Staff Publications

Staff Publications

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    '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.

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Record number 545328
Title Improving Maps from CNNs Trained with Sparse, Scribbled Ground Truths Using Fully Connected CRFs
Author(s) Maggiolo, Luca; Marcos, Diego; Moser, Gabriele; Tuia, Devis
Source In: 2018 IEEE International Geoscience & Remote Sensing Symposium Proceedings. - IEEE Xplore - ISBN 9781538671511 - p. 2099 - 2102.
Event IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium, Valencia, 2018-07-22/2018-07-27
Department(s) Laboratory of Geo-information Science and Remote Sensing
Publication type Contribution in proceedings
Publication year 2018
Abstract Convolutional Neural Networks (CNNs) have become the new standard for semantic segmentation of very high resolution images. But as for other methods, the map accuracy depends on the quantity and quality of ground truth used to train them. Having densely annotated data, i.e. a detailed, pixel-level ground truth (GT), allows obtaining effective models, but requires high efforts in annotation. For this reason, it is more common and efficient to work with point or scribbled annotations rather than with dense ones. A CNN model trained with such incomplete ground truths tends to mischaracterize the shapes of the objects and to be inaccurate near their boundaries. We propose to use an approximation of a fully connected Conditional Random Field (CRF) to solve these issues, in which long range connections are accounted for through auxiliary nodes based on clustering of CNN activation features. Experiments on the ISPRS Vaihingen benchmark, where a CNN is trained only with a non-dense, scribbled ground truth, show that the proposed method can fill part of the performance gap with respect to models trained on the densely annotated, but unrealistic, ground truth.
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