Development of panchromatic image classification techniques for land-use applications

开发用于土地利用应用的全色图像分类技术

• 批准号: 355807-2009
• 负责人: AbdElrahman, AhmedShaker
• 金额: $ 1.31万
• 依托单位: Ryerson University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2010
• 资助国家: 加拿大
• 起止时间: 2010-01-01 至 2011-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=459572
• 关键词: Development; panchromatic; image; classification; techniques

Image classification is necessary in many land-use applications. Since the advent of multi-spectral imagery a significant effort has been made in optimising techniques to allow their spectral characteristics to be fully exploited. However, there is a vast volume of panchromatic imagery (from aerial cameras and space borne sensors) that would need to be used when long-term studies of land-change are to be undertaken. In addition, there are new sensors such as airborne LIDAR and SAR systems that also provide panchromatic imagery and need to be fully exploited. The goal of this project is to develop improved methods and technologies for the use of panchromatic imagery captured from different remote sensing sensors for image classification applications. The proposed project aims to achieve four major objectives. First, the characteristics of panchromatic imagery from different sensors will be evaluated. Then, methods used for classifying multi-spectral images will be examined for panchromatic image classification. Second, new methods for panchromatic image classification will be developed and tested with respect to the characteristics of panchromatic imagery from different sensors. Innovative new techniques, such as hyper classifier algorithms will also be targeted. Third, with regards to the active sensor technology of LIDAR, the project proposes the development of a LIDAR image intensity catalogue by investigating the effects of sensor altitude, incidence angle of the laser beam, and the target reflectivity on the image intensity values so they can be integrated into the image classification process. Fourth, the project will evaluate the applicability of different image-to-image registration models using point/linear features to integrate of different panchromatic images in the classification process. Generally, the results of the project will contribute to both theory and practice of remote sensing image classification and they will attract the society's attention to the use of panchromatic imagery for different applications.

图像分类在许多土地利用应用中是必要的。自多光谱图像出现以来，在优化技术以充分利用其光谱特性方面作出了重大努力。然而，在对土地变化进行长期研究时，需要使用大量的全色图像（来自航空照相机和空间传感器）。此外，还有一些新的传感器，如机载激光雷达和合成孔径雷达系统，也提供全色图像，需要充分利用。该项目的目标是开发更好的方法和技术，将从不同遥感传感器捕获的全色图像用于图像分类应用。拟议项目旨在实现四个主要目标。首先，将评估来自不同传感器的全色图像的特性。然后，用于分类多光谱图像的方法将被检查全色图像分类。其次，将针对不同传感器全色图像的特点，开发和测试全色图像分类的新方法。创新的新技术，如超分类器算法也将成为目标。第三，关于LIDAR的主动传感器技术，该项目建议通过研究传感器高度、激光束入射角和目标反射率对图像强度值的影响来开发LIDAR图像强度目录，以便将其整合到图像分类过程中。第四，该项目将评估不同的图像到图像配准模型的适用性，使用点/线性特征来整合不同的全色图像在分类过程中。总的来说，该项目的成果将有助于遥感图像分类的理论和实践，他们将吸引社会的注意力，全色图像用于不同的应用。