Avalanche Detection in Sar Images Using Deep Learning

Anders U. Waldeland, Jarle Hamar Reksten, Arnt-Børre Salberg

Abstract: Detection of avalanches is critical for keeping avalanche inventories and management of emergency situations. In this paper we propose a deep-learning based avalanche detection method for SAR images. We utilize an existing method for proposing candidate regions, based on change detection in SAR images from multiple passes over the same area. Then a convolutional neural network is used to classify whether the candidate regions contain an avalanche or not. The proposed methodology applies existing pre-trained network that has been trained for classification of natural RGB images. SAR images represent non-standard images and we propose a method for adapting SAR images to be used in pre-trained networks for RGB images. The pre-trained network is then fine-tuned to the task of discriminating avalanches from lookalikes in the candidate regions from the SAR images. Using cross-validation, we find that the proposed method has an average classification error rate of 3.5%.

External IDs:dblp:conf/igarss/WaldelandRS18