论文标题
农村和城市地区土地覆盖细分市场的最低基于班级混乱的转移
Minimum Class Confusion based Transfer for Land Cover Segmentation in Rural and Urban Regions
论文作者
论文摘要
转移学习方法被广泛用于卫星图像分割问题，并改善经典监督学习方法的性能。在这项研究中，我们提出了一种语义分割方法，该方法使我们能够使用转移学习方法制作土地覆盖图。我们比较了在低分辨率图像中训练的模型与目标区域或变焦级别的数据不足。为了提高目标数据的性能，我们尝试了经过无监督，半监督和监督的转移学习方法训练的模型，包括来自公共数据集中的卫星图像和其他未标记的来源。根据实验结果，转移学习改善了农村地区的细分性能3.4％MIOU（平均交叉点）和城市地区的12.9％MIOU。我们观察到，当两个数据集共享一个可比的缩放水平并标有相同的规则时，转移学习更加有效。否则，通过使用未标记的数据，半监督学习更有效。此外，实验表明，HRNET在多级分段中的建筑分割方法优于建筑分割方法。
Transfer Learning methods are widely used in satellite image segmentation problems and improve performance upon classical supervised learning methods. In this study, we present a semantic segmentation method that allows us to make land cover maps by using transfer learning methods. We compare models trained in low-resolution images with insufficient data for the targeted region or zoom level. In order to boost performance on target data we experiment with models trained with unsupervised, semi-supervised and supervised transfer learning approaches, including satellite images from public datasets and other unlabeled sources. According to experimental results, transfer learning improves segmentation performance 3.4% MIoU (Mean Intersection over Union) in rural regions and 12.9% MIoU in urban regions. We observed that transfer learning is more effective when two datasets share a comparable zoom level and are labeled with identical rules; otherwise, semi-supervised learning is more effective by using the data as unlabeled. In addition, experiments showed that HRNet outperformed building segmentation approaches in multi-class segmentation.
