A Two Layer Neural Network Scheme for Improving Classification of Aerial Images

In aerial image classification the dynamic changes of the earth environment or the variety of the geographic locations aggravates the performance of conventional classifiers. To overcome this problem a automatic mechanism is proposed at the user level for optimal categorization of each new incoming image. The proposed mechanism is based on two layer neural network scheme. The first layer is a conventional neural network classifier while the second consists of a MAP estimation procedure and another neural network classifier. The role of the MAP estimation procedure is to optimally select those data of the first classifier so as to correct the misclassification producing more accurate results. Introduction Probably the most important issue when designing and training artificial neural networks especially for aerial photographs classification is network generalization. Recent studies about the use of neural networks for classification of satellite images have shown that the accuracy of the classification can dropped up to 25% when images, from another geographic location, are used. On the contrary, when the network is training on both localities, ratios of 80% accuracy can be achieved [1]. These geographical generalization problems can be due to various reasons, including the fact that earth locations can be quite different from each other, the fact that the conditions of image capturing are generally different, or even the fact that training sets may not be representative enough. Since for commercial applications the performance of the training procedure cannot be realized at the user level, an automatic selection of the most appropriate data, from each new photograph, is required so as to improve the classification. In this paper, we propose an innovative scheme based on a two-layer neural network structure and a Maximum A Posteriori (MAP) estimation procedure which ameliorates the performance of the second layer relied on the output of the first one. This two-layer structure can adapt the hyper-surfaces according to the current environment. The first layer produces an approximation of the final classification map while the second produces the final categorization based on the output of first layer and the MAP procedure. Additional feature elements can be introduced as input vectors for the second layer for improving its performance. MAP estimation mechanism minimizes a convex function using bayesian technique. As a result erroneous decisions of the first layer are eliminated whereas assisting the second layer to extract much more accurate classification. Bayesian technique is applied to the first layer network output under the consideration that the properties of Markov Random Fields (MRFs) are satisfied [2]. Images from Brotazt album have been used in order to confirm our results. The goal of the classification is to separate sea areas from land ones. Each image is examined at block based (8x8 pixels) while Discrete Cosine Transform (DCT) is used as feature vectors. 2. The proposed scheme Let us assume an image f r ( ) , where r m n T = [ , ] and m, n are restricted by the image dimensions In any classification problem the target is to find for each image f r ( ) partitions Si whose their resolution will depend on the selected block size and categorize each of them to one of the p available classes i, i=1,2,. ,p. In general, for better categorization of the partitions Si, feature vectors are extracted from the examined image by applying transforms on f r ( ) and mapping the image space Df into another space. Let us assume that two different feature vectors are extracted from the image blocks, corresponding to features used for the first layer and to additional features that can be used for second layer respectively. Let denote the first group h h h h T T L T T = [ . ] 1 2 , where L is the number of blocks, and the second group g g g g T T L T T = [ , , . , ] 1 2 , where hi T , g i T are the feature vectors for each block i. The total vector that characterizes the image is t h g T T T = [ , ] . The first layer network classifier is assumed to receive as input vectors the first group of features h . The output of the final layer classifier is fed by the total feature vector t and produces the final classification of the image blocks. Based on its input vectors, the network of the first layer will generalize and classify the partitions, or blocks, Si of each image to one of available classes. Due, however, to generalization errors, owning to dynamic changes of the environment or to large variety of geographic locations misclassifications will not be avoided [1]. It would be very important, after classifying all blocks of the image, if some mechanism could detect misclassifications and assists the network of the second layer, correcting the erroneous decisions of the first one. The required time for selecting the optimal data of the each new image is not so crucial in such applications since the decision is not necessary to be concluded immediately. However, proper choice of the structure of the networks can accelerate the decision time. This will be useful for applications where the final classification should be taken in real time. Figure 1 presents the proposed scheme.