DIGITAL IMAGE PROCESSING: SUPERVISED CLASSIFICATION USING GENETIC ALGORITHM IN MATLAB TOOLBOX
Joaquim Jose Furtado, Zhihua Cai, Xiaobo Liu · 2010
Digital Image Processing (DIP) is a multidisciplinary science. The applications of image processing include: astronomy, ultrasonic imaging, remote sensing, medicine, space exploration, surveillance, automated industry inspection and many more areas. Different types of an image can be discriminated using some image classification algorithms using spectral features, the brightness and color information contained in each pixel. The classification procedures can be or unsupervised. With supervised classification, we identify examples of the Information classes (i.e., land cover type) of interest in the image. These are called training sites. The image processing software system is then used to develop a statistical characterization of the reflectance for each information class. Genetic algorithm has the merits of plentiful coding, and decoding, conveying complex knowledge flexibly. An advantage of the Genetic Algorithm is that it works well during global optimization especially with poorly behaved objective functions such as those that are discontinuous or with many local minima. MATLAB genetic algorithm toolbox is easy to use, does not need to write long codes, the run time is very fast and the results can be visual. The aim of this work was to realize the image classification using Matlab software. The image was classified using three and five classes, with a population size of 20 and time of 30, 50 and 100. The gotten results showed that the time seems to affect the classification more than the number of classes. (Report and Opinion 2010;2(6):53-61). (ISSN:1553-9873).