Title :
Automatic classification algorithm for NOAA- AVHRR data using mixels
Author :
Kageyama, Yoichi ; Sato, Ikuma ; Nishida, MaKoto
Author_Institution :
Akita Univ., Akita
Abstract :
This study proposes an automatic classification algorithm for NOAA (National Oceanic and Atmospheric Administration)-AVHRR (Advanced Very High Resolution Radiometer) data using mixels. The proposed algorithm uses the properties of the NOAA-AVHRR multispectral bands, the mixels, and the NDVI (normalized difference vegetation index) to estimate the actual conditions. This study suggests that the proposed approach provides reasonable results as compared to those of maximum likelihood estimation and k-means clustering.
Keywords :
geophysical signal processing; image classification; maximum likelihood estimation; pattern clustering; radiometry; remote sensing; Advanced Very High Resolution Radiometer; NOAA-AVHRR data; National Oceanic and Atmospheric Administration; automatic classification algorithm; k-means clustering; maximum likelihood estimation; mixels; normalized difference vegetation index; Classification algorithms; Clouds; Clustering algorithms; Computer science; Data engineering; Data mining; Fuzzy reasoning; Maximum likelihood estimation; Radiometry; Sea surface; NDVI; NOAA; edge; fuxxy reasoning; mixel;
Conference_Titel :
Geoscience and Remote Sensing Symposium, 2007. IGARSS 2007. IEEE International
Conference_Location :
Barcelona
Print_ISBN :
978-1-4244-1211-2
Electronic_ISBN :
978-1-4244-1212-9
DOI :
10.1109/IGARSS.2007.4423232
