Single Image Cloud Detection via Multi-Image Fusion

Autor:	Workman, Scott, Rafique, M. Usman, Blanton, Hunter, Greenwell, Connor, Jacobs, Nathan
Rok vydání:	2020
Předmět:	Computer Science - Computer Vision and Pattern Recognition
Druh dokumentu:	Working Paper
Popis:	Artifacts in imagery captured by remote sensing, such as clouds, snow, and shadows, present challenges for various tasks, including semantic segmentation and object detection. A primary challenge in developing algorithms for identifying such artifacts is the cost of collecting annotated training data. In this work, we explore how recent advances in multi-image fusion can be leveraged to bootstrap single image cloud detection. We demonstrate that a network optimized to estimate image quality also implicitly learns to detect clouds. To support the training and evaluation of our approach, we collect a large dataset of Sentinel-2 images along with a per-pixel semantic labelling for land cover. Through various experiments, we demonstrate that our method reduces the need for annotated training data and improves cloud detection performance. Comment: IEEE International Geoscience and Remote Sensing Symposium (IGARSS) 2020
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2007.15144 Zobrazit plný text záznamu View this record from Arxiv