Abstract
Synthetic Aperture Radar (SAR) imagery captures the physical properties of the Earth by transmitting microwave signals to its surface and analyzing the backscattered signal. It does not depends on sunlight and therefore can be obtained in any condition, such as nighttime and cloudy weather. However, SAR images are noisier than light images and so far it is not clear the level of performance that a modern recognition system could achieve. This work presents an analysis of the performance of deep learning models for the task of land segmentation using SAR images. We present segmentation results on the task of classifying four different land categories (urban, water, vegetation and farm) on six Canadian sites (Montreal, Ottawa, Quebec, Saskatoon, Toronto and Vancouver), with three state-of-the-art deep learning segmentation models. Results show that when enough data and variety on the land appearance are available, deep learning models can achieve an excellent performance despite the high input noise.
RADARSAT-2 Data and Products © MDA Geospatial Services Inc. (2010 to 2015) – All Rights Reserved. RADARSAT is an official mark of the Canadian Space Agency.
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Saadati, M., Pedersoli, M., Cardinal, P., Oliver, P. (2021). RADARSAT-2 Synthetic-Aperture Radar Land Cover Segmentation Using Deep Convolutional Neural Networks. In: Del Bimbo, A., et al. Pattern Recognition. ICPR International Workshops and Challenges. ICPR 2021. Lecture Notes in Computer Science(), vol 12668. Springer, Cham. https://doi.org/10.1007/978-3-030-68793-9_8
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