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Joint height estimation and semantic labeling of monocular aerial images with CNNS


Srivastava, Shivangi; Volpi, Michele; Tuia, Devis (2017). Joint height estimation and semantic labeling of monocular aerial images with CNNS. In: 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Fort Worth, 23 July 2017 - 28 July 2017, 5173-5176.

Abstract

We aim to jointly estimate height and semantically label monocular aerial images. These two tasks are traditionally addressed separately in remote sensing, despite their strong correlation. Therefore, a model learning both height and classes jointly seems advantageous and so, we propose a multitask Convolutional Neural Network (CNN) architecture with two losses: one performing semantic labeling, and another predicting normalized Digital Surface Model (nDSM) from the pixel values. Since the nDSM/height information is used only in the second loss, there is no need to have a nDSM map at test time, and the model can estimate height automatically on new images. We test our proposed method on a set of sub-decimeter resolution images and show that our model equals the performances of two separate models, but at the cost of a single one.

Abstract

We aim to jointly estimate height and semantically label monocular aerial images. These two tasks are traditionally addressed separately in remote sensing, despite their strong correlation. Therefore, a model learning both height and classes jointly seems advantageous and so, we propose a multitask Convolutional Neural Network (CNN) architecture with two losses: one performing semantic labeling, and another predicting normalized Digital Surface Model (nDSM) from the pixel values. Since the nDSM/height information is used only in the second loss, there is no need to have a nDSM map at test time, and the model can estimate height automatically on new images. We test our proposed method on a set of sub-decimeter resolution images and show that our model equals the performances of two separate models, but at the cost of a single one.

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Additional indexing

Item Type:Conference or Workshop Item (Paper), not_refereed, original work
Communities & Collections:07 Faculty of Science > Institute of Geography
Dewey Decimal Classification:910 Geography & travel
Language:English
Event End Date:28 July 2017
Deposited On:23 Mar 2018 15:08
Last Modified:20 Sep 2018 04:31
Publisher:IEEE
ISBN:978-1-5090-4951-6
OA Status:Closed
Publisher DOI:https://doi.org/10.1109/IGARSS.2017.8128167

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