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Getting pixels and regions to agree with conditional random fields


Tuia, Devis; Volpi, Michele; Moser, Gabriele (2016). Getting pixels and regions to agree with conditional random fields. In: 2016 IEEE International Geoscience & Remote Sensing Symposium, Beijing (China), 10 July 2016 - 15 July 2016, 3290-3293.

Abstract

Land cover / land use classification of remotely sensed images is inherently geographical. The use of spatial information, accounting for neighborhood relationship and spatial smoothness of geographical objects, made its proofs in countless occasions and, especially when considering very high resolution images, methods ignoring spatial context do not perform well. In this paper, we propose a hybrid dual-layer conditional random field model that enforces spatial smoothness and consistency between the pixel and region-based maps. We formulate these intuitions as a standard energy minimization problem, and we show that finding a joint solution over both output spaces leads to strong improvements in the numerical and visual senses.

Abstract

Land cover / land use classification of remotely sensed images is inherently geographical. The use of spatial information, accounting for neighborhood relationship and spatial smoothness of geographical objects, made its proofs in countless occasions and, especially when considering very high resolution images, methods ignoring spatial context do not perform well. In this paper, we propose a hybrid dual-layer conditional random field model that enforces spatial smoothness and consistency between the pixel and region-based maps. We formulate these intuitions as a standard energy minimization problem, and we show that finding a joint solution over both output spaces leads to strong improvements in the numerical and visual senses.

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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:24 Jan 2017 16:15
Last Modified:02 Feb 2018 11:42
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
OA Status:Closed
Publisher DOI:https://doi.org/10.1109/IGARSS.2016.7729851

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