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ミクセルを考慮したNOAA-AVHRRデータのクラス分類アルゴリズム Use of Mixels in Classification Algorithm for NOAA-AVHRR Data 景山, 陽一 佐藤, 郁磨 西田, 眞 Kageyama, Yoichi Sato, Ikuma Nishida, Makoto open access コンテンツの利用については、著作権法に規定されている私的使用や引用などの範囲内で行ってください。 リモートセンシング マルチスペクトルデータ ミクセル クラス ファジイ理論 National Oceanic and Atmospheric Administration (NOAA) and Advanced Very High Resolution Radiometer (AVHRR) data are available on a daily basis and have been frequently used for global observation. The ground image can be resolved 1.1km immediately below the satellite on a horizontal scale. Both pure and mixed pixels (mixels) can be used to accurately classify land-, sea-, and cloud- cover conditions. We propose the use of a classification algorithm for the NOAA-AVHRR data. The algorithm has four steps. First, multispectral bands are used to estimate elements of three classes (sea, land, and cloud) as supervised data for pre-classification. Second, pure pixels of the three classes are extracted on the basis of the multispectral bands and the Normalized Difference Vegetation Index (NDVI) of the same pixel. Third, we determine pure pixels and mixels by using fuzzy reasoning for the remaining pixels mentioned above, with the exception of the "land and sea" class. Finally, the edge information facilitates the retrieval of the "land and sea" mixel. Our experimental results suggest that the proposed approach provides results suitable for classifying various conditions. 一般社団法人映像情報メディア学会 2009-03-01 jpn journal article VoR http://hdl.handle.net/10295/2004 https://air.repo.nii.ac.jp/records/1893 13426907 AN10588970 映像情報メディア学会誌 : 映像情報メディア 63 3 339 348 https://air.repo.nii.ac.jp/record/1893/files/kageyama1.pdf application/pdf 3.1 MB 2017-02-16