Based the Morphological Filtering BP Algorithm of SAR Image Recognition
SAR image recognition is an important content of of aviation image interpretation work. In this paper, the characteristics of SAR images a practical significance of morphological filtering neural network model and its adaptive BP learning algorithm. As can be seen through the experimental results, the algorithm can not only adapt to the complex and diverse background environment, and has a displacement of the same continuous moving target detection capability, telescopic invariant and rotation invariant features. Keywords morphological filtering; BP algorithm; neural network; Image Recognition Using imaging radar photographs, called the SAR images. SAR image, unlike the general aerial photos as truthfully reflect the target image, it is based on a different light and dark spots and stripes to display the target image, and the general aerial photographs images has significant difference. With the rapid development of technology of aerial reconnaissance, SAR image reconnaissance occupy an increasingly important position in its all weather, real time image transmission and other unique advantages in reconnaissance systems, image interpretation and identification method has become a hot research topic. In this paper, the characteristics of SAR images a practical significance of morphological filtering neural network model and its adaptive BP learning algorithm. The morphological filtering neural network system has a self organizing structure in the process of learning (for example involved in the interaction of the external environment) each neuron compete with each other and collaborate, to constantly adjust the value of the network weights and Distributed stored at each nerve element in giving some intelligence neural network to achieve optimization of image morphological filtering and target recognition. Solving performance applications of knowledge into the learning process to improve the practical problems (moving target detection) to be binding and boot, dynamic tracking learning algorithms use heuristic methods combined with the learning rules, trying to meet efficiency and the versatility requirements, to obtain the overall performance better learning outcomes. I Morphological filtering neural network model Assignment preprocessing the search space for a limited range of values, and improve the efficiency of algorithm optimization, the need for the dynamic range of the structural element. At this time, the available sequence images to assess gray upper and lower bounds to determine the sample on the gray layer remaining space, and in order to standardize the number of structure element B tolerance. Set grayscale range of the SAR image, the variable range of the B component, wherein. Unified Analysis for ease of model samples, the same time, the need to implement a measure of the normalization processing, the energy value of each mode samples a unified unit. When a hardware operation, this quantization pretreatment is also beneficial to reduce the dynamic range of the image signal, to save storage space. Set of morphological filters the input of the first model the distribution of the sample data in the form of and the corresponding structural elements The normalized image gray upper bound and the value calculated using the assessment mode samples and structural elements, limit the range [0,1] At this point, can be directly calculated of N, and Taking into account the erosion and dilation operations has duality Thus, the expression of morphological erosion and dilation operations modified to: Th Fk k g b f k B 1 1 0 max min b f k 2 2 0 max b f M kM 0 max (1) Fk k g b f M k B 1 1 min max b f M k 1 2 1 min b f kM 1 1 min (2) According to formula (1) (2) the morphology operation relationship can be designed directly corrosion and expansion operation of the neural network structure. The logic of the combination of erosion and dilation operations can be further designed morphological opening and closing operation of the three layer feedforward neural network model, as shown in Figure 1. When the hidden layer for corrosion computing network output layer for the expansion of computing network, constitute the morphological opening operation of neural networks. Conversely, when the hidden layer is a dilation operation network while the output layer is a corrosion operation network constituting the morphological closing operation neural network. Fig.1 Neural networks of opening or closing operation The structure element B as a specific template window in the entire image plane, a sliding filter (in this case involved in the operation of only the image data within the template window) can be completed to the image expansion or etching operations. While the opening and closing operation when the structure element B is required in the image plane, respectively sliding twice filtering the image Proceedings of the 2nd International Conference on Computer Science and Electronics Engineering (ICCSEE 2013) Published by Atlantis Press, Paris, France. (c) the authors 3035 data of the input layer thus participate in operation on each node in the output layer has exceeded the scope of the template window B. The result of the operation and the hidden layer: z g z G E R R z 2 (3) The stringent constraints ensure that the template window for each layer of the input data, and will be in the open, closing operation of the neural network structure, the introduction of defined weighting vectors W1 and W2 binarized. Namely: Corrosion: S n R n z Expansion: S n R n z (4) Other II The adaptive BP learning algorithm In this paper, the concept of learning method is a heuristic search space concept description and concept description space state optimization learning rules to complete the original image data (training sample set) Learning rules need to be constrained, the introduction of relevant prior knowledge and statistical regularities and preferred criteria (cost function) to guide the solution process. For filtering parameters optimization training, the most critical is the form of the nonlinear filter mapping output to try approaching the expected value of the training samples that require optimal solution consistent with the examples given and for the shortest description. At the same time, the need to take into account the termination of the maneuverability of the algorithm (stopping criterion) Therefore, the choice of the optimal solution relative error rate of the target as a corrective traction to optimize the search for the ideal cost function. Error rate based on the definition of the function, can be obtained as the gradient vector of the network weights