| Airport recognition has always received much attention. It contains two aspects: identification and localization of airport. Nowadays, many algorithms for airport recognition have been presented, but lack of efficient strategies to identify airport. Among them, an algorithm based on contextual information has been approved to exactly detect the airport localization, but has a high false alarm rate. So, a new method to reduce false alarm in the airport recognition under the precondition of high detection rate is presented.Firstly, this thesis introduces the algorithm to recognize airport based on contextual information, and then analyzes the origins of high false alarm rate from 3 aspects: runway detection, taxiway detection and the criteria of airport identification.Secondly, runway recognition using a runway contrast metric is conducted. Since runways are always significantly bright, we use the contrast between runway lines'and other lines'Radon transformed results to characterize the runway contrast metric. We present several candidate metrics and choose one that can best express the contrast.Thirdly, two discriminances to classify the runway contrast metric are presented. One is based on the difference of the two image classes (airport image and non-airport image), the other is based on the diversity of non-airport image. We compare the two discriminances through experiments.Finally, taxiway recognition is used to further reduce false alarm after runway recognition. Taxiway recognition is mainly according to 3 features: the uniformity of taxiway's grayscale, the salience of taxiway's brightness and the information of taxiway's extended lines. Recognition of at least one taxiway can prove the existence of airport.The experiments demonstrate most false alarms can be excluded in the runway recogniton and the rest can be eliminated in the taxiway recognition. |
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