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SAR Image Land Cover Datasets for Classification Benchmarking of Temporal Changes

Dumitru, Corneliu Octavian and Schwarz, Gottfried and Datcu, Mihai (2018) SAR Image Land Cover Datasets for Classification Benchmarking of Temporal Changes. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 11 (5), pp. 1571-1592. IEEE - Institute of Electrical and Electronics Engineers. ISSN 1939-1404

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Official URL: https://ieeexplore.ieee.org/document/8303669/


The increased availability of high-resolution SAR (Synthetic Aperture Radar) satellite images has led to new civil applications of these data. Among them is the systematic classification of land cover types based on patterns of settlements or agriculture recorded by SAR imagers, in particular the identification and quantification of temporal changes. A systematic (re-)classification shall allow the assignment of continuously updated semantic content labels to local image patches. This necessitates a careful selection of well-defined and discernible categories being contained in the image data that have to be trained and validated. These steps are well-established for optical images, while the peculiar imaging characteristics of SAR sensors often prevent a comparable approach. Especially, the vast range of SAR imaging parameters and the diversity of local targets impact the image product characteristics and need special care. In the following, we present guidelines and practical examples of how to obtain reliable image patch classification results for time series data with a limited number of given training data. We demonstrate that one can avoid the generation of simulated training data if we decompose the classification task into physically meaningful subsets of characteristic target properties and important imaging parameters. Then the results obtained during training can serve as benchmarking figures for subsequent image classification. This holds for typical remote sensing examples such as coastal monitoring or the characterization of urban areas where we want to understand the transitions between individual land cover categories. For this purpose, a representative dataset can be obtained from the authors. A final proof of our concept is the comparison of classification results of selected target areas obtained by rather different SAR instruments. Despite the instrumental differences, the final results are surprisingly similar.

Item URL in elib:https://elib.dlr.de/120460/
Document Type:Article
Title:SAR Image Land Cover Datasets for Classification Benchmarking of Temporal Changes
AuthorsInstitution or Email of AuthorsAuthors ORCID iD
Dumitru, Corneliu OctavianCorneliu.Dumitru (at) dlr.deUNSPECIFIED
Schwarz, GottfriedGottfried.Schwarz (at) dlr.deUNSPECIFIED
Datcu, MihaiMihai.Datcu (at) dlr.deUNSPECIFIED
Date:1 May 2018
Journal or Publication Title:IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Refereed publication:Yes
Open Access:No
Gold Open Access:No
In ISI Web of Science:Yes
Page Range:pp. 1571-1592
Publisher:IEEE - Institute of Electrical and Electronics Engineers
Keywords:Satellite images, remote sensing, SAR, land cover, image classification, classification accuracy, classification maps.
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Earth Observation
DLR - Research area:Raumfahrt
DLR - Program:R EO - Erdbeobachtung
DLR - Research theme (Project):R - Vorhaben hochauflösende Fernerkundungsverfahren
Location: Oberpfaffenhofen
Institutes and Institutions:Remote Sensing Technology Institute > EO Data Science
Deposited By: Dumitru, Corneliu Octavian
Deposited On:20 Jun 2018 12:08
Last Modified:20 Jun 2018 12:08

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