Post-classification analysis is an important way for remotely sensed imagery change detection. In this paper, we propose a novel classification way for change detection using multispectral IKONOS imagery. The classification way is called after Enhanced Growing Self-Organization Map (EGSOM). The EGSOM is designed to solve two limitation of traditional Self Organization Feature Map (SOM). One is the training time of SOM is endless, the other is SOM's structure is fixed before train. EGSOM make use of Growing Self Organization Feature Map and the network's weights are initialized after hierachical clustering method. The method can save network-training time and make the network express input data correctly. Using EGSOM, we classify Multispectral IKONOS imagery and analyze the change detection result. The experiment shows the EGSOM can achieve better classification results than max likelihood method.
As we all know, it is difficult and time-consuming to acquire and share multi-source geospatial information in grid computing environment, especially for the data of different geo-reference benchmark. Although middleware for data format transformation has been applied by many grid applications and GIS software systems, it remains difficult to on demand realize spatial data assembly jobs among various geo-reference benchmarks because of complex computation of rigorous coordinate transformation model. To address the problem, an efficient hierarchical quadtree structure referred as multi-level grids is designed and coded to express the multi-scale global geo-space. The geospatial objects located in a certain grid of multi-level grids may be expressed as an increment value which is relative to the grid central point and is constant in different geo-reference benchmark. A mediator responsible for geo-reference transformation function with multi-level grids has been developed and aligned with grid service. With help of the mediator, a map or query spatial data sets from individual source of different geo-references can be merged into an uniform composite result. Instead of complex data pre-processing prior to compatible spatial integration, the introduced method is adaptive to be integrated with grid-enable service.
Conference Committee Involvement (1)
International Conference on Earth Observation Data Processing and Analysis