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Object-oriented remote sensing image information extraction method based on multi-classifier combination and deep learning algorithm
Pattern Recognition Letters ( IF 5.1 ) Pub Date : 2020-09-01 , DOI: 10.1016/j.patrec.2020.08.028
Qulin Tan , Bin Guo , Jun Hu , Xiaofeng Dong , Jiping Hu

In recent years, high spatial resolution remote sensing technology has made significant progress. High-resolution remote sensing satellites provide great convenience for high-quality image acquisition. In order to adapt to changes in the appearance of the target, mainstream tracking algorithms often use pattern recognition methods to build a target appearance model with learning capabilities, and use the image frames acquired during the tracking process to update the appearance model. This paper mainly studies the object-oriented remote sensing image information extraction method based on multi-classifier combination and deep learning algorithm. In this paper, we use the splitting mechanism of the tree structure to retain the appearance model with diversity, and through the integrated learning integration strategy, the target position is collaboratively predicted. Through the comparative analysis on the OTB and VOT platforms, the algorithm works well when the requirements of the tracking standards are low (the accuracy threshold is greater than 20 pixels and the success threshold is less than 0.4 pixels). The experimental results in this paper show that compared with other advanced classification methods, the proposed method shows better generalization performance in accuracy, recall, f-measure, g-mean and AUC.



中文翻译:

基于多分类器组合和深度学习算法的面向对象遥感图像信息提取方法

近年来,高空间分辨率遥感技术取得了重大进展。高分辨率遥感卫星为高质量图像采集提供了极大的便利。为了适应目标外观的变化,主流跟踪算法经常使用模式识别方法来构建具有学习能力的目标外观模型,并使用在跟踪过程中获取的图像帧来更新外观模型。本文主要研究基于多分类器组合和深度学习算法的面向对象的遥感图像信息提取方法。在本文中,我们使用树结构的拆分机制来保留具有多样性的外观模型,并通过集成学习集成策略,共同预测目标位置。通过在OTB和VOT平台上的对比分析,该算法在跟踪标准要求较低(精度阈值大于20像素,成功阈值小于0.4像素)时效果很好。实验结果表明,与其他高级分类方法相比,该方法在准确性,查全率,f-measure,g-mean,AUC等方面具有更好的泛化性能。

更新日期:2020-09-01
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