Recognition of Aerospace Images with Neural Networks

Recognition of Aerospace Images with Neural Networks Karen Lucero Roldán-Serrato, Tetyana Baydyk, Ernst Kussul, Graciela Velasco-Herrera Centro de Ciencias Aplicadas y Desarrollo Tecnológico. Universidad Nacional Autónoma de México. [email protected]

Abstract The purpose of this work is to develop a recognition system of aerospace images based on neural networks. We used an image database collection of 10 images with different geographic areas, for example, urban zones, forests, etc. We made the texture feature extraction of images. As features we calculate the histogram of brightness and histogram of contrast for every image. To classify the different zones we apply the neural classifier RTC. We present in the article the structure and algorithms of this neural classifier. We describe the initial results of image recognition system development. 1. Introduction In the late twentieth century, a high resolution satellite images were obtained. Different methods are developed to extract the useful information from these images, for example, multispectral classification techniques [1], statistical methods [2] and, so on. One of the interesting tasks of aerospace image recognition is the task of classification of land cover types [3]. For this purpose texture features are used [4]. The internal variability of images reduces the statistical separability of the classes. For example, a tree can have variability, when it is illuminated by sunlight or partly in the dark side (Fig. 1). The tree has very different spectral response, belonging to the same class [5].

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Fig.1. Tree image variability

The different segmentation algorithms were proposed [2]. Some of them are based on analysis of very small regions [6]. The procedure is based on three criteria: color, smoothness and compactness (Fig. 2) [6].

Fig. 2. Segmentation algorithm of a satellite image [6] Another technique for recognition of satellite images is the feature extraction [7]. In this technique, several features are extracted to describe color, texture, form and design, especially color histogram (CH), color coherence vector (CCV), coefficient of the discrete cosine transform (DCT), range direction histogram (EDH) and vector coherent direction range (EDCV). In this paper we proposed to use neural networks for the classification and recognition of aerospace images. The neural classifier RTC (Random Threshold Classifier), which has a multilayer structure [8] can be used in the task of image recognition. This classifier has shown good results in the metallic texture recognition [9]. We use a supervised training. The database of 10 images of different types of aerospace landscape was used. Each image was obtained from [10] with the size of each image of 500x500 pixels. We select to recognize three classes: 1) field (crops), 2) forests (parks) and 3) city (built up area). 2. The RTC neural classifier structure At present in many technological developments in the field of medicine, instrumentation, aerospace engineering among others, the computer image recognition is used as a very important tool. There is development many techniques for image recognition [2]. The approach of this work is artificial neural networks, specifically neural classifier called RTC (Random Threshold Classifier). The RTC neural classifier has a multilayer structure [8] and it is very efficient in the task of image recognition. The training of this classifier is a supervised training. This classifier showed good results in the recognition of metallic textures [9]. 2.1 Images and feature extraction We selected an aerospace image base of 10 images of size 500x500 pixels. In Fig. 3 several images are presented with forest and urban areas.

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The extraction of features of histograms of brightness, a) for each image is made with calculations b) contrast and histogram of microcontours. They are used as the input characteristics for the RTC NASA Astronaut. Soichi Noguchi. NASA Astronaut. Soichi Noguchi. classifier. http://twitpic.com/photos/Astro_Soichi http://twitpic.com/photos/Astro_Soichi The image pixels have brightness from 0 to 255. We divided into 16 levels of gray. For each interval the number of pixels having Fig. gray3.level in thisofinterval was images calculated. In this way we obtained the Examples aerospace brightness histogram. To obtain he contrast histogram we calculate the difference of will give us the difference in brightness between two neighboring pixels and move for a pixel to be computed. The algorithm of calculation of histogram of micro contours orientations is based on the algorithm of Schwartz [11]. 2.2 RTC Neural Classifier In Fig. 4 the RTC neural classifier structure is presented [12]. The inputs of the system are obtained from the histograms of brightness, contrast, and micro contour orientations (X1, X2, .., Xn). Every feature X is input for two neurons with thresholds l and h. Its superscript corresponds to group of neurons, and the subscript corresponds to the features. The thresholds l and h are chosen randomly. Always the condition l