Development of innovative fusion strategies and methods to improve vegetation characterization from multi-sensor remotely sensed data

开发创新的融合策略和方法，以改善多传感器遥感数据的植被特征

• 批准号: RGPIN-2015-06563
• 负责人: Hu, Baoxin
• 金额: $ 1.6万
• 依托单位: York University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2018
• 资助国家: 加拿大
• 起止时间: 2018-01-01 至 2019-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=653954
• 关键词: Development; innovative; fusion; strategies; methods

The long term goal of this proposed research program is to develop innovative integration strategies and fusion methods for the scientific advancement related to the accurate characterization of vegetation canopies from remotely sensed data. It is motivated and driven by technological developments in remote sensing and the demand for advancing science and innovation in vegetation characterization. Rapidly developed remote sensing technologies are making earth observation data widely available in unprecedented volume and detail; and meanwhile recent years have witnessed an increased demand for accurate determination of a growing number of attributes of vegetation canopies using remote sensing for sustainable management of forest resources, environment protection, and precision agriculture. The critical question we currently face is how to effectively utilize these data and intelligently integrate them together to improve vegetation characterization, such as the determination of vegetation types and conditions.*******Even though data/information fusion is not a new topic in the remote sensing community, the increasing number and heterogeneity of information sources, coupled with the complexity of vegetation canopies leads to an increasing demand for advanced methods  to fully exploit the available technologies, geospatial data, and accumulated knowledge. In this research, I propose innovative integration strategies that are active, intelligent and adaptive, and physics-and knowledge-based, representing a new paradigm in data fusion. These strategies deal with not only methodologies for information combination, but also mechanisms for information selection which actively control and manage the fusion process. With them, the whole scene is not necessarily analyzed in the same way; information and procedures used can be adaptive to local characteristics.*******The long-term research goal will be achieved through fulfilling the following short-term objectives addressing three important aspects in the characterization of vegetation canopies that are unique in term of algorithm development. (1) Developing intelligent integration approaches to improve individual tree crown delineation. (2) Developing innovative methods to adaptively combine all information obtained from different data sources and improve forest species classification. (3) Developing knowledge-based progressive inversion strategies to improve the retrieval of vegetation parameters using the multi-source remotely sensed data.*******The success of this program will have a great impact on research and development of information fusion and vegetation characterization. Research results will generate new knowledge and improve our understanding of different sensing technologies and their integrations in vegetation characterization and advance scientific and industrial applications.*********

这项提出的计划的长期是，从遥感的数据和植被表征中的科学和创新的需求中，开发了对植被檐篷的准确性的融合策略和融合方法前所未有的数量和细节；与此同时，人们对使用遥感的植被罐头的属性进行了越来越多的需求，以实现对森林资源的可持续管理，环境保护和精确农业的可持续性。数据将它们共同改善植被的表征，植被类型和条件。在这项研究中，对可用的技术，地理空间数据和累积知识的需求。对于以相同的方式分析的信息，我们的融合过程不必要。在算法开发的植被檐篷方面的三个重要方面。 * ******* THE SULL HAVE A GREAT IMPACT OND Development of Information Fusion and Vegetation Characterization. Rch Results Will Generate New Knowledge and IMPROVE OF DIFFERENT SENSING TECHNOLOGIES AND THEIR INTEGRATIONS in CAATATITION ERIZATION and ADVANCE SCIENTIEC And Industrial Applications. ** *********