho Junior
• Format: Article
• Language: English
• Published: IEEE 2021-01-01
• Series: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
• Subjects: Cloud; deep learning; mask R-CNN; time series
• Online Access: https://ieeexplore.ieee.org/document/9513599/

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 (<inline-formula><tex-math notation="LaTeX">$&lt; 20\%$</tex-math></inline-formula>) and cloudy images (<inline-formula><tex-math notation="LaTeX">$&gt;75\%$</tex-math></inline-formula>), 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&#x0025; average precision (AP), 93&#x0025; AP with a 0.5 intersection over union threshold (AP50)]. However, results were similar (74&#x0025; AP, 88&#x0025; 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.