WINDMILLS DETECTION USING DEEP LEARNING ON SENTINEL SATELLITE IMAGES

Abstract. Automatic detection of objects from Earth Observation images is still a challenge for researchers. This paper aims at extracting automatically windmills on mid-resolution images (10-meter resolution), based on Sentinel satellite products. Sentinel-2 optical images are obvious candidates for our study. At 10-meter resolution, a windmill is represented with only a few pixels. We also start to consider Synthetic Aperture Radar (SAR) Sentinel-1 images but no particular windmill radar response on GRD (Ground Range Multi-look Detected) products seemed to be relevant.

Considering the maturity of deep learning techniques for object detection in computer vision, we explore the use of deep neural networks for windmill detection on remote sensing images. For that purpose, we had to create the training data sets but we took advantage of the availability of many Sentinel images and of the use of automated labelling as the objects are georeferenced. The proposed approach relies on the U-Net framework, reformulating our problem of object detection in terms of semantic segmentation. We trained several neural networks on different data sets emanating from different countries. That enabled us to measure the performance of detection within a country but also across two countries (training on a country and predicting on another country). The results show the ability of detection of such small objects with respect to the resolution and we obtain various levels of performances depending on the trained and test data sets.