Extracting curvilinear features from synthetic aperture radar images of Arctic ice: algorithm discovery using the genetic programming paradigm

Author

Daida, Jason M. ; Hommes, Jonathan D. ; Ross, Steven J. ; Vesecky, John F.

Author_Institution

Artificial Intelligence Lab., Michigan Univ., Ann Arbor, MI, USA

Volume

1

fYear

34881

fDate

10-14 Jul1995

Firstpage

673

Abstract

Focuses on how a method for automated programming (i.e., genetic programming) applies in the computer-aided discovery of algorithms that enhance and extract features from remotely sensed images. Highlighted as a case study is the use of this method in the problem of extracting pressure ridge features from ERS-1 SAR imagery; a problem for which there has been no known satisfactory solution

Keywords

feature extraction; genetic algorithms; geophysical signal processing; oceanographic regions; oceanographic techniques; radar applications; radar imaging; remote sensing by radar; sea ice; spaceborne radar; synthetic aperture radar; Arctic ice; SAR image; algorithm; automated programming; curvilinear feature extraction; genetic programming; image processing; measurement technique; ocean; pressure ridge; radar imaging; radar remote sensing; sea ice; synthetic aperture radar; topography; Arctic; Artificial intelligence; Automatic programming; Data mining; Feature extraction; Genetic programming; Image segmentation; Laboratories; Sea ice; Synthetic aperture radar;

fLanguage

English

Publisher

ieee

Conference_Titel

Geoscience and Remote Sensing Symposium, 1995. IGARSS '95. 'Quantitative Remote Sensing for Science and Applications', International

Conference_Location

Firenze

Print_ISBN

0-7803-2567-2

Type

conf

DOI

10.1109/IGARSS.1995.520489

Filename

520489