This paper describes an algorithm framework for fusion airborne based laser scanning data (LIDAR) and optical images.
A efficient and reliable intensity-based registration framework has been used to determining the spatial transform form
LIDAR to optical images. On the basis of segmented airborne images, the paper raises the arithmetic and process of
merging multi-data sources to carry through classification by using multi-echo, point's space discrete characteristics, and
the statistic spectrum characteristics. In addition to the theoretical method, the paper presents a experimental analysis the
sensitivity and robustness of this approach to assess effectiveness the proposed arithmetic.
This paper describes an algorithm framework for automatic registration of airborne based laser scanning data (LIDAR)
and optical images by using mutual information. The part on methodology describes aspects such as pre-processing of
images, intensity value interpolation, optimization strategy, adaptations to the mutual information measure, and a
progressive registration procedure. In addition to the theoretical method, the paper presents a experimental analysis
based on the quality of fit of final alignment between the LIDAR and digital imagery.
Based on analysis of many image change detection methods, a new method of multidimensional change template analysis is proposed in this paper. The new method combines the use of GIS knowledge and the advantages of other change detection methods. It has been used to detect change and update image database for the different temporal digital orthophoto maps (DOM). Test results show that this method is effective.