Ensemble of Training Models for Road and Building Segmentation
Ensemble of Training Models for Road and Building Segmentation
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DOI：
10.1109/dicta47822.2019.8945903
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发表时间：
2019-12
期刊：
影响因子：
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通讯作者：
Ryosuke Kamiya;Kyoya Sawada;K. Hotta
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文献类型：
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作者：
Ryosuke Kamiya;Kyoya Sawada;K. Hotta
在本文中，我们提出了一种卫星图像中的目标分割方法的集成模型，通过训练过程中获得。为了提高识别精度，使用不同随机种子获得的模型的集成。在这里，我们关注通过训练过程获得的模型的集成。在模型集成中，要把不同观点的模型进行集成。由于边界等概率较低的像素往往通过训练过程进行更新，因此训练过程中每个模型对边界区域的概率不同，这些概率图的集成对于提高分割精度是有效的。建筑物和道路分割的实验证明了训练模型集成的有效性。我们提出的方法提高了约4%，与验证选择的最佳模型相比。我们的方法也取得了更好的准确性比标准的合奏模型。
In this paper, we propose an object segmentation method in satellite images by the ensemble of models obtained through training process. To improve recognition accuracy, the ensemble of models obtained by different random seeds is used. Here we pay attention to the ensemble of models obtained through training process. In model ensemble, we should integrate the models with different opinions. Since the pixels with low probability such as boundary are often updated through training process, each model in training process has different probability for boundary regions, and the ensemble of those probability maps is effective for improving segmentation accuracy. Effectiveness of the ensemble of training models is demonstrated by experiments on building and road segmentation. Our proposed method improved approximately 4% in comparison with the best model selected by validation. Our method also achieved better accuracy than the standard ensemble of models.