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Topical Collection "Geographic Object-Based Image Analysis (GEOBIA)"

Editor

Collection Editor
Prof. Dr. Norman Kerle

Department of Earth Systems Analysis (ESA), Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente, P.O. Box 6, Hengelosestraat 99, 7500 AA Enschede, Netherlands
Website 1 | Website 2 | E-Mail
Interests: disaster risk management; damage assessment; vulnerability; UAV; resilience; recovery; OBIA; object-oriented analysis; VGI

Topical Collection Information

Dear Colleagues,

During the last decade, Geographic Object-Based Image Analysis (GEOBIA)has grown from a niche discipline to a recognized and vibrant branch of geoinformation science, and methods developed by the growing community have helped to tackle problems in virtually all domains where geographic data are used. The growing importance of image processing, be it of traditional airborne or satellite data, or complex hyperspectral data stacks, videos, or image data used by other communities, has resulted in a multitude of methodological approaches. Object-based approaches have turned out to be an excellent way to incorporate process and feature knowledge, in addition to providing an effective way of dealing with multi-scale data. Remote Sensing has already published three Special Issues on the theme of GEOBIA, the most recent one publishing the most interesting outcomes of the GEOBIA 2016 conference that took place in 2016 at the University of Twente in Enschede, the Netherlands. The 2016 conference focused on specific two issues: (i) solutions, and (ii) synergies. The theme highlighted both the need for more operational OBIA methodologies that can help solve current societal problems, and the potential gains of merging traditional OBIA with developments in related domains, such as photogrammetry, computer vision and machine learning.

The Special Issue for the GEOBIA 2016 conference already comprises 12 excellent papers. Given the continued relevance of the topic, we invite further submissions for an ongoing topical GEOBIA collection.

Authors are required to check and follow the specific Instructions to Authors, https://www.mdpi.com/journal/remotesensing/instructions.

Prof. Dr. Norman Kerle
Collection Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the collection website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Operationalization of OBIA solutions
  • Transferability of solutions to other datasets, datatypes and data of different quality
  • Automatic determination of segmentation and classification parameters and thresholds
  • Objective success scoring of OBIA solutions (e.g., via the Benchmarking exercise)
  • Open source solutions
  • Big data
  • Machine learning methods in OBIA
  • Segmentation-based point-cloud analysis
  • Processing of UAV data (very high resolution, oblique)
  • Developments in GIS-based OBIA

Related Special Issues

Published Papers (1 paper)

2018

Open AccessArticle Identifying Generalizable Image Segmentation Parameters for Urban Land Cover Mapping through Meta-Analysis and Regression Tree Modeling
Remote Sens. 2018, 10(1), 73; https://doi.org/10.3390/rs10010073
Received: 10 November 2017 / Revised: 20 December 2017 / Accepted: 3 January 2018 / Published: 6 January 2018
Cited by 2 | PDF Full-text (45881 KB) | HTML Full-text | XML Full-text
Abstract
The advent of very high resolution (VHR) satellite imagery and the development of Geographic Object-Based Image Analysis (GEOBIA) have led to many new opportunities for fine-scale land cover mapping, especially in urban areas. Image segmentation is an important step in the GEOBIA framework,
[...] Read more.
The advent of very high resolution (VHR) satellite imagery and the development of Geographic Object-Based Image Analysis (GEOBIA) have led to many new opportunities for fine-scale land cover mapping, especially in urban areas. Image segmentation is an important step in the GEOBIA framework, so great time/effort is often spent to ensure that computer-generated image segments closely match real-world objects of interest. In the remote sensing community, segmentation is frequently performed using the multiresolution segmentation (MRS) algorithm, which is tuned through three user-defined parameters (the scale, shape/color, and compactness/smoothness parameters). The scale parameter (SP) is the most important parameter and governs the average size of generated image segments. Existing automatic methods to determine suitable SPs for segmentation are scene-specific and often computationally intensive, so an approach to estimating appropriate SPs that is generalizable (i.e., not scene-specific) could speed up the GEOBIA workflow considerably. In this study, we attempted to identify generalizable SPs for five common urban land cover types (buildings, vegetation, roads, bare soil, and water) through meta-analysis and nonlinear regression tree (RT) modeling. First, we performed a literature search of recent studies that employed GEOBIA for urban land cover mapping and extracted the MRS parameters used, the image properties (i.e., spatial and radiometric resolutions), and the land cover classes mapped. Using this data extracted from the literature, we constructed RT models for each land cover class to predict suitable SP values based on the: image spatial resolution, image radiometric resolution, shape/color parameter, and compactness/smoothness parameter. Based on a visual and quantitative analysis of results, we found that for all land cover classes except water, relatively accurate SPs could be identified using our RT modeling results. The main advantage of our approach over existing SP selection approaches is that our RT model results are not scene-specific, so they can be used to quickly identify suitable SPs in other VHR images. Full article
Figures

Graphical abstract

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