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13 March 2003 Appropriate training area selection for supervised texture classification by using the genetic algorithms
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Abstract
A new method for selection of appropriate training areas which are used for supervised texture classification is proposed. In the method, the genetic algorithms (GA) are employed to determine the appropriate location and the appropriate size of each texture category's training area. The proposed method consists of the following procedures: 1) the determination of the number of classification category and those kinds; 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 searching capability of appropriate training areas of the proposed method by using images from Brodatz's photo album and their rotated images. The experimental results show that the proposed method can select appropriate training areas much faster than conventional try-and-error method. The proposed method has been also applied to supervised texture classification of airborne multispectral scanner images. The experimental results show that the proposed method can provide appropriate training areas for reasonable classification results.
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Hiroshi Okumura, Masaru Maeda, and Kohei Arai "Appropriate training area selection for supervised texture classification by using the genetic algorithms", Proc. SPIE 4885, Image and Signal Processing for Remote Sensing VIII, (13 March 2003); https://doi.org/10.1117/12.463148
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