Development of panchromatic image classification techniques for land-use applications

在许多土地利用应用中，图像分类是必不可少的。自从多光谱图像出现以来，人们在优化技术方面做出了重大努力，以使其光谱特性得到充分利用。然而，在进行土地变化的长期研究时，需要使用大量的全色图像(来自航空相机和空间传感器)。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，需要充分开发。该项目的目标是开发改进的方法和技术，以便将从不同遥感传感器捕获的全色图像用于图像分类应用。拟议的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征。然后，将研究用于对多光谱图像进行分类的方法，以用于全色图像分类。其次，将针对来自不同传感器的全色图像的特征，开发和测试新的全色图像分类方法。创新的新技术，如超分类器算法也将成为目标。第三，针对激光雷达的主动传感器技术，通过研究传感器高度、激光入射角和目标反射率对图像强度值的影响，提出了一种激光雷达图像强度目录，以便将它们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/线特征在分类过程中整合不同的全色图像。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，并将引起社会对全色图像在不同应用中的使用的关注。

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.