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Semantic labeling of aerial images by learning class-specific object proposals


Volpi, Michele; Tuia, Devis (2016). Semantic labeling of aerial images by learning class-specific object proposals. In: IEEE International Geoscience & Remote Sensing Symposium IGARSS, Beijing (China), 10 July 2016 - 15 July 2016, 1556-1559.

Abstract

Land-cover and land-use semantic labeling in centimeter resolution imagery (ultra-high resolution) is mostly performed by supervised classification of informative descriptors extracted from spatially coherent but small objects (e.g. superpixels or patches). In this paper, we propose an extension of this reasoning by proposing a class-specific, multi-scale and bottom-up object proposal strategy to perform semantic labeling. Specifically, we rely on a fully trainable boundary (edge) detector, allowing us to extract class-specific object-proposals. Such proposals enable training rich appearance and object models as well as enhanced spatial reasoning. We evaluate the proposed strategy on the Vaihingen dataset with promising results.

Abstract

Land-cover and land-use semantic labeling in centimeter resolution imagery (ultra-high resolution) is mostly performed by supervised classification of informative descriptors extracted from spatially coherent but small objects (e.g. superpixels or patches). In this paper, we propose an extension of this reasoning by proposing a class-specific, multi-scale and bottom-up object proposal strategy to perform semantic labeling. Specifically, we rely on a fully trainable boundary (edge) detector, allowing us to extract class-specific object-proposals. Such proposals enable training rich appearance and object models as well as enhanced spatial reasoning. We evaluate the proposed strategy on the Vaihingen dataset with promising results.

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Additional indexing

Item Type:Conference or Workshop Item (Paper), refereed, original work
Communities & Collections:07 Faculty of Science > Institute of Geography
Dewey Decimal Classification:910 Geography & travel
Language:English
Event End Date:15 July 2016
Deposited On:07 Feb 2017 10:19
Last Modified:26 Mar 2017 05:28
Publisher:Institute of Electrical and Electronics Engineers
Series Name:IEEE International Geoscience and Remote Sensing Symposium Proceedings
ISSN:2153-6996
ISBN:978-1-5090-3332-4
Additional Information:Proceedings
Publisher DOI:https://doi.org/10.1109/IGARSS.2016.7729397

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