作者：
Huang, Zhongling;Dumitru, Corneliu Octavian;Pan, Zongxu;Lei, Bin;Datcu, Mihai*
通讯作者：
Datcu, Mihai
作者机构：
Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, China
School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing, China
Key Laboratory of Technology in Geo-Spatial Information Processing and Application System, Chinese Academy of Sciences, Beijing, China
EO Data Science Department, German Aerospace Center (DLR), Wessling, Germany
通讯机构：
German Aerosp Ctr DLR, EO Data Sci Dept, D-82234 Wessling, Germany.
语种：
英文
关键词：
Synthetic aperture radar;Radar polarimetry;Data models;Training;Task analysis;Learning systems;Remote sensing;High-resolution (HR) synthetic aperture radar (SAR) images;label noise;land cover classification;TerraSAR-X (TSX);transfer learning
期刊：
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
ISSN：
1545-598X
年：
2021
卷：
18
期：
1
页码：
107-111
基金类别：
10.13039/501100001809-National Natural Science Foundation of China (Grant Number: 61701478 and University of Chinese Academy of Sciences (UCAS) Joint Ph.D. Training Program scholarship)
摘要：
The classification of large-scale high-resolution synthetic aperture radar (SAR) land cover images acquired by satellites is a challenging task, facing several difficulties such as semantic annotation with expertise, changing data characteristics due to varying imaging parameters or regional target area differences, and complex scattering mechanisms being different from optical imaging. Given a large-scale SAR land cover data set collected from TerraSAR-X images with a hierarchical three-level annotation of 150 categories and comprising more than 100 000 patches, three main challenges in automatically interpreting SAR images of highly imbalanced classes, geographic divers...
