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Improved Image Classification by Proper Patch Size Selection: TerraSAR-X vs. Sentinel-1A

Dumitru, Corneliu Octavian and Schwarz, Gottfried and Cui, Shiyong and Datcu, Mihai (2016) Improved Image Classification by Proper Patch Size Selection: TerraSAR-X vs. Sentinel-1A. In: Proceedings of IWSSIP 2016, pp. 1-4. IEEE Xplore. The 23rd International Conference on Systems, Signals and Image Processing (IWSSIP 2016), 2016-05-23 - 2016-05-25, Bratislava, Slovak Republic. doi: 10.1109/IWSSIP.2016.7502739. ISSN 978-1-4673-9554-0.

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Official URL: http://iwssip.stuba.sk/


When we perform image content classification by appending semantic labels to regularly cut image patches, we have to be sure that the selected patch size is a good choice for the images at hand. In the following, we look at SAR (Synthetic Aperture Radar) satellite images, and analyse the impact of the selected patch size on the attainable classification accuracy. For test images with precisely known ground truth, one can determine the true precision / recall performance of the applied classification method. In our case, we interactively trained a classifier system via active learning, and compared the resulting classification accuracy for high and medium resolution SAR images of different space borne instruments taken over urban areas, characterized by a high diversity of target categories. At a first glance, it turns out that the selected patch size does have a significant impact leading to a varying number of identified categories with strangely related confidence levels. A fundamental understanding of the relationships between the number of detected categories and their associated confidence levels requires detailed knowledge about SAR imaging, target characteristics, pixel size effects, radiometric image quality, the availability of appropriate semantic labels, and the selected active learning environment together with its image classification tool.

Item URL in elib:https://elib.dlr.de/104519/
Document Type:Conference or Workshop Item (Speech)
Title:Improved Image Classification by Proper Patch Size Selection: TerraSAR-X vs. Sentinel-1A
AuthorsInstitution or Email of AuthorsAuthor's ORCID iDORCID Put Code
Journal or Publication Title:Proceedings of IWSSIP 2016
Refereed publication:Yes
Open Access:No
Gold Open Access:No
In ISI Web of Science:No
Page Range:pp. 1-4
Publisher:IEEE Xplore
Keywords:SAR images; classification; Gabor features; patch selection; TerraSAR-X; Sentinel-1A
Event Title:The 23rd International Conference on Systems, Signals and Image Processing (IWSSIP 2016)
Event Location:Bratislava, Slovak Republic
Event Type:international Conference
Event Start Date:23 May 2016
Event End Date:25 May 2016
Organizer:IWSSIP Org.
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 > Photogrammetry and Image Analysis
Deposited By: Dumitru, Corneliu Octavian
Deposited On:03 Jun 2016 10:23
Last Modified:24 Apr 2024 20:09

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