An Image Similarity Evaluation in Rainfall Forecasting Model
DOI:
https://doi.org/10.24203/ijcit.v9i5.47Keywords:
Image Similarity; Image Matching; Rainfall ForecasingAbstract
The Global Satellite Mapping of Precipitation or GSMaP data which is used to display the rainfall data was used to analyze and create the rainfall forecasting model. This work is the evaluation of this rainfall forecasting model which is the short-term forecast. The GSMaP forecasting data were matched with the GSMaP history data and calculate their similarity values by applying the original image matching method. The modification of Rainfall Forecasting Model and its evaluation that applied the original image instead of the image hash improve the accuracy of rainfall forecasted results.
References
Encyclopedia of Mathematics, “Hamming distance,” URL: http://encyclopediaofmath.org/index.php?title=Hamming_distance&oldid=39148.
ESRI Inc., “ESRI ASCII raster format,”
G. Jianhua, Y. Fan, T. Hai, W. Jing-xue, and L. Zhiheng, “Image matching using structural similarity and geometric constraint approaches on remote sensing images,” Journal of Applied Remote Sensing, 2016: 10. 045007. 10.1117/1.JRS.10.045007.
K. Aonashi, J. Awaka, M. Hirose, T. Kozu, T. Kubota, G. Liu, S. Shige, S. Kida, S. Seto, N. Takahashi, and Y. N. Takayabu, “GSMaP passive, microwave precipitation retrieval algorithm: Algorithm description and validation,” J. Meteor. Soc. Japan, 2009, 87A, 119-136.
K. Okamoto, T. Iguchi, N. Takahashi, K. Iwanami and T. Ushi, “The Global Satellite Mapping of Precipitation (GSMaP) project,” 25th IGARSS Proceedings, 2005, pp. 3414-3416.
N. Raut, “What is Hamming Distance?”, 2018, TutorialPoint.
P. Deeprasertkul, “An Assessment of Rainfall Forecast using Image Similarity Processing,” ICIGP Proceedings of the 2020 3rd International Conference on Image and Graphics Processing, 2020, pp. 141–145. https://doi.org/10.1145/3383812.3383821.
T. Kubota, S. Shige, H. Hashizume, K. Aonashi, N. Takahashi, S. Seto, M. Hirose, Y. N. Takayabu, K. Nakagawa, K. Iwanami, T. Ushio, M. Kachi, and K. Okamoto, “Global Precipitation Map using Satelliteborne Microwave Radiometers by the GSMaP Project : Production and Validation,” IEEE Trans. Geosci. Remote Sens., Vol. 45, No. 7, 2007, pp.2259-2275.
T. Ushio, T. Kubota, S. Shige, K. Okamoto, K. Aonashi, T. Inoue, N. Takahashi, T. Iguchi, M. Kachi, R. Oki, T. Morimoto, and Z. Kawasaki, “A Kalman filter approach to the Global Satellite Mapping of Precipitation (GSMaP) from combined passive microwave and infrared radiometric data,” J. Meteor. Soc. Japan, 2009, 87A, 137-151.
Downloads
Published
Issue
Section
License
Copyright (c) 2020 Prattana Deeprasertkul
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
The articles published in International Journal of Computer and Information Technology (IJCIT) is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
