GIS data can be updated using the larger scale GIS data based on map generalization. Matching is a critical first step for extracting updates in this process. This paper presents an approach for road data matching based on level analysis. Matches of objects between road networks are divided into three levels, i.e. decomposed level, basic level and abstracted level. The matching of edge end point as the decomposed level, the node matching as the basic level, and the route matching and between node and edge matching as the abstracted level are discovered. Based on the order relationship of three matching levels and the interdependent among them, matches of objects are accomplished by a set of algorithms developed. Meanwhile during this process, the exceptional matching can be as the index for manual checking to improve the matching accuracy. Matches of objects meet the requirement for searching data unmatched. The experiment results of matching road data at 1:10,000 and 1:50,000 show higher matching accuracy.
QTM (Quaternary Triangular Mesh) on spherical surface is an efficient structure for managing the large volume of global spatial data. But the existing methods for the transformation between QTM Codes and Longitude/Latitude Coordinates are either not very efficient or lacking of orientation information in encoding scheme which is very useful in the adjacent query. To overcome these serious deficiencies, a new method is developed for transformation between long\latitude and QTM address codes. This method is simply called Row & Column Approach, or simply RCA. This method is a simple method without any map projection and it recursively approaches the address codes according to orientation information. Experimental evaluation shows that this method is much more efficient than the ETP (Equilateral Triangular Projection), i.e. only about 18% (conversion from longitude/latitude to address code) and 33% (conversion from address code to longitude/latitude) of time required by ETP.
The Pearl River estuary and Hong Kong's coastal waters were selected to study the ocean color categories related to water quality. Three ocean color sensitive parameters: turbidity, suspended sediments (SS) and chlorophyll-a concentration (Chl-a), in 58 monitoring stations were selected to evaluate the water quality. A dataset with 88 samples was picked up from the monitoring stations and the successfully retrieved points of SS and Chl-a from SeaWiFS, 66 of the 88 samples were used at training data and the other 22 as testing data. The normalized difference water index was extracted from the Landsat TM image on Dec. 22, 1998 and the threshold segmentation was used to retrieve the waters from the image for further analysis. The methods of maximum likelihood, neural network and support vector machine were employed for ocean color classification of the selected Landsat TM image. Five classes of water quality could be well interpreted for all the methods. The results showed spatial variation from the west turbid waters to the east relative clear waters and suggested that the turbid wsters could be well classified using Landsat TM data.
Conference Committee Involvement (1)
International Conference on Earth Observation Data Processing and Analysis
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.