- author = "J. M. Daida and J. D. Hommes and S. J. Ross and J. F. Vesecky",
- title = "Extracting curvilinear features from SAR images of arctic ice: Algorithm discovery using the genetic programming paradigm",
- booktitle = "Proceedings of IEEE International Geoscience and Remote Sensing",
- year = "1995",
- editor = "T. Stein",
- pages = "673--675",
- address = "Florence, Italy",
- publisher_address = "Washington",
- publisher = "IEEE Press",
- keywords = "genetic algorithms, genetic programming",
-
URL = "
ftp://ftp.eecs.umich.edu/people/daida/papers/igarss95_GP.pdf",
-
URL = "
http://citeseer.ist.psu.edu/406479.html",
-
notes = "This paper focuses on how a method for automated
programming (i.e., genetic programming) applies in the
computeraided 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.
The research on algorithm discovery uses the genetic programming paradigm to assist geoscientists in extracting textural features from satellite synthetic aperture radar imagery (i.e., ERS-1). Manual methods are extremely time consuming and limited to a few frames (in this case, a 1k by 1k low-res data product, or a 8k by 8k hi-res data product). Desirable are semi-automated, automated, or computer-assisted algorithm developmental tools for data analysis. (gp-list 13 Apr 95)
Firenze, Italy",
