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Classification of Large-Scale High-Resolution SAR Images With Deep Transfer Learning

Huang, Zhongling and Dumitru, Corneliu Octavian and Pan, Zongxu and Lei, Bin and Datcu, Mihai (2020) Classification of Large-Scale High-Resolution SAR Images With Deep Transfer Learning. IEEE Geoscience and Remote Sensing Letters, 18 (1), pp. 107-111. IEEE - Institute of Electrical and Electronics Engineers. doi: 10.1109/LGRS.2020.2965558. ISSN 1545-598X.

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Official URL: https://ieeexplore.ieee.org/document/8966281


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 diversity, and label noise are addressed. In this letter, a deep transfer learning method is proposed based on a similarly annotated optical land cover data set (NWPU-RESISC45). Besides, a top-2 smooth loss function with cost-sensitive parameters was introduced to tackle the label noise and imbalanced classes’ problems. The proposed method shows high efficiency in transferring information from a similarly annotated remote sensing data set, a robust performance on highly imbalanced classes, and is alleviating the overfitting problem caused by label noise. What is more, the learned deep model has a good generalization for other SAR-specific tasks, such as MSTAR target recognition with a state-of-the-art classification accuracy of 99.46%.

Item URL in elib:https://elib.dlr.de/140440/
Document Type:Article
Title:Classification of Large-Scale High-Resolution SAR Images With Deep Transfer Learning
AuthorsInstitution or Email of AuthorsAuthor's ORCID iDORCID Put Code
Pan, ZongxuAerospace Information Research Institute, Chinese Academy of SciencesUNSPECIFIEDUNSPECIFIED
Lei, BinAerospace Information Research Institute, Chinese Academy of SciencesUNSPECIFIEDUNSPECIFIED
Journal or Publication Title:IEEE Geoscience and Remote Sensing Letters
Refereed publication:Yes
Open Access:Yes
Gold Open Access:No
In ISI Web of Science:Yes
Page Range:pp. 107-111
Publisher:IEEE - Institute of Electrical and Electronics Engineers
Keywords:High-resolution (HR) synthetic aperture radar (SAR) images, label noise, land cover classification, TerraSAR-X (TSX), transfer learning
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Earth Observation
DLR - Research area:Raumfahrt
DLR - Program:R EO - Earth Observation
DLR - Research theme (Project):R - Vorhaben hochauflösende Fernerkundungsverfahren (old)
Location: Oberpfaffenhofen
Institutes and Institutions:Remote Sensing Technology Institute > EO Data Science
Deposited By: Karmakar, Chandrabali
Deposited On:14 Jan 2021 13:25
Last Modified:24 Oct 2023 12:45

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