Development of deep segmentation methods applied to satellite imagery

开发应用于卫星图像的深度分割方法

• 批准号: 521746-2017
• 负责人: Jodoin, PierreMarc
• 金额: $ 1.82万
• 依托单位: Université de Sherbrooke
• 依托单位国家: 加拿大
• 项目类别: Engage Grants Program
• 财政年份: 2017
• 资助国家: 加拿大
• 起止时间: 2017-01-01 至 2018-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=639610
• 关键词: Development; deep; segmentation; methods; applied

In collaboration with UrtheCast inc., the goal of this project is to explore the concrete usability of deepconvolutional neural networks in the field of satellite imagery. Since this project spans over a short period oftime (only 6 months) we will focus on image segmentation, a topic at the core of the research activities of pr.Jodoin and a glaring issue for UrtheCast. As such, we look forward to answer the following four questions:1) Which state-of-the-art network configuration works best and what is its accuracy compared to thenone-deep-learning solutions currently deployed by UrtheCast?2) How many manually annotated satellite images are required to properly train a convolutional neuralnetwork? Also, does weakly annotated data can be used to increase the accuracy of a network?3) How can very large satellite images (from 2,000 x 2,000 to 6,000 x 6,000 pixels) be processed (both attraining and at test time) on a single 12 Gb GPU?4) Can a model trained on a RGB dataset be transferred to a multispectral dataset as well as data from theUrtheCast satellites?Since deep segmentation models are new to the world of remote sensing, this project is fundamentallyimportant both for UrtheCast and the Canadian society as a whole. With its "UrtheDaily(TM)" project,UrtheCast is about to deploy a constellation of satellites which will acquire an unprecedented number ofimages that no one can manually analyse. This project will thus provide the company with the tools forprocessing those images. The same models could also be used to process data acquire by other Canadiansatellites such as RadarSat2 and SCISAT.

与UrtheCast Inc.合作，该项目的目标是探索深度卷积神经网络在卫星图像领域的具体可用性。由于这个项目的时间跨度很短（只有6个月），我们将专注于图像分割，这是Jodoin教授研究活动的核心主题，也是UrtheCast的一个突出问题。因此，我们期待回答以下四个问题：1）与UrtheCast目前部署的单一深度学习解决方案相比，哪种最先进的网络配置效果最好，其准确性如何？2)正确训练卷积神经网络需要多少手动注释的卫星图像？此外，弱注释数据是否可以用来提高网络的准确性？3)如何在单个12 Gb GPU上处理超大卫星图像（从2，000 x 2，000到6，000 x 6，000像素）（在训练和测试时）？4)在RGB数据集上训练的模型是否可以传输到多光谱数据集以及来自UrtheCast卫星的数据？由于深度分割模型对遥感世界来说是新的，因此该项目对UrtheCast和整个加拿大社会都非常重要。通过“UrtheDaily（TM）”项目，UrtheCast即将部署一个卫星星座，它将获得前所未有的图像数量，没有人可以手动分析。因此，该项目将为该公司提供处理这些图像的工具。同样的模型也可用于处理雷达卫星2号和科学卫星等其他南极卫星获得的数据。