ISPRS-Annals ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences ISPRS-Annals ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci. 2194-9050 Copernicus Publications Göttingen, Germany 10.5194/isprsannals-II-3-W4-223-2015 IMPROVING THE ACCURACY OF ESTIMATED 3D POSITIONS USING MULTI-TEMPORAL ALOS/PRISM TRIPLET IMAGES Susaki J. 1 Kishimoto H. 1 Department of Civil and Earth Resources Engineering, Graduate School of Engineering, Kyoto University, Japan 11 03 2015 II-3/W4 223 230 Copyright: © 2015 J. Susaki 2015 This work is licensed under the Creative Commons Attribution 3.0 Unported License. To view a copy of this licence, visit https://creativecommons.org/licenses/by/3.0/ This article is available from https://isprs-annals.copernicus.org/articles/II-3-W4/223/2015/isprs-annals-II-3-W4-223-2015.html The full text article is available as a PDF file from https://isprs-annals.copernicus.org/articles/II-3-W4/223/2015/isprs-annals-II-3-W4-223-2015.pdf

In this paper, we present a method to improve the accuracy of a digital surface model (DSM) by utilizing multi-temporal triplet images. The Advanced Land Observing Satellite (ALOS) / Panchromatic Remote-sensing Instrument for Stereo Mapping (PRISM) measures triplet images in the forward, nadir, and backward view directions, and a DSM is generated from the obtained set of triplet images. To generate a certain period of DSM, multiple DSMs generated from individual triplet images are compared, and outliers are removed. Our proposed method uses a traditional surveying approach to increase observations and solves multiple observation equations from all triplet images via the bias-corrected rational polynomial coefficient (RPC) model. Experimental results from using five sets of PRISM triplet images taken of the area around Saitama, north of Tokyo, Japan, showed that the average planimetric and height errors in the coordinates estimated from multi-temporal triplet images were 3.26 m and 2.71 m, respectively, and that they were smaller than those generated by using each set of triplet images individually. As a result, we conclude that the proposed method is effective for stably generating accurate DSMs from multi-temporal triplet images.