Title :
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
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;
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
DOI :
10.1109/IGARSS.1995.520489
