In this paper, a new approach is proposed for texture feature extraction of satellite images using genetic algorithms. In this approach, the texture feature does not change by changing the angle of the texture. First, image texture is convolved with a trained mask, and then its energy is calculated. A regulatory mask has also been trained by a genetic algorithm using many examples of textured images. Feature extraction experiments are performed on a series of aerial images. Experimental results on both agricultural and urban images show the effectiveness of this feature for texture discrimination process of satellite images. Feature extraction for texture analysis is the key method that is used for texture recognition of satellite images. It is divided into four main categories: 1- structural methods; 2- model-based methods, 3- conversion methods, and 4- statistical methods (1-2). Texture of structural method is defined as micro-texture and macro-texture. The advantage of structural method describes a good symbolic from images and more for composition analysis is presented. This method is not suitable for normal textures, since great variability exists in micro-texture and macro-texture and cannot be identified to distinguish them. Texture analysis based on such models, fractal and Markov models are based on building an image, so that it can be used to describe the composition and texture. In this method, the image, as a possible model or as a combination of basic functions is described by a set of lines. Fractal model for modeling natural textures and sizes and analyzed texture and texture differentiation is useful, but this method is weak to choose the angle and describing the structure of the local image context is useful. Methods such as Fourier transform, Gabor and violet by using frequencies within the image, do texture analysis work, and each has its own weaknesses. Statistical methods indirectly describe the texture distribution and the relationship between the gray levels of the image handles. For images with natural texture, most statistical methods are used for feature extraction. In statistical methods, texture is specified as a probability distribution on the image space to form a random model or a set of statistical features. The most common features that are actually used for texture analysis, are based on harmony search and search pattern in the fabric. In the weakness in features from most of the above methods, there is a change in the angle of the texture that will change, and therefore the feature for classification in the texture of a different type than the angle changes does not stay constant, and it cannot be so strong in appearance (3). In continued histological classification of other methods (4-5), a method for extracting a feature here is useful to apply the texture, which is a measure of energy. Of previous methods for texture classification, the feature space is necessarily random search. In this method, a random search of the parameter space for masks (6) takes place before applying the texture of the image and calculating the energy, initially trained by type of texture, which results in a simple and effective classifier. We apply a convolution mask on the texture image and obtain the energy of each pixel at position (I, J) from image. In the proposed method, we set the parameters for the mask instead of the conventional randomized algorithms, and then the genetic algorithm is used. By using the genetic algorithm makes, this method is insensitive to the angle of the texture and the significance of the classifier of random algorithms will increase. The reminder paper is organized as follows: Section 2 introduces desirable texture features. In Section 3, genetic algorithm is used to optimize a mask to extract the optimal texture features, and experimental results are presented in Section 4.