Automatic classification algorithm for NOAA- AVHRR data using mixels

Author

Kageyama, Yoichi ; Sato, Ikuma ; Nishida, MaKoto

Author_Institution

Akita Univ., Akita

fYear

2007

fDate

23-28 July 2007

Firstpage

2040

Lastpage

2043

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;

fLanguage

English

Publisher

ieee

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

Type

conf

DOI

10.1109/IGARSS.2007.4423232

Filename

4423232