电子信息工程学院

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第八届多时相遥感影像分析会议

会议名称(中文): 第八届多时相遥感影像分析会议
会议名称(英文):
所属学科: 测绘与遥感,计算机应用技术,信号与信息处理
开始日期: 2015-07-22
结束日期: 2015-07-24
所在国家: 法国
所在城市:     法国
具体地点: Annecy, France
主办单位: University of Savoie, France
会议网站: http://www.multitemp2015.org/
会议背景介绍:
After 40 years of Earth Observation missions with both passive (multispectral, hyperspectral, etc.) and active (synthetic aperture radar, lidar, etc.) sensors, remote sensing data offer a unique opportunity to record, to analyze and to predict the evolution of our living planet. In the last decade, a large number of new satellite remote sensing missions have been launched, resulting in dramatic improvement in the image acquisition capabilities. The successful launching of the Sentinel-1 in 2014 and the launching of the coming satellites of the Copernicus program, with regular acquisition plans and free data access policy, result in new challenge for handling and processing such huge volume of data. This increasing number of Earth Observation systems involves an enhanced possibility to acquire multitemporal images of the Earth surface, with improved temporal and spatial resolution. Such new scenario significantly increases the interest of the time series processing in the remote sensing community. The development of novel data processing techniques to address new important and challenging applications seems promising.

Nonetheless, the properties of the images acquired by the last generation sensors (e.g. very high spatial resolution, long time series, etc.) raise new methodological problems that require the development of new methods for the analysis of multitemporal data. The potential of the technological development is strengthened with the increasing awareness of the importance of monitoring the Earth surface at local, regional and global scale. Assessing, monitoring and predicting the dynamics of natural land covers and of antrophic processes is on the basis of both the understanding of the problems related to climate changes and the definition of politics for sustainable development.

In the context of "Big Data” encountered in the remote sensing community, the objective of MultiTemp 2015 provides a scientific forum of discussions for methodology and application issues related to multitemporal data analysis. The workshop aims to propose novel solutions for technical problems related to the analysis of multitemporal data, to promote the use of the multitemporal images in an ever increasing number of strategic and challenging applications and to strengthen the connections between the scientists and the end-users. In this perspective, contributions are welcome from the methodological community dealing with novel technologies and methods for data analysis, as well as from the application sectors focusing on the use of multitemporal data in practical settings.
征文范围及要求:
Contributions to all the issues related to multitemporal data processing, to the analysis of time series acquired by passive and active sensors and to the related applications are welcome, including:

Multitemporal image analysis techniques
Image registration, calibration and correction techniques
Classification of multitemporal data
Fusion and assimilation of multitemporal data
Data mining and analysis of remote sensing time series
Change detection methods
Change detection accuracy assessment
Multitemporal SAR and InSAR data analysis
Multitemporal LiDAR data analysis
Timelaps and multitemporal photogrammetric data analysis
Land-cover and land-use dynamics
Phenology product development and monitoring applications
Applications of multitemporal data and time series
Sea‐ice dynamics and cryospheric monitoring and modeling
Ocean dynamics, modelling and prediction
Water and ecosystem resources monitoring and modeling
Environmental reclamation monitoring and modeling
Drought monitoring and predictive modeling
Vegetation dynamics and productivity
Forestry and agriculture monitoring
Stress and damage assessment
New satellite missions for high temporal resolution time series
New satellite missions for very high spatial resolution time series