星载SAR图像机场兴趣区检测算法

提出一种解决低信噪比、大尺寸的星载合成孔径雷达(SAR)图像机场兴趣区(ROI)自动检测问题的方法。对图像进行基于马尔可夫场的分割，由目标标记方法确定ROI的候选区。用Hough变换检测跑道平行线，滤除部分伪ROI。计算ROI候选区的跑道方向投影直方图及目标的宽、高、宽高比、目标背景面积比和目标背景灰度比5个参数，用支持向量机对样本参数进行学习，完成ROI的最终判定。用实际星载SAR图像进行试验，结果表明了该方法的有效性和可靠性。

Detection Algorithm for Airport ROI in Spaceborne SAR Image

This paper proposes a novel airport Region of Interest(ROI) detection algorithm for large-scale spaceborne Synthetic Aperture Radar(SAR) image. The image is segmented via ICM algorithm according to the frame of MAP and the model of markov random field. The candidate ROIs are recorded by using object labeled algorithm. The parallel is detected by Hough transformation and the ones without parallel are removed. The histogram of projection of the candidate ROIs are calculated and the sample vector of candidate ROIs are constructed by five factors, including width and height of object, Ratio between Width and Height(RWH), Area Ratio between Object and Background(AROB) and Gray Mean between Object and Background(GMROB). Support Vector Machine(SVM) classifier is trained and applied to give the final result of ROI detection. Experiment with actual data indicates the algorithm is effective.