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首页> 外文会议>International symposium on multispectral image processing and pattern recognition >Airplane detection based on fusion framework by combining saliency model with Deep Convolution Neural Networks
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Airplane detection based on fusion framework by combining saliency model with Deep Convolution Neural Networks

机译：显着模型与深度卷积神经网络相结合的基于融合框架的飞机检测

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摘要

Aircraft detection from very high resolution remote sensing images, has gained more increasing interest in recent years due to the successful civil and military applications. However, several problems still exist: 1) how to extract the high-level features of aircraft; 2) locating objects within such a large image is difficult and time consuming; 3) A common problem of multiple resolutions of satellite images still exists. In this paper, inspirited by biological visual mechanism, the fusion detection framework is proposed, which fusing the top-down visual mechanism (deep CNN model) and bottom-up visual mechanism (GBVS) to detect aircraft. Besides, we use multi-scale training method for deep CNN model to solve the problem of multiple resolutions. Experimental results demonstrate that our method can achieve a better detection result than the other methods.
机译：由于民用和军用的成功应用，近年来从非常高分辨率的遥感图像中进行飞机检测已经引起了越来越多的兴趣。但是，仍然存在一些问题：1）如何提取飞机的高级特征; 2）在如此大的图像中定位对象既困难又费时; 3）仍然存在卫星图像多分辨率的普遍问题。在生物视觉机制的启发下，提出了融合检测框架，融合了自上而下的视觉机制（深度CNN模型）和自下而上的视觉机制（GBVS）来检测飞机。此外，针对深度CNN模型，我们采用了多尺度训练方法来解决多分辨率问题。实验结果表明，与其他方法相比，我们的方法可以获得更好的检测结果。

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