ID 原文 译文
21335 飞机检测是遥感图像分析领域的研究热点， Aircraft detection is a hot issue in the field of remote sensing image analysis.
21336 现有检测方法的检测流程分为多步，难以进行整体优化，并且对于飞机密集区域或背景复杂区域的检测精度较低。 There exist many problems in current detection methods, such as complex detection procedure, low accuracy in complex background and dense aircraft area.
21337 针对以上问题，该文提出一种端到端的检测方法MDSSD来提高检测精度。 To solve these problems, an end-to-end aircraft detection method namedMDSSD is proposed in this paper.
21338 该方法基于单一网络目标多尺度检测框架(SSD)，以一个密集连接卷积网络(DenseNet)作为基础网络提取特征，后面连接一个由多个卷积层构成的子网络对目标进行检测和定位。 Based on Single Shot multibox Detector (SSD), a Densely connectedconvolutional Network (DenseNet) is used as the base network to extract features for its powerful ability infeature extraction, then an extra sub-network consisting of several feature layers is appended to detect andlocate aircrafts.
21339 该方法融合了多层次特征信息，同时设计了一系列不同长宽比的候选框，以实现不同尺度飞机的检测。 In order to locate aircrafts of various scales more accurately, a series of aspect ratios of defaultboxes are set to better match aircraft shapes and combine predictions deduced from feature maps of different layers.
21340 该文的检测方法完全摒弃了候选框提取阶段，将所有检测流程整合在一个网络中，更加简洁有效。 The method is more brief and efficient than methods that require object proposals, because it eliminates proposal generation completely and encapsulates all computation in a single network.
21341 实验结果表明，在多种复杂场景的遥感图像中，该方法能够达到较高的检测精度。 Experiments demonstratethat this approach achieves better performance in many complex scenes.
21342 该文在分析由常规窄带雷达获得的直升机、螺旋桨和喷气式飞机实测回波数据特征的基础上，提出一种基于多特征联合的分类识别算法。 After analyzing the features of three measured data from the low-resolution radar system, corresponding to the helicopter, the propeller, and the turbojet, an algorithm is proposed by using multiple features to classify and recognize the aircraft targets.
21343 通过对大量实测回波数据的特征分析，提取多普勒频移、幅度相对量、时域和频域波形熵、时频特征多个具有明显区分性的特征，将其输入支撑向量机(SVM)分类器实现3类空中目标的分类。 First, multiple features are extracted, including Dopplerfrequency shift, relative magnitude, waveform entropy of time and frequency domain, and time-frequencydomain features from the measured data. Then, these features are utilized for classification purpose by means ofthe Support Vector Machine (SVM).
21344 在分类的基础上，基于回波数据的时频谱宽和对称性特征，提出一种奇数与偶数片桨叶直升机识别方法。 Finally, owing to the symmetry and the width of time-frequency distributions of the returned signals between the helicopters with odd and even blades, a method is proposed to recognize of helicopter.