Title :
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
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;
Conference_Titel :
Geoscience and Remote Sensing Symposium, 2001. IGARSS '01. IEEE 2001 International
Conference_Location :
Sydney, NSW
Print_ISBN :
0-7803-7031-7
DOI :
10.1109/IGARSS.2001.977104
