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首页> 外文会议>International Congress on Image and Signal Processing, BioMedical Engineering and Informatics >A Fast Training Method for SAR Large Scale Samples Based on CNN for Targets Recognition
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A Fast Training Method for SAR Large Scale Samples Based on CNN for Targets Recognition

机译：基于CNN的SAR大规模样本快速训练目标识别方法。

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摘要

In recent years, as CNN has made breakthroughs in targets detection and recognition, such method has drawn increasing attention on targets recognition of SAR images. However, when CNN was applied to targets recognition of SAR images, its training efficiency was severely limited by the abundant pixel units of SAR image samples. Compared with CNN commonly used samples, the high resolution SAR images contain more pixel units. If the CNN is directly applied to SAR images, the process of extracting features will have low computational efficiency, which seriously affects the performance of targets recognition. In response to this problem, a method of this paper for preprocessing the input samples is proposed. The experimental results of the real airborne SAR data verify the efficiency of this method.
机译：近年来，随着CNN在目标检测和识别方面取得突破，这种方法越来越引起人们对SAR图像目标识别的关注。然而，当CNN应用于SAR图像的目标识别时，其训练效率受到SAR图像样本的大量像素单元的严重限制。与CNN常用样本相比，高分辨率SAR图像包含更多像素单元。如果将CNN直接应用于SAR图像，特征提取过程将具有较低的计算效率，这将严重影响目标识别的性能。针对这一问题，提出了一种对输入样本进行预处理的方法。实际机载SAR数据的实验结果证明了该方法的有效性。

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