论文标题
基于深卷积神经网络的沿海地区弱监督的土地分类，通过将双极化特征纳入培训数据集中
Weakly-supervised land classification for coastal zone based on deep convolutional neural networks by incorporating dual-polarimetric characteristics into training dataset
论文作者
论文摘要
在这项工作中，我们使用Spaceborne Polarimetric合成孔径（Polsar）数据集探索了DCNNS在语义分割方面的性能。使用POLSAR数据的语义分割任务可以归类为弱监督的学习，当SAR数据的特征和数据注释过程的特征被考虑到中。然后，根据空间分辨率和观看几何形状来检查太空传播和空降数据集之间的差异。在这项研究中，我们使用了Terrasar-X DLR获得的两个双偏光图像。开发了一种具有更多监督信息的培训数据集的新颖方法。具体而言，一系列典型的分类图像以及强度图像用作训练数据集。对大约20平方公里的区域进行了现场调查，以获得用于准确评估的地面真相数据集。为上述培训数据集制定了几种转移学习策略，这些策略将以可行的顺序合并。接下来，将实现三种DCNN模型，包括SEGNET，U-NET和LINKNET。
In this work we explore the performance of DCNNs on semantic segmentation using spaceborne polarimetric synthetic aperture radar (PolSAR) datasets. The semantic segmentation task using PolSAR data can be categorized as weakly supervised learning when the characteristics of SAR data and data annotating procedures are factored in. Datasets are initially analyzed for selecting feasible pre-training images. Then the differences between spaceborne and airborne datasets are examined in terms of spatial resolution and viewing geometry. In this study we used two dual-polarimetric images acquired by TerraSAR-X DLR. A novel method to produce training dataset with more supervised information is developed. Specifically, a series of typical classified images as well as intensity images serve as training datasets. A field survey is conducted for an area of about 20 square kilometers to obtain a ground truth dataset used for accuracy evaluation. Several transfer learning strategies are made for aforementioned training datasets which will be combined in a practicable order. Three DCNN models, including SegNet, U-Net, and LinkNet, are implemented next.
