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纸质出版日期： 2021-04-16 ，
录用日期： 2020-05-05
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韩萍, 万义爽, 刘亚芳, 韩宾宾. LBP特征分类的极化SAR图像机场跑道检测[J]. 中国图象图形学报, 2021,26(4):952-960.
Ping Han, Yishuang Wan, Yafang Liu, Binbin Han. Airport runway detection based on LBP feature classification in PolSAR images[J]. Journal of Image and Graphics, 2021,26(4):952-960.
目的
2
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
2
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
2
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
2
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
2
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
2
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
2
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
2
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.
极化合成孔径雷达(PolSAR)图像机场跑道检测局部二值模式(LBP)特征支持向量机(SVM)分类阈值分割
polarimetric SAR(PolSAR) imageairport runway detectionlocal binary patterns(LBP) featuresupport vector machine(SVM) classificationthreshold segmentation
Aytekin Ö, Zöngür U and Halici U. 2013. Texture-based airport runway detection. IEEE Geoscience and Remote Sensing Letters, 10(3): 471-475[DOI:10.1109/LGRS.2012.2210189]
Ajith B, Adlinge S D, Dinesh S, Rajeev U P and Padmakumar. 2019. Robust method to detect and track the runway during aircraft landing using colour segmentation and runway features//Proceedings of the 3rd International Conference on Trends in Electronics and Informatics (ICOEI). Tirunelveli, Inida: IEEE, 751-757[DOI: 10.1109/ICOEI.2019.8862529http://dx.doi.org/10.1109/ICOEI.2019.8862529]
Budak Ü, Halici U, Şengür A, Karabatak M and Xiao Y. 2016. Efficient airport detection using line segment detector and fisher vector representation. IEEE Geoscience and Remote Sensing Letters, 13(8): 1079-1083[DOI:10.1109/LGRS.2016.2565706]
Ai S F, Yan J H and Li D L. 2017. Airport runway detection algorithm in remote sensing image. Electronics Optics&Control, 24(2): 43-46
艾淑芳, 闫钧华, 李大雷. 2017. 遥感图像中的机场跑道检测算法. 电光与控制, 24(2): 43-46 [DOI:10.3969/j.issn.1671-637X.2017.02.009]
Xie K, Chen J and Yang M. 2019. Research on a method of airport runway detection algorithm//Proceedings of the 4th IEEE International Conference on Image, Vision and Computing (ICIVC). Xiamen, China: IEEE, 587-591[DOI: 10.1109/ICIVC47709.2019.8981276http://dx.doi.org/10.1109/ICIVC47709.2019.8981276]
Chen Z, Han P and Han S C. 2019. Airport runway area detection for PolSAR image based on robust fuzzy C-means clustering. Journal of Civil Aviation University of China, 37(5): 30-34
程争, 韩萍, 韩绍程. 2019. 基于RFCM的PolSAR图像机场跑道区域检测方法. 中国民航大学学报, 37(5): 30-34 [DOI:CNKI:SUN:ZGMH.0.2019-05-007]
Liang W, Zhou Z L, Chen X Y, Sheng X L and Ye X D. 2020. Research on airport runway FOD detection algorithm based on texture segmentation//Proceedings of the 4th IEEE Information Technology, Networking, Electronic and Automation Control Conference (ITNEC). Chongqing, China: IEEE: #341797728[DOI: 10.1109/ITNEC48623.2020.9085150http://dx.doi.org/10.1109/ITNEC48623.2020.9085150]
Han P, Chang L and Cheng Z. 2016. Runways detection based on h/q decomposition and iterative Bayesian classification. Systems Engineering and Electronics, 38(9): 2048-2054
韩萍, 常玲, 程争. 2016. 基于h/q分解和贝叶斯迭代分类的跑道检测算法. 系统工程与电子技术, 38(9): 2048-2054 [DOI:10.3969/j.issn.1001-506X.2016.09.12]
Han P, Cheng Z and Chang L. 2016. Automatic runway detection based on unsupervised classification in polsar image//Proceedings of 2016 Integrated Communications Navigation and Surveillance (ICNS). Herndon, USA: IEEE: 6E3-1-6E3-8[DOI: 10.1109/ICNSURV.2016.7486365http://dx.doi.org/10.1109/ICNSURV.2016.7486365]
Han P, Xu J S and Zhao A J. 2014. PolSAR image runways detection based on multi-stage classifition. Systems Engineering and Electronics, 36(5): 866-871
韩萍, 徐建飒, 赵爱军. 2014. 基于多级分类的PolSAR图像机场跑道检测. 系统工程与电子技术, 36(5): 866-871 [DOI:10.3969/j.issn.1001-506X.2014.05.09]
Jin R J, Zhou W and Yang J. 2014. Airport automatic detection in large-scale polarimetric SAR images. Journal of Tsinghua University (Science and Technology), 54(12): 1588-1593
晋瑞锦, 周伟, 杨健. 2014. 大场景下的极化SAR机场检测. 清华大学学报(自然科学版), 54(12): 1588-1593
Lu X G, Zhou B, Han P and Han B B. 2019. Aircraft detection based on multi-feature classification for PolSAR image. Journal of Signal Processing, 35(4): 563-573
卢晓光, 周波, 韩萍, 韩宾宾. 2019. 多特征分类的PolSAR图像飞机目标检测. 信号处理, 35(4): 563-573 [DOI:10.16798/j.issn.1003-0530.2019.04.006]
Ojala T, Pietikainen M and Harwood D. 1994. Performance evaluation of texture measures with classification based on Kullback discrimination of distributions//Proceedings of the 12th International Conference on Pattern Recognition. Jerusalem, Israel: IEEE: 582-585[DOI: 10.1109/ICPR.1994.576366http://dx.doi.org/10.1109/ICPR.1994.576366]
Ojala T, Pietikainen M and Maenpaa T. 2002. Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Transactions on Pattern Analysis and Machine Intelligence, 24(7): 971-987[DOI:10.1109/tpami.2002.1017623]
Qiao Y Q, Xiao J H, Huang Y H and Yin K Y. 2016. Extraction algorithm for airport region in spaceborne SAR imagery based on improved randomised hough transform. Electronic Measurement Technology, 39(2): 56-60
乔寅骐, 肖健华, 黄银和, 尹奎英. 2016. 基于改进RHT的SAR图像机场区域提取算法. 电子测量技术, 39(2): 56-60 [DOI:10.3969/j.issn.1002-7300.2016.02.013]
Xiong W, Zhong J J and Zhou Y. 2012. Automatic recognition of airfield runways based on Radon transform and hypothesis testing in SAR images//Proceedings of the 5th Global Symposium on Millimeter-Waves. Harbin, China: IEEE: 462-465[DOI: 10.1109/GSMM.2012.6314098http://dx.doi.org/10.1109/GSMM.2012.6314098]
Zhang D F, Gao N H, Wang H, Feng X H, Huo J W and Zhang J. 2019. Face recognition algorithm based on block LBP fusion feature and SVM. Transducer and Microsystem Technologies, 38(5): 154-156, 160
张敦凤, 高宁化, 王姮, 冯兴华, 霍建文, 张静. 2019. 基于分块LBP融合特征和SVM的人脸识别算法. 传感器与微系统, 38(5): 154-156, 160 [DOI:10.13873/J.1000-9787(2019)05-0154-03]
Zhang Z, Wan Y S, Han P and Cheng Z. 2019. Airport runways detection algorithm based on difference degree iteration in PolSAR image. Journal of Signal Processing, 35(6): 1041-1050
张喆, 万义爽, 韩萍, 程争. 2019. 基于差异度迭代的极化SAR图像机场跑道检测算法. 信号处理, 35(6): 1041-1050 [DOI:10.16798/j.issn.1003-0530.2019.06.015]
Sujay S N, Reddy H S M and Ravi J. 2017. Face recognition using extended LBP features and multilevel SVM classifier//Proceedings of International Conference on Electrical, Electronics, Communication, Computer, and Optimization Techniques (ICEECCOT). Mysuru, India, 1-4[DOI: 10.1109/ICEECCOT.2017.8284596http://dx.doi.org/10.1109/ICEECCOT.2017.8284596]
Hoang V T. 2018. Unsupervised LBP histogram selection for color texture classification via sparse representation//Proceedings of 2018 IEEE International Conference on Information Communication and Signal Processing (ICICSP). Singapore, Singapore: IEEE, 79-84[DOI: 10.1109/ICICSP.2018.8549724http://dx.doi.org/10.1109/ICICSP.2018.8549724]
Yan Y S, Shuai C and Liang G. 2019. Feature extraction method based on improved linear LBP operator//Proceedings of the 3rd IEEE Information Technology, Networking, Electronic and Automation Control Conference (ITNEC). Chengdu, China: IEEE, 1536-1540[DOI: 10.1109/ITNEC.2019.8729320http://dx.doi.org/10.1109/ITNEC.2019.8729320]
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