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Fast detection of airport runways in PolSAR images based on adaptive unsupervised classification

  • role： First author第一作者
  • Affiliation：

    Tianjin Key Laboratory for Advanced Signal Processing, Civil Aviation University of China, Tianjin 300300, China

  • Email：xglu@cauc.edu.cn
  • Introduction：卢晓光，1983年生，男，讲师，研究方向为自适应雷达信号处理，极化合成孔径雷达图像目标检测与识别、机载气象雷达信号处理。E-mail：xglu@cauc.edu.cn
LU Xiaoguang1,  
  • Affiliation：

    Middle South Regional Air Traffic Management Bureau, Hubei Branch, CAAC, Wuhan 432200, China

LIN Zeshan2,  
  • role： Corresponding author通信作者
  • Affiliation：

    Tianjin Key Laboratory for Advanced Signal Processing, Civil Aviation University of China, Tianjin 300300, China

  • Email：hanpingcauc@163.com
  • Introduction：韩萍，1966年生，女，教授，研究方向为雷达图像图像处理、模式识别。E-mail：hanpingcauc@163.com
HAN Ping1*,  
  • Affiliation：

    Tianjin Key Laboratory for Advanced Signal Processing, Civil Aviation University of China, Tianjin 300300, China

ZOU Can1

resumen

Airport detection is important for military and civilian applications. As a prominent feature of an airport, a runway is frequently used to recognize airport areas in image-processing fields. The classification method is extensively used to resolve the problem of airport detection in PolSAR images. That is, the runway area is extracted through the classification method to determine the airport area. A disadvantage in applying this method is that the number of class in the image must be manually set through the classification method. This process will decrease the robustness of the algorithm. Moreover, given that the classification is normally performed on the pixel of an image, a high computational cost is required. In this study, a fast, adaptive unsupervised classification method for runway detection is proposed. First, the coherent matrix of a PolSAR image is decomposed to construct the eigenvalue image, and then the superpixel image is obtained when the eigenvalue image is processed through the SLIC algorithm. Second, the number of categories of ground objects in the superpixel image is estimated, and the runway ROIs of the superpixel image are extracted through the spectral clustering method, which is an adaptive unsupervised classification. Finally, the prospected areas are further filtered morphologically, and the real runways are identified by combining with the structural features, such as the size, topology, and parallel characteristics of the runways. The algorithm is verified using multiple groups of real PolSAR images acquired through the UAVSAR system and compared with two representative unsupervised algorithms. Experimental results show that the proposed algorithm can detect the runway area quickly and accurately with low false alarm rate And that the runway structure is clear. These experimental results are improved with the use of the proposed algorithm. Thus, the following conclusions are drawn: The SLIC algorithm is used to form a superpixel image to compress the dimension of the original image and reduce computational cost. Simultaneously, speckle noise is suppressed slightly, This result is beneficial for the classification in the subsequent processing. The adaptive class number estimation of images with VAT and DBE algorithms increase the robustness of the proposed method. Runways are extracted effectively and accurately when the polarimetric feature of a PolSAR image is combined with the spectral clustering method. Further studies must be explored despite obtaining improved detection results through the proposed method. For example, improving the blurred part caused by a superpixel image in the detected airport area must be investigated. A probable scheme is to set the number of the superpixel of the image adaptively in accordance with the complexity of the original image contents to yield a high-quality superpixel image.

palabra clave

remote sensing;PolSAR image;airport runway area detection;superpixel image;adaptive unsupervised classification;spectral clustering

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