DocumentCode :
330083
Title :
Assessing the robustness of neural network classifiers
Author :
Marin, John A. ; Ray, Clark K. ; Brockhaus, J. ; Klingseisen, Robert
Author_Institution :
Dept. of Electr. Eng. & Comput. Sci., US Mil. Acad., West Point, NY, USA
Volume :
5
fYear :
1998
fDate :
11-14 Oct 1998
Firstpage :
4257
Abstract :
A preliminary report on the first phase of a multiyear experiment designed to assess the robustness of neural network classifiers compared to human experts involving the classification of terrain features is presented and discussed. The experiment includes a definition of the problem, description of the terrain data sets, preprocessing of the imagery, a variable reduction scheme involving genetic algorithms, manual and automatic classification routines, and an assessment of the different methodologies. Preliminary results of a parametric and automatic classification of a Landsat image are also presented
Keywords :
genetic algorithms; military computing; neural nets; pattern classification; terrain mapping; Landsat image; automatic classification; genetic algorithms; imagery preprocessing; manual classification; neural network classifier robustness assessment; parametric classification; terrain feature classification; variable reduction scheme; Computer networks; Design engineering; Geography; Humans; Hyperspectral imaging; Military computing; Neural networks; Robustness; Satellites; Testing;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Systems, Man, and Cybernetics, 1998. 1998 IEEE International Conference on
Conference_Location :
San Diego, CA
ISSN :
1062-922X
Print_ISBN :
0-7803-4778-1
Type :
conf
DOI :
10.1109/ICSMC.1998.727514
Filename :
727514
Link To Document :
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