Object-based 3D information incorporated classification and change detection

基于对象的 3D 信息结合分类和变化检测

• 批准号: 239066-2011
• 负责人: Zhang, Yun
• 金额: $ 3.93万
• 依托单位: University of New Brunswick
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2015
• 资助国家: 加拿大
• 起止时间: 2015-01-01 至 2016-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=570560
• 关键词: Object; based; 3D; information; incorporated

As much as 80 percent of the information used for decision making has some geospatial context. Numerous modern high resolution satellites have been launched since 1999 to improve the capacity of acquiring and updating detailed geospatial information globally. However, due to very limited availability of effective technologies to extract information from the images, many images have become outdated before being effectively used or even being used. Land cover classification and change detection are two most widely used techniques in information extraction from the images. Object-based classification is a newly emerging technology. It has demonstrated tremendous potential, but also encountered many technically challenging problems. Based on the findings and results of my current NSERC projects, this proposed research intends to develop new solutions to mitigate problems existing in the three essential areas of object-based classification and change detection, i.e. object segmentation, object classification, and image registration. New techniques for supervised segmentation, object-based and 3D information incorporated supervised classification, and 3D information incorporated change detection will be developed. 3D information from stereo images will be utilized, as all the modern high resolution satellites and airborne sensors have stereo capacity. Object-based and 3D information incorporated image registration will be developed to overcome existing image registration problems. The successful outcome of this research will contribute to the gap reduction between vastly collected remote sensing images and very limited capacity of information extraction from the images. It will help users to effectively utilize remote sensing images for a broad range of applications, such as environment monitoring, natural resource investigation, planning, and military missions. The education of high quality researchers or engineers in the proposed research area is strategically important for keeping Canada's leading position in geoinformatics.

用于决策的信息中有多达80%具有一定的地理空间背景。自1999年以来，已经发射了许多现代高分辨率卫星，以提高全球获取和更新详细地理空间信息的能力。然而，由于从图像中提取信息的有效技术非常有限，许多图像在有效使用甚至使用之前就已经过时了。