基于遥感图像分块直线特征检测的机场跑道检测方法

doi:10.7527/S1000-6893.2013.0476

Abstract

Abstract:

A method of airport runway detection is proposed based on line feature detection of partitioned remote sensing images. First, in order to deal with the computational difficulties arising from the huge data size of remote sensing images which may be hundreds of megabytes or even gigabytes, overlapped partitions are carried out on these images, and line feature detection is performed on each part by introducing the line segment detector (LSD). Then, based on the mathematical features of airport runways which are summarized and listed, the extracted line features are divided into several parallel line groups according to angles, and for each group, after line growing and merging, candidate airport runway areas are obtained with the help of Radon transform. Finally, airport runways are located and confirmed based on the gray-scale statistical information of the candidate runways and the histograms of oriented gradients. The performance of the proposed approach is validated by experiments carried on a series of remote sensing images downloaded from Google Earth, from which effective line features can be extracted.

Key words: remote sensing image, airport runways, detection, partitioned LSD, Radon transform, histogram of oriented gradients

CLC Number:

V19
TP391

MENG Gang, HE Jie, BAO Li, WANG Jiantao, YAN Sunzhen, XU Jinping. Airport Runway Detection Based on Line Feature Detection of Partitioned Remote Sensing Images[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2014, 35(7): 1957-1965.

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Airport Runway Detection Based on Line Feature Detection of Partitioned Remote Sensing Images

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