SETIT 2007 Object Oriented Classification of Forest Images Using Soft Computing Approach

Abstract

Abstract: In this paper the searching capability of build up an object oriented classification system which is capable of classification a given forest scene into its various constituents. To simplify the problem, six categories of forest structures were defined. These categories are trees, bushes, grasses, foliage, sky and background sky .They are sufficient to represent typical forest scenes dealt with in this application. To implement such classification system. We proposed a Genetic Algorithm (GA) to segmentation image and find the best seed for each category. According to this scheme, an image is divided evenly into small block. Then it is processed block by block. For each block, Discrete Cosine Transform (DCT) is applied to determine some of DCT coefficient in compressed domain as the feature vectors. Then take the seed values for each segment and the DCT coefficients to represented the inputs of feed forward neural network. These system successes in classification all objects in image although used different kinds of activation functions (hyperbolic functions), compares among them and find the best of it in obtaining on fast results. As a result the A soft computing method will be higher classification accuracy than that of traditional pixel-based supervised classification and gives convenient environment to use