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首页> 外文期刊>IEEE Geoscience and Remote Sensing Letters >Oil Tank Extraction Based on Joint-Spatial Saliency Analysis for Multiple SAR Images
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Oil Tank Extraction Based on Joint-Spatial Saliency Analysis for Multiple SAR Images

机译：基于多个SAR图像的关节空间显着性分析的油箱提取

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摘要

The lack of true color and the presence of background clutter reduce the accuracy rate of the saliency analysis for oil tank extraction in the synthetic aperture radar (SAR) images. This letter proposes a specially designed unsupervised method to extract oil tanks using the joint-spatial saliency analysis (JSSA) for multiple SAR images. First, the intrasaliency analysis is established on a saliency driven iterative clustering. This considers the spatial intensity and texture feature within a single image and suppresses most backgrounds. Second, the cospatial residual and the local grayscale statistics are considered independently in the intersaliency analysis. The common salient parts among the input series are extracted and used to overcome the problem of the lack of true color. Third, to make the fusion of the two kinds of saliency maps, the low-rank matrix is introduced. The weights of different maps are calculated and the saliency cues are integrated efficiently. Finally, after the statistics of the highlight points within the candidates, the location of the oil tanks is refined. The experiments show the superiority of the proposed method in both the pixel level and the geometric segmentation. The result of the JSSA model appears to improve the accuracy with fewer missing objects compared with the competing algorithms.
机译：缺乏真正的颜色和背景杂波的存在降低了合成孔径雷达（SAR）图像中的油箱萃取显着性分析的精度率。这封信提出了一种专门设计的无监督方法，用于使用多个SAR图像的关节空间显着性分析（JSSA）提取油箱。首先，在显着的驱动迭代聚类上建立累积性分析。这考虑了单个图像中的空间强度和纹理功能，并抑制大多数背景。其次，在跨性分析中独立地考虑了Coospatial残差和局部灰度统计。提取输入系列中的常见突出部分，并用于克服缺乏真实颜色的问题。第三，要使两种显着性图的融合，介绍了低秩矩阵。计算不同地图的权重，并且显着性提示有效整合。最后，在候选人内突出显示的突出点统计之后，精制了油箱的位置。该实验表明了像素水平和几何分割中所提出的方法的优越性。与竞争算法相比，JSSA模型的结果似乎提高了缺失对象的准确性。

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