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首页> 外文会议>Signal Acquisition and Processing, 2009. ICSAP 2009 >Multiresolution Framwork with Neural Network Approach for Automatic Target Recognition (ATR)
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Multiresolution Framwork with Neural Network Approach for Automatic Target Recognition (ATR)

机译：具有神经网络方法的多分辨率框架，用于自动目标识别（ATR）

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Automatic Target Recognition (ATR) is an approach by which we identify one or a group of target-objects in a scene. It plays a pivotal role in the challenging fields of defense and civil. Most of the methods in this context are based on fix window-size technique. In this paper we propose a novel approach which gives scale, rotation and translation invariant results for automatic target recognition in high-resolution satellite images which in turn are able to recognize the multiple targets in a scene. We have developed a system which can predict the possible area of interest in a scene, where target may be present or not. Prediction of areas of interest is based on edge detection and similarity measure of wavelet co-occurrence features of segmented sub-blocks. Zernike moments, calculated for scale and translation normalized area of interest, is thereby used as the features of the concerned area. Zernike moments are rotation invariant. The extracted features are then fed to trained neural network for recognition. This approach is more suitable for the satellite images because resolution of image and idea about the target are two essential factors by which we can predict the minimum and maximum size of the target. The approach takes considerably less time compared to the fix window based approach because the predicted numbers of interest areas to be processed in a scene are very less. The proposed approach has successfully been tested on number of satellite images of different resolutions and their timing analysis has been compared with fix window based approach.
机译：自动目标识别（ATR）是一种我们可以识别场景中一个或一组目标对象的方法。它在具有挑战性的国防和民用领域中发挥着举足轻重的作用。在这种情况下，大多数方法都基于固定窗口大小技术。在本文中，我们提出了一种新颖的方法，该方法为高分辨率卫星图像中的目标自动识别提供了比例，旋转和平移不变的结果，从而能够识别场景中的多个目标。我们已经开发了一种可以预测场景中可能存在的感兴趣区域的系统，无论目标是否存在。感兴趣区域的预测基于边缘检测和分段子块小波共现特征的相似性度量。因此，针对比例和平移归一化感兴趣区域计算出的Zernike矩用作相关区域的特征。 Zernike矩是旋转不变的。然后将提取的特征输入经过训练的神经网络进行识别。这种方法更适合于卫星图像，因为图像的分辨率和关于目标的想法是我们可以预测目标最小和最大尺寸的两个基本因素。与基于固定窗口的方法相比，该方法花费的时间少得多，因为在场景中要处理的兴趣区域的预测数量非常少。所提出的方法已经成功地在不同分辨率的卫星图像上进行了测试，并将其时序分析与基于固定窗口的方法进行了比较。

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