李英成,钱赛男,朱祥娥,刘晓龙,李晶晶

• 1:中测新图(北京)遥感技术有限责任公司
• 2:中国测绘科学研究院
• 3:自然资源部航空遥感技术重点实验室

摘要(Abstract)：

针对大规模无序图像分类处理中成对图像的匹配和几何验证的计算量大的问题,该文通过研究和学习机器学习及图像识别领域先进的方法,提出了一种基于孪生神经网络的大规模图像有序化方法。该算法主要是:通过抽取已训练好的VGG19的网络模型的卷积层作为图像的特征,将提出的特征分别加权后,连接起来,再次卷积和池化,利用响应函数判定图像之间连通性,实现对输入图像对连通性判定。经实验证明,该算法可有效地识别具有场景重叠的图像对,效率和精度上也有所提高,无须执行详尽的推定匹配和几何验证,适用于运动恢复结构,图像连接等各种场景。

关键词(KeyWords)： 卷积神经网络;VGGNet;siamese学习;图像对;无序图像;图像分类

Abstract:

Keywords:

基金项目(Foundation): 国家重点研发计划项目(2017YFB0503004)

作者(Author): 李英成,钱赛男,朱祥娥,刘晓龙,李晶晶

DOI: 10.16251/j.cnki.1009-2307.2019.06.017

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