The automatic detection of ships in low-resolution synthetic aperture radar (SAR) imagery is investigated in this article. The detector design objectives are to maximise detection accuracy across multiple images, to minimise the computational effort during image processing, and to minimise the effort during the design stage. The results of an extensive numerical study show that a novel approach, using genetic programming (GP), successfully evolves detectors which satisfy the earlier objectives. Each detector represents an algebraic formula and thus the principles of detection can be discovered and reused. This is a major advantage over artificial intelligence techniques which use more complicated representations, e.g. neural networks.
%0 Journal Article
%1 Howard1999303
%A Howard, Daniel
%A Roberts, SimonC
%A Brankin, Richard
%D 1999
%J Advances in Engineering Software
%K *file-import-12-02-26 genetic, programming,
%N 5
%P 303--311
%R 10.1016/S0965-9978(98)00093-3
%T Target detection in SAR imagery by genetic programming
%U http://www.sciencedirect.com/science/article/pii/S0965997898000933
%V 30
%X The automatic detection of ships in low-resolution synthetic aperture radar (SAR) imagery is investigated in this article. The detector design objectives are to maximise detection accuracy across multiple images, to minimise the computational effort during image processing, and to minimise the effort during the design stage. The results of an extensive numerical study show that a novel approach, using genetic programming (GP), successfully evolves detectors which satisfy the earlier objectives. Each detector represents an algebraic formula and thus the principles of detection can be discovered and reused. This is a major advantage over artificial intelligence techniques which use more complicated representations, e.g. neural networks.
@article{Howard1999303,
abstract = {{The automatic detection of ships in low-resolution synthetic aperture radar (SAR) imagery is investigated in this article. The detector design objectives are to maximise detection accuracy across multiple images, to minimise the computational effort during image processing, and to minimise the effort during the design stage. The results of an extensive numerical study show that a novel approach, using genetic programming (GP), successfully evolves detectors which satisfy the earlier objectives. Each detector represents an algebraic formula and thus the principles of detection can be discovered and reused. This is a major advantage over artificial intelligence techniques which use more complicated representations, e.g. neural networks.}},
added-at = {2012-03-02T03:39:18.000+0100},
author = {Howard, Daniel and Roberts, SimonC and Brankin, Richard},
biburl = {https://www.bibsonomy.org/bibtex/2ffbf37ccb11b108635f5ae1695fe6f8c/baby9992006},
citeulike-article-id = {10386987},
citeulike-linkout-0 = {http://dx.doi.org/10.1016/S0965-9978(98)00093-3},
citeulike-linkout-1 = {http://www.sciencedirect.com/science/article/pii/S0965997898000933},
doi = {10.1016/S0965-9978(98)00093-3},
interhash = {49d3797d54a5a71d758589400e1742d7},
intrahash = {ffbf37ccb11b108635f5ae1695fe6f8c},
journal = {Advances in Engineering Software},
keywords = {*file-import-12-02-26 genetic, programming,},
number = 5,
pages = {303--311},
posted-at = {2012-02-26 12:57:33},
priority = {2},
timestamp = {2012-03-02T03:39:20.000+0100},
title = {{Target detection in SAR imagery by genetic programming}},
url = {http://www.sciencedirect.com/science/article/pii/S0965997898000933},
volume = 30,
year = 1999
}