This paper presents a method to design programming system using hybrid techniques represented by soft computing to classify objects from the air photos and satellite images depending on their features with minimum acceptable error. These images usually consist of seven layers, while the work in this research focuses on dealing with three bands (red ,green and blue). This paper concerns with classifying five kinds of objects (urban area, forests, roads, rivers, football-stadiums). Accordingly, the database which describes that objects depending on their attributes were built. Then, the Evolution algorithm of type breeder genetic algorithm to procedure genetic clustering process to segment image which provides a number of clusters found in that image data set were used. To avoid the overlapping between clusters with other, one of the clustering validity measures called "Davies-Bouldin index" as fitness function of that algorithm was used. Moreover, four methods of the recombination, which are:(Discrete Recombination (DR), Extended Line Recombination (ELR), Extended Intermediate Recombination (EIR), Fuzzy Recombination(FR)) were discussed. Then, two types of features for each cluster which are visual features including(Pattern, Shape,Texture, Shadow, Associative), and statistical features represented by spectrum features that include (Intensity, Hue, Saturation ) were extracted. After that, feed forward neural network from type error back propagation neural network to determine the class under which each feature vector belongs to was used. At the last stage, IF-Then rule to form several rules that govern each class attributes were used.

GIS (Geographic Information System) has been used in military and commercial applications for many years. The data of GIS are very expensive. So, it is very important to prevent any illegal use of these data. Digital watermarking can provide potential solution. As we know, in most of the applications of digital watermarking, the watermark is used to protect the copyright of digital product. In the other word, the product (cover) is important. According to this fact, we presented 2D vector map watermarking for GIS digital map based on a new watermarking concept called intelligent watermarking. The need of this concept is increased in the last years to protect the wide range of digital maps data transmitted via computer networks. Briefly, the scheme depends on the feature of the cover. The embedding of the watermark can be done in several steps. First extracting some features from the original digital vector map. Second combine these features with external watermark in order to get the intelligent watermark. Third embed the intelligent watermark in the vector map to get the watermarked vector map. The extracting of the watermark can be done in several steps. First, extract the intelligent watermark from the watermarked digital vector map. Second decompose the intelligent watermark into features and external watermark. Third reverse the extracted features to features of watermarked vector map. So, if the watermarked vector map is attacked, this watermarked vector map will be distorted, otherwise will not be distorted. Our proposed scheme can be applied in all the other media.

There are many types of insects that affect agricultural fields. These harmful insects should be classified in a smart implementation for the rural fields. The main point to detect depends on their texture color. These textures are different from one insect to another. We propose a new hybrid method based on Gray Level Co-occurrence Matrix (GLCM) to detect the harmful insects in agricultural fields. The main idea shows that a tested image is composed of different texture regions of the insect and this will help to extract feature value. This paper consists of three steps: the first step extracts texture features using GLCM in four directions which are 0, 90, 180 and 270 degrees from the gray image. The second step trains the neural network depending on texture features in a large number of variety insect's images. The third step tests the unknown insect's image to classify it whether harmful or not. The purpose of this study helps rural farmers to detect the harmful insects and classify them to take care of their crops.