Title :
A new combined classification method for land covering using AVHRR data supervised by TM data
Author :
Hua Qin ; Li, Ming ; Sun, Weidong
Author_Institution :
Dept. of Electron. Eng., Tsinghua Univ., Beijing, China
Abstract :
High resolution satellite images with wide area coverage are usually expensive and rarely obtained, but the precision of classification basically depends on the resolution of the satellite images. In this paper, a new combined classification method using low-resolution satellite data (NOAA AVHRR data) supervised by high-resolution satellite data (LANDSAT TM data) was proposed. In this method, some sample areas of TM data were classified by unsupervised cluster at first, and then the likelihood functions were estimated for each class according to the "multi-single" spatial correspondence between TM and AVHRR pixels. Finally, the whole AVHRR image can be classified with these likelihood functions by maximum likelihood classification method. The result of our experiments shown that, the accuracy of the classification greatly depended on where and which the sample areas were selected, the precision of the classification would be equal to those classified by high resolution satellite images directly if the sample areas were selected properly.
Keywords :
geophysical signal processing; image classification; image resolution; maximum likelihood estimation; terrain mapping; AVHRR data; NOAA; TM data; high resolution satellite images; image classification; land covering; maximum likelihood classification method; maximum likelihood estimation method; unsupervised cluster; Data analysis; Data engineering; Image resolution; Maximum likelihood estimation; Remote monitoring; Remote sensing; Satellites; Spatial resolution; Sun; Testing;
Conference_Titel :
Robotics, Intelligent Systems and Signal Processing, 2003. Proceedings. 2003 IEEE International Conference on
Print_ISBN :
0-7803-7925-X
DOI :
10.1109/RISSP.2003.1285710