日本地球惑星科学連合2022年大会

講演情報

[E] 口頭発表

セッション記号 M (領域外・複数領域) » M-SD 宇宙開発・地球観測

[M-SD40] Micro-satellite and its constellation for next-generation remote sensing

2022年5月24日(火) 10:45 〜 12:15 302 (幕張メッセ国際会議場)

コンビーナ:高橋 幸弘(北海道大学・大学院理学院・宇宙理学専攻)、コンビーナ:Le Huy Xuan(Vietnam National Space Center)、コンビーナ:Dashdondog Erdenebaatar(Associated Professor of the National University of Mongolia)、Chairperson:Huy Xuan Le(Vietnam National Space Center)、Erdenebaatar Dashdondog(Associated Professor of the National University of Mongolia)

11:45 〜 12:00

[MSD40-05] Automated Georeferencing Algorithm for Microsatellite's Multispectral Images

*San Lin Phyo1、Hline Htet Win - -1Ye Min Htay - -1Junichi Kurihara - -1 (1.Hokkaido University)

キーワード:Image Matching, Geometric affine transformation, Georeferencing

Georeferencing means that the internal coordinate system of a map or aerial photo image can be related to a geographic coordinate system. It is crucial to make satellite and aerial as well as terrestrial imagery useful for mapping. Wrong georeferencing leads to inaccurate map projections and measurements that require post-processing correction. The objective of georeferencing is to establish a relationship between image pixel coordinates and a corresponding geographic coordinate system. Microsatellites utilize their on-board sensors for georeferencing but the error in attitude measurements and satellite position remains because of their limited sensor’s performance. Therefore, this research intends to develop an autonomous optimum algorithm which can provide high accuracy for multispectral image georeferencing and automated processing by applying image matching techniques and Google Earth Engine. The objectives of this research are to identify important parameters (brightness, viewing angle, local solar time, etc.) affecting the georeferencing accuracy and to clarify geographical and spectral dependence of the georeferencing accuracy. At first, the data set (such as geolocation, time range, types of bands) of the satellite query image which is required to do georeferencing are obtained. Based on these data set, the suitable base images are searched and obtained using Google Earth Engine. After that, the features matching process between the two images are developed. And then, the false matching points called outliers are rejected and correct matching points known as inliers are collected. Based on these inlier points, the homography matrix which requires for affine transformation is manipulated. The query image is transformed to the base image’s coordinate frame and the pixel coordinates(x,y) of query image are converted to the geographic coordinates(latitude, Longitude) based on the geolocation of base image. Finally, the georeferenced query image is obtained. In this seminar, regarding image matching techniques, the geometric affine transformation and geographic coordinates transformation will be presented and the georeferencing results will be discussed.