Airport detection using convolutional neural network and salient feature

doi:10.13474/j.cnki.11-2246.2019.0216

Abstract

Abstract: Existing algorithms of airport detection using handcraft features perform time-consuming and poor robustness. In view of these problems, this paper proposes a method using convolutional neural network and salient feature. First, a deep convolutional neural network is used to extract the regions of interest (ROI) from complex remote sensing images. Then, saliency detection based on frequency-tuned is introduced to get saliency map of those regions. Through segment on the saliency map and marking the connected region on the binary image, the maximum connected region which is most likely be area of the airport is extracted. Different kinds of airports are used to test and the results show that the proposed method has obvious advantages in precision and speed. With the aid of saliency detection, the precise boundary of the airport and runway can be obtained effectively and the effect and practical value of the airport detection are hugely improved.

Key words: airport detection, remote sensing image, convolutional neural network, salient feature

CLC Number:

P237

YU Donghang, ZHANG Ning, ZHANG Baoming, GUO Haitao, LU Jun. Airport detection using convolutional neural network and salient feature[J]. 测绘通报, 2019, 0(7): 44-49.

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