关键词: 机场检测, 重复模式, 直线平行性, 局部对比度, 深度卷积网络

Abstract: To deal with large amount of remote sensing image data and the shortcoming of existing airport detection methods, a new algorithm which eliminates repetitive patterns, fusing linear saliency and regional saliency is proposed. Firstly, an improved saliency model based on Hypercomplex Fourier Transform（HFT） is used to remove repetitive patterns in remote sensing images, so as to reduce the amount of subsequent data processing. Secondly, runways have parallel long straight line characteristics and there are differences between the airport and the surrounding environment. The linear saliency map and the regional saliency map are calculated according to these characteristics. Then two saliency maps are fused. Finally, the candidate regions are determined according to the runway width. The deep Convolution Neural Network（CNN） and Support Vector Machine（SVM） are used to extract feature and determine whether the regions contain airports or not. Experimental results show that this algorithm is more accurate for the airport location, with higher recognition rate, lower false alarm rate and faster speed.

Key words: airport detection, repetitive patterns, linear parallelism, local region contrast, deep convolution neural network