Staff Publications

Staff Publications

  • external user (warningwarning)
  • Log in as
  • language uk
  • About

    'Staff publications' is the digital repository of Wageningen University & Research

    'Staff publications' contains references to publications authored by Wageningen University staff from 1976 onward.

    Publications authored by the staff of the Research Institutes are available from 1995 onwards.

    Full text documents are added when available. The database is updated daily and currently holds about 240,000 items, of which 72,000 in open access.

    We have a manual that explains all the features 

Record number 563153
Title Joint height estimation and semantic labeling of monocular aerial images with CNNS
Author(s) Srivastava, Shivangi; Volpi, Michele; Tuia, Devis
Source In: 2017 IEEE International Geoscience and Remote Sensing Symposium. - Institute of Electrical and Electronics Engineers Inc. (International Geoscience and Remote Sensing Symposium (IGARSS) ) - ISBN 9781509049523 - p. 5173 - 5176.
Event 37th Annual IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2017, Fort Worth, 2017-07-23/2017-07-28
DOI https://doi.org/10.1109/IGARSS.2017.8128167
Publication type Contribution in proceedings
Publication year 2017
Keyword(s) Convolutional neural networks - Digital Surface Model - Multitask learning - Semantic labeling
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.

Comments
There are no comments yet. You can post the first one!
Post a comment
 
Please log in to use this service. Login as Wageningen University & Research user or guest user in upper right hand corner of this page.