Exploring unsupervised domain adaptation methods for automated linear disturbance mapping

探索自动线性扰动映射的无监督域适应方法

• 批准号: 577643-2022
• 负责人: Henry, ChristopherCJ
• 金额: $ 4.37万
• 依托单位: University of Winnipeg
• 依托单位国家: 加拿大
• 项目类别: Alliance Grants
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=743621
• 关键词: Exploring; unsupervised; domain; adaptation; methods

In Canada's vast northern region of boreal forest and wetlands, disturbances such as roads, seismic exploration, pipelines, and energy transmission corridors are a leading cause of the decline of woodland caribou (Rangifer tarandus) - boreal population. As a result, a deep understanding of these "linear disturbances" has become a research and forest management priority in Canada. An ideal tool to support managing linear disturbances is a way to automatically generate cost-effective maps that accurately identify this form of forest habitat fragmentation. As a result, the focus of this project is to develop an approach for automated map production from multispectral satellite images using deep learning methods. Specifically, convolutional neural networks designed for classifying each pixel in a satellite image will be used to produce linear disturbance maps from Sentinel-2 data. The main research challenge to be addressed is that labels were developed using expensive high-resolution SPOT-6 satellite data, and there are no equivalent labels for the free and medium-resolution Sentinel-2 data. Thus, the proposed work will develop an unsupervised domain adaptation approach, which aims to take the data and corresponding labels from one domain and adapt it for training semantic segmentation models in a related, but different domain. The main benefit of this work is advancing methods for monitoring the fragmented habitats affecting the boreal Caribou herds at scale, thereby supporting important conservation outcomes. Additional benefits include advancing general unsupervised domain adaptation from finer spatial resolution domains to coarser ones, providing improved opportunities to use freely available Sentinel-2 data for other related environmental monitoring tasks, and developing baseline methodology to build upon for spectral or time-series analysis.

在加拿大广阔的北方地区的北方森林和湿地，干扰，如道路，地震勘探，管道和能源传输走廊是林地驯鹿（Rangifer tarandus）-北方种群下降的主要原因。因此，深入了解这些“线性干扰”已成为加拿大研究和森林管理的优先事项。支持管理线性干扰的一个理想工具是自动生成具有成本效益的地图，准确识别这种形式的森林生境破碎化。因此，该项目的重点是开发一种使用深度学习方法从多光谱卫星图像自动生成地图的方法。具体而言，为对卫星图像中的每个像素进行分类而设计的卷积神经网络将用于从Sentinel-2数据中生成线性扰动图。需要解决的主要研究挑战是，标签是使用昂贵的高分辨率SPOT-6卫星数据开发的，而免费的中等分辨率Sentinel-2数据没有等同的标签。因此，拟议的工作将开发一种无监督的域自适应方法，其目的是从一个域中获取数据和相应的标签，并将其用于在相关但不同的域中训练语义分割模型。这项工作的主要好处是推进了对影响北方驯鹿群的零散生境进行大规模监测的方法，从而支持重要的保护成果。其他好处包括将一般无监督的域适应从较精细的空间分辨率域推进到较粗糙的空间分辨率域，提供更好的机会将免费获得的哨兵-2数据用于其他相关的环境监测任务，并开发基线方法，以用于光谱或时间序列分析。