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Proceedings Paper

Unsupervised texture classification method using appropriate training area selection based on genetic algorithms
Author(s): Hiroshi Okumura; Masaru Maeda; Hideki Sueyasu; Yuuki Togami; Takeshi Tadanou; Kohei Arai
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Paper Abstract

A new unsupervised texture classification method based on the genetic algorithms (GA) is proposed. In the method, the GA are employed to determine location and size of the typical textures in the target image. The proposed method consists of the following procedures: (1) the determination of the number of classification category; (2) each chromosome used in the GA consists of coordinates of center pixel of each training area candidate and those size; (3) 50 chromosomes are generated using random number; (4) fitness of each chromosome is calculated; the fitness is the product of the Classification Reliability in the Mixed Texture Cases (CRMTC) and the Stability of NZMV against Scanning Field of View Size (SNSFS); (5) in the selection operation in the GA, the elite preservation strategy is employed; (6) in the crossover operation, multi point crossover is employed and two parent chromosomes are selected by the roulette strategy; (7) in mutation operation, the locuses where the bit inverting occurs are decided by a mutation rate; (8) go to the procedure 4. Some experiments are conducted to evaluate classification capability of the proposed method by using images from Brodatz's photo album and actual airborne multispectral scanner. The experimental results show that the proposed method can select appropriate texture samples and can provide reasonable classification results.

Paper Details

Date Published: 5 February 2004
PDF: 9 pages
Proc. SPIE 5238, Image and Signal Processing for Remote Sensing IX, (5 February 2004); doi: 10.1117/12.510908
Show Author Affiliations
Hiroshi Okumura, Saga Univ. (Japan)
Masaru Maeda, Hitachi Medical Corp (Japan)
Hideki Sueyasu, Saga Univ. (Japan)
Yuuki Togami, Saga Univ. (Japan)
Takeshi Tadanou, Saga Univ. (Japan)
Kohei Arai, Saga Univ. (Japan)

Published in SPIE Proceedings Vol. 5238:
Image and Signal Processing for Remote Sensing IX
Lorenzo Bruzzone, Editor(s)

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