目的 在极化合成孔径雷达（synthetic aperture radar，SAR）图像中常用直线检测进行机场跑道的识别，但是河流、道路等与机场跑道具有相似直线的地物容易对检测结果造成干扰，出现检测目标难定位、目标模糊、多虚警等问题。为此，本文设计了一种利用目标散射特性结合局部二值模式（local binary patterns，LBP）特征分类的极化SAR图像机场跑道区域检测方法，采用LBP特征对极化SAR图像进行有监督的分类来提取真实的机场区域。方法 首先利用异化散射功率对极化SAR图像进行阈值分割，然后通过形态学处理得到疑似机场跑道区域，同时构建机场跑道和非机场跑道两类训练样本，并提取、统计样本的LBP特征，形成直方图，得到特征向量训练支持向量机（support vector machine，SVM）二分类器，其中SVM二分类器采用了径向基函数（radial basis function，RBF）核函数；接着对疑似机场跑道区域构建LBP特征，送入SVM二分类器中分类，对机场跑道进行检测识别，最终得到真实的机场跑道区域。结果 利用UAVSAR（uninhabited aerial vehicle synthetic aperture radar）系统采集的7幅极化SAR图像数据进行实验检测，并选取基于几何特征辨识跑道的两种算法进行对比，3种方法均有效检测出了7幅场景中的真实跑道，但是本文方法在7幅数据中总的虚警和漏警个数均为1，而两种对比算法中的虚警个数分别为2和11、漏警个数分别为8和1。结论 本文方法不仅能有效检测出机场跑道区域，且检测效果更好，计算量较小，虚警和漏警率低，效率更高。

Objective Straight or parallel lines are commonly used as a typical feature in airport runway detection and identification for polarimetric synthetic aperture radar (PolSAR) images. However, some ground targets, such as rivers and roads, have line features similar to airport runways. Thus, they are likely to interfere with detection results. That is, the line features used in detection may result in wrong detection, increasing the false alarm rate and resulting in other problems. To address this issue, this study designs a novel detection method that combines support vector machine (SVM) classification with local binary pattern (LBP) feature in airport runway detection. LBP feature describes the local texture information of an image. It is widely used in the fields of face recognition and target detection and classification. Compared with other features, LBP feature exhibits the beneficial characteristics of rotation invariance and grayscale invariance, making its use easy and effective in distinguishing among different ground objects. Method In this study, airport runway detection is performed on the basis of a classification method in which polarization characteristics are used to extract the region of interest (ROI) and LBP characteristics are used to train the SVM classifier. The proposed algorithm has two parts:the training and detection parts. In the training part, training samples are selected from the original PolSAR image data. We divide the training samples into two types. That is, the samples from the airport runway area are regarded as one type and the samples from the non-airport runway area, such as forests, roads, oceans, and buildings, are regarded as another type. After constructing the sample sets, the LBP operator is applied to these sets to obtain the LBP features. Then, LBP feature histograms are counted to a form feature vector that is sent to the SVM for training. In the testing part, the suspected airport runway area, referred to as the ROI, is first segmented from the image. Then, LBP features are extracted from the ROI and sent to the trained SVM classifier for classification to obtain the initial detection result. Further identification processing is required to generate the final detection result. In extracting the suspected airport runway area, the polarimetric scattering entropy and power value of PolSAR images are calculated separately to construct a new scattering feature, namely, the alienated scattering power. The alienated scattering value of the suspected airport runway area is less than the average alienated scattering power of the entire image. Thus, extraction of the suspected airport runway area is achieved through this characteristic by setting a threshold. In the detection part, the images are classified using LBP features and SVM. First, LBP features are extracted by sliding an n×n window in the power image of the ROI. Then, the extracted features are translated into histograms to generate feature vectors, which are sent to the trained SVM classifier for classification. The classification results are represented as a binary image in which the airport runway area is denoted as "1" and other areas are denoted as "0". In the final identification process of the airport runway area, the binary image is masked to obtain the mask map, and operation is performed between the mask map and the extracted suspected runway area. The number of changed pixels in the suspected runway area is calculated. If the number of changed pixels is less than 50% of the area, then the area is considered the final real airport runway area; otherwise, it is a non-airport runway area. Result PolSAR data collected by an uninhabited aerial vehicle SAR(UAVSAR) system are used to test the proposed method. The experimental results show that the method can detect airport runways with a complete structure and clear edges, and it has low false alarm and missed alarm rates. Conclusion Compared with existing methods, the method proposed in this study can more effectively detect airport runway areas and exhibits better detection effect and lower computation cost.