当前位置: X-MOL 学术ISPRS Int. J. Geo-Inf. › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
Semantic Segmentation of Remote-Sensing Imagery Using Heterogeneous Big Data: International Society for Photogrammetry and Remote Sensing Potsdam and Cityscape Datasets
ISPRS International Journal of Geo-Information ( IF 3.4 ) Pub Date : 2020-10-12 , DOI: 10.3390/ijgi9100601
Ahram Song , Yongil Kim

Although semantic segmentation of remote-sensing (RS) images using deep-learning networks has demonstrated its effectiveness recently, compared with natural-image datasets, obtaining RS images under the same conditions to construct data labels is difficult. Indeed, small datasets limit the effective learning of deep-learning networks. To address this problem, we propose a combined U-net model that is trained using a combined weighted loss function and can handle heterogeneous datasets. The network consists of encoder and decoder blocks. The convolutional layers that form the encoder blocks are shared with the heterogeneous datasets, and the decoder blocks are assigned separate training weights. Herein, the International Society for Photogrammetry and Remote Sensing (ISPRS) Potsdam and Cityscape datasets are used as the RS and natural-image datasets, respectively. When the layers are shared, only visible bands of the ISPRS Potsdam data are used. Experimental results show that when same-sized heterogeneous datasets are used, the semantic segmentation accuracy of the Potsdam data obtained using our proposed method is lower than that obtained using only the Potsdam data (four bands) with other methods, such as SegNet, DeepLab-V3+, and the simplified version of U-net. However, the segmentation accuracy of the Potsdam images is improved when the larger Cityscape dataset is used. The combined U-net model can effectively train heterogeneous datasets and overcome the insufficient training data problem in the context of RS-image datasets. Furthermore, it is expected that the proposed method can not only be applied to segmentation tasks of aerial images but also to tasks with various purposes of using big heterogeneous datasets.

中文翻译:

使用异构大数据的遥感图像的语义分割:摄影测量与遥感国际协会波茨坦和城市景观数据集

尽管最近使用深度学习网络对遥感图像进行语义分割已经证明了其有效性,但是与自然图像数据集相比,在相同条件下获取遥感图像来构造数据标签非常困难。实际上,小型数据集限制了深度学习网络的有效学习。为了解决这个问题,我们提出了一个组合的U-net模型,该模型使用组合的加权损失函数进行训练,并且可以处理异构数据集。网络由编码器和解码器模块组成。与异构数据集共享形成编码器块的卷积层,并为解码器块分配单独的训练权重。在这里 国际摄影测量与遥感学会(ISPRS)的波茨坦和城市景观数据集分别用作RS和自然图像数据集。共享图层时,仅使用ISPRS Potsdam数据的可见波段。实验结果表明,当使用相同大小的异构数据集时,使用我们提出的方法获得的波茨坦数据的语义分割精度低于仅使用其他方法(例如SegNet,DeepLab- V3 +和U-net的简化版本。但是,使用较大的Cityscape数据集时,波茨坦图像的分割精度会提高。组合的U-net模型可以有效地训练异构数据集并克服RS图像数据集背景下训练数据不足的问题。此外,
更新日期:2020-10-12
down
wechat
bug