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 547536
Title Injecting spatial priors in Earth observation with machine vision
Author(s) Gonzalez, Diego
Source Wageningen University. Promotor(en): D. Tuia. - Wageningen : Wageningen University - ISBN 9789463435604 - 130
Department(s) Laboratory of Geo-information Science and Remote Sensing
PE&RC
Publication type Dissertation, internally prepared
Publication year 2019
Keyword(s) cum laude
Abstract

Remote Sensing (RS) imagery with submeter resolution is becoming ubiquitous. Be it from satellites, aerial campaigns or Unmanned Aerial Vehicles, this spatial resolution allows to recognize individual objects and their parts from above.

This has driven, during the last few years, a big interest in the RS community on Computer Vision (CV) methods developed for the automated understanding of natural images.

A central element to the success of \CV is the use of prior information about the image generation process and the objects these images contain: neighboring pixels are likely to belong to the same object; objects of the same nature tend to look similar with independence of their location in the image; certain objects tend to occur in particular geometric configurations; etc.

When using RS imagery, additional prior knowledge exists on how the images were formed, since we know roughly the geographical location of the objects, the geospatial prior, and the direction they were observed from, the overhead-view prior.

This thesis explores ways of encoding these priors in CV models to improve their performance on RS imagery, with a focus on land-cover and land-use mapping.

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