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首页> 外文期刊>International journal of image and data fusion >Accurate playground localisation based on multi-feature extraction and cascade classifier in optical remote sensing images
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Accurate playground localisation based on multi-feature extraction and cascade classifier in optical remote sensing images

机译：基于多功能提取和级联分类器的准确游乐场定位，在光遥感图像中

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摘要

To address the low accuracy problem of playground detection under complex background, the accurate playground localization based on multi-feature extraction and cascade classifier is proposed in this paper. It is difficult to utilize this information to separate objects from the complex background. Therefore, we adopt multi-feature extraction method to make the playgrounds more easily to be detected. The proposed localization method is partitioned into two modules: feature extraction and classification. First, multi feature extraction method combining histogram of oriented gradients (HOG) and Haar is utilized to extract features from raw images. HOG can authentically capture the shape information, which is extracted to characterize the local region. Haar can improve the image eigenvalue calculation effectively. Afterwards, cascade classifier based on AdaBoost algorithm is adopted to classify the extracted features. Finally we conduct the experiments with our proposed methodology on a publicly accessible remote sensing images from Google Earth. The results demonstrate that the proposed framework has a better effect with achieving high levels of recall, precision and F-score compared to the state-of-the-art alternatives, without sacrificing computational soundness. What is more, the results indicate that the proposed playground 1ocalisation method has strong robustness under different complex backgrounds with high detection rate.
机译：为了解决复杂背景下的游乐场检测的低精度问题，本文提出了基于多功能提取和级联分类器的准确游乐场定位。很难利用这些信息来分离复杂背景的对象。因此，我们采用多重特征提取方法，使游乐场更容易被检测到。所提出的本地化方法被划分为两个模块：特征提取和分类。首先，利用面向梯度（HOG）和HAAR直方图的多特征提取方法用于从原始图像中提取特征。 Hog可以真实地捕获提取的形状信息以表征局部区域。 HAAR可以有效地改善图像特征值计算。然后，采用基于AdaBoost算法的级联分类器来分类提取的功能。最后，我们在来自Google地球的公开可访问的遥感图像上用我们提出的方法进行实验。结果表明，与最先进的替代方案相比，拟议的框架与实现高水平的召回，精度和F分，而不会牺牲计算声音。更重要的是，结果表明，所提出的游乐场1℃方法在具有高检测率的不同复杂背景下具有强大的鲁棒性。

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