基于深度卷积神经网络并结合双极化特征到训练数据集的沿海地区弱监督土地分类
Weakly-supervised land classification for coastal zone based on deep convolutional neural networks by incorporating dual-polarimetric characteristics into training dataset
AI总结：
本文研究基于星载PolSAR数据的沿海地区弱监督土地分类，提出结合双极化特征构建训练数据集的新方法，并通过SegNet、U-Net和LinkNet三种DCNN模型验证了迁移学习策略的有效性。
摘要：
本文探讨了深度卷积神经网络（DCNN）在使用星载极化合成孔径雷达（PolSAR）数据集进行语义分割时的性能。当考虑SAR数据特性和数据标注过程时，使用PolSAR数据的语义分割任务可归类为弱监督学习。首先对数据集进行分析，以选择可行的预训练图像。然后从空间分辨率和观测几何角度考察了星载与机载数据集之间的差异。本研究使用了由TerraSAR-X DLR获取的两幅双极化图像。提出了一种生成具有更多监督信息的训练数据集的新方法。具体而言，一系列典型分类图像以及强度图像被用作训练数据集。对约20平方公里的区域进行了实地调查，以获得用于精度评估的地面真值数据集。针对上述训练数据集制定了若干迁移学习策略，并将以可行的顺序进行组合。随后实现了三种DCNN模型，包括SegNet、U-Net和LinkNet。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
