Automatic Database Development Methods for a very Large Number of Satellite Images
Koki Iwao, ** Taizo Yamamoto, * Ryosuke Shibasaki, *** Shiro Ochi,
and *** Mitsuharu Tokunaga
*center for Spatial Information Science at the University of Tokyo
** Nippon Telegraph and Telephone Corporation
*** Institute of Industrial Science, University of Tokyo
7-22-1, Roppongi, Minato-Ku Tokyo 106-8558, Japan
Tel: (81) -3-2402-6231 (ext. 2563) Fax : (81)-3-3479-2762
E-mail : iwao@skl.iis.u-tokyo.ac.jp
Abstract
Recently, we can use many kinds of satellite images for many different objectives. And it is said that we will be able to obtain 1TB of images per day in the near future, in that sense, development of a database system for managing satellite data will be very useful. To satisfy such needs, in this research, we focused on two methods to improve the efficiency in developing database system for managing a very large number of geo-coded satellite images and raster data. Methods are (1) and automated geometric correction method for satellite images; and (2) to develop a resampling methods with high accuracy and high speed.
1.Introduction
In recent times a large volume of natural and synthetic image and grid data are being generated and becoming available. For, example, data from current images satellites like Landsat, RADARSAT, SPOT, ERS, NOAA etc. and also, there are current plans to launch more than one hundred Earth Observing Satellites by the year 2005, with 60 of those scheduled for launch by the end of 1999- with some of these satellite generating as many as 22,000 scenes per day. Digital orthophoto mapping mapping is another field in which major financial investment is being made and in which a large volume of raster data is being produced investment is being made and in which a large volume of raster data is being produced. Obviously, there will be a great chance if we can integrate these different types of data into a database. With the development of such a kind of database system, we will be able to effectively use these data for various purposes like environmental monitoring of the earth. In this research. We have focused on two methods to construct database system for managing. A very large number of geo-coded satellite images and raster data. They are-
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An automated geometric correction method for satellite images ( because we have to handle so many images, automatic system is indispensable ) and adjust geometric errors over all images based on the idea of block adjustment
- Resampling methods with high accuracy and high speed ( because the processing time and the accuracy depends on resampling methodology).
2.Methodology
2.1 Automatic Geometric Correction Methods
For high accuracy geometric corrction , GCP ( ground control point) is indispensable . But it is not always easy to fine GCP points clearly in one scene for image coordinate registration. And geometric correction using GCP is very labor demanding. Further, traditional geometric correction methods can not be used if was fail to find good GCPs in a single scene. Therefore it is necessary to correct geometrically al these satellite images automatically for all images at same time. We propose a method to identify GCPs and tie-points in an automated manner, and to adjust geometric errors over all images based on the idea of block adjustment used in photogrammer. With this method , we can improve the efficiency and reduced the total amount of GCPs required for geometric correction. Process flow of this system is as follows
Fig .1 Flow of the geometric Correction system
2.1.2 Evaluations of the Automatic Geometric Correction Accuracy
2.1.2.1Preprocessing:
Here, we have used Landsat TM (1989, jan -may ) images as a test image . NDVI image can easily identify water body from vegetation.
2.1.2.2 Automated Acquisition of Tie Points
in the block adjustment method used n photogrammetry, tie point information need to be acquired. Tie point is the common point connecting two neighboring images. Images correlation is applied to identify the tie points . in order to check the quality and efficiency , we test the relating between template size, correlation values, calculating time and positioning accuracy. As a result, in automated acquisition of the tie points, we found that 9 by 9 pixels is the best tem plated size and that threshold value of correlation should be 0.85
Fig .2 Tie point acquisition method
Quality check (200 samples, correlation >= 0.85)
| RMSE | 1.02 PIXEL |
| Max Error | 3 pixel |
Table 1. Quality of tie point acquisition
