Automated Extraction of Control Points for High Spatial Resolution Satellite Images

碩士 === 國立中央大學 === 土木工程研究所 === 89 === The high spatial resolution satellite images have widely attracted the attention in the remote sensing community recently. It is obvious that one of the great challenges to process the high spatial resolution satellite images will be the geometric correction pra...

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Bibliographic Details
Main Authors: Cheng-Yi LIN, 林乘逸
Other Authors: Chi-Farn CHEN
Format: Others
Language:zh-TW
Published: 2001
Online Access:http://ndltd.ncl.edu.tw/handle/06654570292997150894
Description
Summary:碩士 === 國立中央大學 === 土木工程研究所 === 89 === The high spatial resolution satellite images have widely attracted the attention in the remote sensing community recently. It is obvious that one of the great challenges to process the high spatial resolution satellite images will be the geometric correction practice. Conventionally, the positioning of the image control points is manually performed by a labor-intensive and time-consuming procedure. The practicable experience indicates that the qualified image control points would be the points with striking features such as the road intersection. Thus, due to the abundant image contents, high spatial resolution satellite image would have plenty of the qualified control points. As a result, the manual identification and positioning of control points will become even more inefficient and unbearable. Therefore, the main objective of this study aims to develop an automated image processing technique to extract the control points for the high spatial resolution satellite images. Among numerous spatial features, this study considers road intersection the main target to perform the control point extraction. The proposed image-processing algorithm consists of three steps. The first step is designed to segment the image and produce the feature image. The second step is proposed to extract roadblock features from the feature image. The third step is planned to locate the center position of the roadblock. A series of high spatial resolution satellite images are used to test the proposed method. The preliminary results shows that the proposed image processing approach has the potential to automatically position the control points in the high spatial resolution satellite image.