Probability guided and minimum residual exhaustive searching approaches for subpixel classification

Author

Zhu, H. ; Eastman, J.R.

Author_Institution

Clark Labs, Clark Univ., Worcester, MA, USA

Volume

4

fYear

2001

fDate

2001

Firstpage

1883

Abstract

The linear mixing model (LMM) is one of the models to describe the characteristics of mixed pixels in a remotely sensed image. This paper discusses the inverse process, the unmixing process. A minimum residual exhaustive searching (MRES) method and a probability guided (PG) method are presented. The MRES method takes residuals as the measurement to search for the unmixing results with a minimum residual. The PG method makes use of statistical information to determine the dominant components of a pixel and then, to unmix the pixel into the dominant components

Keywords

geophysical signal processing; image classification; terrain mapping; MRES method; PG method; inverse process; linear mixing model; minimum residual exhaustive searching method; mixed pixels; probability guided method; remote sensing; remotely sensed image; statistical information; unmixing process; Data mining; Detectors; Electromagnetic scattering; Equations; Image classification; Layout; Pixel; Probability; Sensor phenomena and characterization; Spatial resolution;

fLanguage

English

Publisher

ieee

Conference_Titel

Geoscience and Remote Sensing Symposium, 2001. IGARSS '01. IEEE 2001 International

Conference_Location

Sydney, NSW

Print_ISBN

0-7803-7031-7

Type

conf

DOI

10.1109/IGARSS.2001.977104

Filename

977104