Generation of High Resolution Global DSM from ALOS PRISM

J. Takaku,T. Tadono,K. Tsutsui

Published 2014 in ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences

ABSTRACT

Panchromatic Remote-sensing Instrument for Stereo Mapping (PRISM), one of onboard sensors carried on the Advanced Land Observing Satellite (ALOS), was designed to generate worldwide topographic data with its optical stereoscopic observation. The sensor consists of three independent panchromatic radiometers for viewing forward, nadir, and backward in 2.5 m ground resolution producing a triplet stereoscopic image along its track. The sensor had observed huge amount of stereo images all over the world during the mission life of the satellite from 2006 through 2011. We have semi-automatically processed Digital Surface Model (DSM) data with the image archives in some limited areas. The height accuracy of the dataset was estimated at less than 5 m (rms) from the evaluation with ground control points (GCPs) or reference DSMs derived from the Light Detection and Ranging (LiDAR). Then, we decided to process the global DSM datasets from all available archives of PRISM stereo images by the end of March 2016. This paper briefly reports on the latest processing algorithms for the global DSM datasets as well as their preliminary results on some test sites. The accuracies and error characteristics of datasets are analyzed and discussed on various fields by the comparison with existing global datasets such as Ice, Cloud, and land Elevation Satellite (ICESat) data and Shuttle Radar Topography Mission (SRTM) data, as well as the GCPs and the reference airborne LiDAR/DSM.

PUBLICATION RECORD

  • Publication year

    2014

  • Venue

    ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences

  • Publication date

    2014-04-23

  • Fields of study

    Geography, Engineering, Environmental Science

  • Identifiers
  • External record

    Open on Semantic Scholar

  • Source metadata

    Semantic Scholar

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