Journal of the Japan society of photogrammetry and remote sensing
Online ISSN : 1883-9061
Print ISSN : 0285-5844
ISSN-L : 0285-5844
Volume 59, Issue 1
Displaying 1-12 of 12 articles from this issue
Preface
Original Papers
  • Takashi MIURA, Masao MORIYAMA
    2020 Volume 59 Issue 1 Pages 29-40
    Published: 2020
    Released on J-STAGE: March 01, 2021
    JOURNAL FREE ACCESS

    We propose a post-launch algorithm for the SGLI active fire detection products. Based on the pre-launch algorithm, some modification is made in order to work on the SGLI fire monitoring system. The main point of the modification is to apply statistical methods to the fire detection scheme, possibly improving the robustness of the algorithm performance. The post-launch algorithm was developed and evaluated using the SGLI data. It was clarified that this algorithm was able to detect obvious fire pixels with relatively higher accuracy, and the false alarms (false positives) were seen in burned-out sites and bright soils.

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  • Toshihisa SUZUKI, Hideaki MAEHARA, Michihiro KUCHI, Kenji TAIRA
    2020 Volume 59 Issue 1 Pages 41-48
    Published: 2020
    Released on J-STAGE: March 01, 2021
    JOURNAL FREE ACCESS

    As our previous work, we have reported a method of measuring the river water level from camera image by detecting water border positions based on the deep learning technology, targeting non-installation sites of water gauges.

    For this time, we have considered an additional method which automatically collects training data from images taken by cameras installed by river sides and lets neural networks learn the classification between water images and non-water images. We also have estimated the effectiveness of our new method using actual river image data set. As the results, the true positive ratio of classification reached from 6 to 37 points improvement.

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