Model-based feature classification and change detection

Author

Ngai, Francis M. ; Curlander, James C. ; Stocker, Alan D.

Author_Institution

Vexcel Corp., Boulder, CO, USA

Volume

4

fYear

1994

fDate

8-12 Aug 1994

Firstpage

2531

Abstract

Describes a novel image processing system which performs constant false-alarm rate (CFAR) change detection and model-based feature classification on temporally separated and precision registered images. The authors present results of algorithms performed on 0.5 m to 2 m resolution scanned aerial photographs of the Yuma, Arizona region to identify changes that correspond to man-made targets and new roads and to classify false changes

Keywords

feature extraction; geophysical signal processing; geophysical techniques; image classification; image sequences; optical information processing; remote sensing; Arizona; United States USA; Yuma; aerial photograph; algorithm; change detection; constant false-alarm rate; false changes; feature extraction image classification; geophysical measurement technique; image processing; image sequences; land surface; man-made target; model-based feature classification; optical imaging; remote sensing; road; temporally separated; terrain mapping; Change detection algorithms; Computer vision; Data processing; Gas detectors; Image processing; Image resolution; Layout; Pixel; Roads; Spatial resolution;

fLanguage

English

Publisher

ieee

Conference_Titel

Geoscience and Remote Sensing Symposium, 1994. IGARSS '94. Surface and Atmospheric Remote Sensing: Technologies, Data Analysis and Interpretation., International

Conference_Location

Pasadena, CA

Print_ISBN

0-7803-1497-2

Type

conf

DOI

10.1109/IGARSS.1994.399789

Filename

399789