目的 针对全极化、复杂场景下飞机目标检测问题，提出了区域筛选与多级特征判别相结合的PolSAR飞机目标检测方法。方法 首先对原始PolSAR图像进行滤波及去取向预处理，消除相干斑和随机取向对检测效果的影响；其次对图像进行基于功率值的区域分割，提取感兴趣区域；然后对感兴趣区域进行区域筛选，提取疑似飞机目标；最后以功率交叉熵、背景匀质性、功率差异度为特征对疑似飞机目标进行筛选，得到最终的检测结果。结果 利用美国NASA实验室的AIRSAR和UVASAR系统采集的Half-Moon-Bay、Kahului及Kona地区的实测数据进行实验，并与其他方法进行了对比。在实验1中，本文方法和对比方法均能准确检测出场景中存在的2架飞机目标，本文方法产生了7个虚警，对比方法产生了22个虚警；在实验2中，本文方法和对比方法都检测出了4架飞机目标，本文方法产生了4个虚警，对比方法产生了17个虚警；在实验3中，本文方法检测出了15架飞机中的13架，产生了6个虚警，对比方法检测出了6个待测目标，产生了17个虚警。结论 本文方法在提取出疑似飞机目标的前提下，利用多种特征对疑似飞机目标进行筛选，不需要提取出机场跑道和停机坪区域，避免了由于跑道和停机坪区域提取不完整导致的检测不准确的问题，相比于对比方法，本文方法在降低虚警和漏警的同时，提高了运算效率。

Objective Few studies on the target detection of PolSAR images that can be referred worldwide are currently available. In the full polarization SAR image of complex scenes, difficulties in aircraft target detection exhibit the following aspects: On the one hand, the background part includes not only the airport but also several areas, such as city, forest, mountain, ocean, and road, due to the different statistical characteristics of each area. Hence, fitting all the statistical characteristics of the background with a statistical feature is impossible. On the other hand, the polarization characteristics of the vehicle, ship, and some small buildings are particularly similar to those of the aircraft target in the SAR image. Therefore, aircraft targets are difficult to distinguish from other targets with one feature. Moreover, the shape of the aircraft target cannot be presented in the full polarization PolSAR image due to the resolution of the PolSAR data obtained by the imaging system, which can only be expressed by some pixel features. The existence of these problems complicates the detection of aircraft targets in PolSAR images. Result shows that the aircraft target exhibits several characteristics in the PolSAR image: 1) the scattering power of the aircraft target is generally higher than that of the surrounding background; 2) the aircraft target is often parked in fixed areas, such as airports and aprons, and the regions are characterized by homogeneity; and 3) the aircraft target is presented in the form of pixel blocks in the PolSAR image. Method An aircraft target detection algorithm combined with regional screening and multi-feature discriminant is proposed to solve the abovementioned problems and combine prior data. First, image preprocessing is performed to minimize the effect on the original PolSAR image due to the speckle and random orientation from target reflection. Second, the regions of interest of the runway, tarmac, and regions whose scattering properties are similar are extracted in accordance with the image power value. Then, suspected aircrafts are extracted by the area of connected domain. Finally, prior knowledge indicated that the power of the aircraft target is relatively large, the scattering power of the airport area is comparatively small, the aircraft target tail and wing roots demonstrate dihedral structural features, and the aircraft targets often appear in the airport or apron area. Thus, the suspected aircrafts are screened in terms of different characteristics, such as power cross entropy, background homogeneity, and power difference. Result Experiments were performed with the polarimetric synthetic aperture radar data from Half-Moon-Bay and Kahului Kona acquired by AIRSAR and UVASAR systems from NASA Laboratories in the United States. Few documents on the target detection of PolSAR image aircraft are available. Thus, the experiment was only compared with one method. The detection result of Half-Moon-Bay shows that both methods can accurately detect the aircraft targets. However, the proposed method produces seven false alarms. By contrast, the comparison method produces 22 false alarms. The Kahului result shows that the proposed and comparison methods can detect four aircraft targets. Nonetheless, the proposed method produces four false alarms. Contrarily, the comparison method produces 17 false alarms. The Kona result shows that the proposed method detects 13 out of 15 aircrafts and produces six false alarms. By contrast, the comparison method detects six out of 15 targets and produces 17 false alarms. The time spent in the experiment implies that the algorithm exhibits high computational efficiency. Conclusion The method eliminates the false targets by fusing different features to extract the suspected aircraft target and then obtain the final test results. The algorithm does not need to extract the airport runway and apron area, thereby avoiding inaccurate detection caused by the incomplete extraction of the apron area. The final test results produce few leak alarms, and some false alarms were generated, but the proposed method simultaneously produces fewer false and leak alarms than the comparison method. This method presents great improvement in operational efficiency because it only needs to traverse the extracted suspected target and not all the pixel points. However, the proposed algorithm still needs improvement. For example, the algorithm must be improved in terms of controlling false alarms. The false alarms generated in the algorithm are small buildings, vehicles, and ships because their characteristics in the PolSAR image are similar to the aircraft target. The parameter selection remains unable to achieve complete self-adaptation because the background area contains not only the airport but also other areas, such as urban, forests, mountains, and oceans. The background’s statistical properties cannot be fitted with distribution. In addition, when the two targets are close to each other, the target background area obtained by morphological expansion may include the target to be detected, which may have a certain influence on the result. Thus, these problems must be solved in future works.