E-Mail Alert

Add your e-mail address to receive forthcoming issues of this journal:

Journal Browser

Journal Browser

Special Issue "Advances in Object-Based Image Analysis—Linking with Computer Vision and Machine Learning"

A special issue of Remote Sensing (ISSN 2072-4292).

Deadline for manuscript submissions: closed (1 January 2017)

Special Issue Editors

Guest 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
Website1 | Website2 | E-Mail
Interests: damage assessment; vulnerability; disaster risk management; UAV; resilience; recovery; OBIA; object-oriented analysis; VGI
Guest Editor
Prof. Dr. Markus Gerke

Institute of Geodesy and Photogrammetry, Technical University of Braunschweig, Braunschweig, Germany
Website | E-Mail
Phone: +31 53 4874522
Interests: image geometry; scene understanding; UAVs; oblique airborne photogrammetry
Guest Editor
Prof. Dr. Sébastien Lefèvre

IRISA - Université Bretagne Sud, Campus de Tohannic, BP 573, 56017 Vannes Cedex France
Website | E-Mail
Phone: +33 2 97 01 72 66
Interests: image analysis and processing, multiscale representations, computer vision, pattern recognition, machine learning, data mining, remote sensing

Special Issue Information

Dear Colleagues,

During the last 10 years the GEOBIA community 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. As a consequence, hundreds of scientific publications have greatly enriched the geoinformation science domain over the past decade. Following a range of very successful biennial conferences, the GEOBIA 2016 conference took place from 14-16 September at the University of Twente in Enschede, the Netherlands. It focused on two broad issues where more progress is needed: (i) Solutions: while GEOBIA has undoubtedly advanced our understanding of a wide range of environmental processes or anthropogenic activities, many studies are marked by great technical complexity, a reliance on proprietary software, and have resulted in methods and procedures that are difficult to replicate and use for non-expert stakeholders. Especially in countries with limited resources and great environmental challenges, GEOBIA has to date only played a minor role in actual problem solving, which was addressed by the conference. GEOBIA 2016 also included a benchmarking effort, connected to an ongoing ISPRS initiative, aimed at stimulating the development of optimized, generic and transferable methods for standard GEOBIA problems. (ii) Synergies: Segmentation-based analysis is not an exclusive GEOBIA domain. On the contrary, such methodological approaches play a vital role in the biomedical and pharmaceutical communities already mentioned, but also feature very strongly in the computer vision domain. Object-based analysis is as critical to the analysis of lidar or image-derived point clouds as to the processing of satellite images. As such, those domains have a lot to share with the GEOBIA field, and vice versa. A core effort of GEOBIA 2016 was to find synergies between the disciplines that share an interest in object-based data processing.

This Special Issue will primarily feature selected papers from the GEOBIA 2016 conference. Authors wishing to have their work considered for this issue, including those not able to present at the conference, should contact the guest editors.

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

Dr. Norman Kerle
Dr. Markus Gerke
Prof. Dr. Sébastien Lefèvre
Guest Editors

Submission

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. Papers will be published continuously (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as 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 refereed through a 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 1600 CHF (Swiss Francs).

Published Papers (6 papers)

View options order results:
result details:
Displaying articles 1-6
Export citation of selected articles as:

Research

Open AccessArticle Reproducibility and Practical Adoption of GEOBIA with Open-Source Software in Docker Containers
Remote Sens. 2017, 9(3), 290; doi:10.3390/rs9030290
Received: 30 December 2016 / Revised: 22 February 2017 / Accepted: 6 March 2017 / Published: 18 March 2017
PDF Full-text (15511 KB) | HTML Full-text | XML Full-text
Abstract
Geographic Object-Based Image Analysis (GEOBIA) mostly uses proprietary software,
but the interest in Free and Open-Source Software (FOSS) for GEOBIA is growing. This interest stems not only from cost savings, but also from benefits concerning reproducibility and collaboration. Technical challenges hamper practical reproducibility,
[...] Read more.
Geographic Object-Based Image Analysis (GEOBIA) mostly uses proprietary software,
but the interest in Free and Open-Source Software (FOSS) for GEOBIA is growing. This interest stems not only from cost savings, but also from benefits concerning reproducibility and collaboration. Technical challenges hamper practical reproducibility, especially when multiple software packages are required to conduct an analysis. In this study, we use containerization to package a GEOBIA workflow in a well-defined FOSS environment. We explore the approach using two software stacks to perform an exemplary analysis detecting destruction of buildings in bi-temporal images of a conflict area. The analysis combines feature extraction techniques with segmentation and object-based analysis to detect changes using automatically-defined local reference values and to distinguish disappeared buildings from non-target structures. The resulting workflow is published as FOSS comprising both the model and data in a ready to use Docker image and a user interface for interaction with the containerized workflow. The presented solution advances GEOBIA in the following aspects: higher transparency of methodology; easier reuse and adaption of workflows; better transferability between operating systems; complete description of the software environment; and easy application of workflows by image analysis experts and non-experts. As a result, it promotes not only the reproducibility of GEOBIA, but also its practical adoption. Full article
Figures

Open AccessArticle Geometric Refinement of ALS-Data Derived Building Models Using Monoscopic Aerial Images
Remote Sens. 2017, 9(3), 282; doi:10.3390/rs9030282
Received: 28 December 2016 / Revised: 9 March 2017 / Accepted: 12 March 2017 / Published: 16 March 2017
PDF Full-text (5806 KB) | HTML Full-text | XML Full-text
Abstract
Airborne laser scanning (ALS) has proven to be a strong basis for 3D building reconstruction. While ALS data allows for a highly automated processing workflow, a major drawback is often in the point spacing. As a consequence, the precision of roof plane and
[...] Read more.
Airborne laser scanning (ALS) has proven to be a strong basis for 3D building reconstruction. While ALS data allows for a highly automated processing workflow, a major drawback is often in the point spacing. As a consequence, the precision of roof plane and ridge line parameters is usually significantly better than the precision of gutter lines. To cope with this problem, the paper presents an approach for geometric refinement of building models reconstructed from ALS data using monoscopic aerial images. The core idea of the proposed modeling method is to obtain refined roof edges by intersecting roof planes accurately and reliably extracted from 3D point clouds with viewing planes assigned with building edges detected in a high resolution aerial image. In order to minimize ambiguities that may arise during the integration of modeling cues, the ALS data is used as the master providing initial information about building shape and topology. We evaluate the performance of our algorithm by comparing the results of 3D reconstruction executed using only laser scanning data and reconstruction enhanced by image information. The assessment performed within a framework of the International Society for Photogrammetry and Remote Sensing (ISPRS) benchmark shows an increase in the final quality indicator up to 8.7%. Full article
Figures

Open AccessArticle A Classification-Segmentation Framework for the Detection of Individual Trees in Dense MMS Point Cloud Data Acquired in Urban Areas
Remote Sens. 2017, 9(3), 277; doi:10.3390/rs9030277
Received: 29 December 2016 / Revised: 6 March 2017 / Accepted: 9 March 2017 / Published: 16 March 2017
PDF Full-text (4871 KB) | HTML Full-text | XML Full-text
Abstract
In this paper, we present a novel framework for detecting individual trees in densely sampled 3D point cloud data acquired in urban areas. Given a 3D point cloud, the objective is to assign point-wise labels that are both class-aware and instance-aware, a task
[...] Read more.
In this paper, we present a novel framework for detecting individual trees in densely sampled 3D point cloud data acquired in urban areas. Given a 3D point cloud, the objective is to assign point-wise labels that are both class-aware and instance-aware, a task that is known as instance-level segmentation. To achieve this, our framework addresses two successive steps. The first step of our framework is given by the use of geometric features for a binary point-wise semantic classification with the objective of assigning semantic class labels to irregularly distributed 3D points, whereby the labels are defined as “tree points” and “other points”. The second step of our framework is given by a semantic segmentation with the objective of separating individual trees within the “tree points”. This is achieved by applying an efficient adaptation of the mean shift algorithm and a subsequent segment-based shape analysis relying on semantic rules to only retain plausible tree segments. We demonstrate the performance of our framework on a publicly available benchmark dataset, which has been acquired with a mobile mapping system in the city of Delft in the Netherlands. This dataset contains 10.13 M labeled 3D points among which 17.6 % are labeled as “tree points”. The derived results clearly reveal a semantic classification of high accuracy (up to 90.77 %) and an instance-level segmentation of high plausibility, while the simplicity, applicability and efficiency of the involved methods even allow applying the complete framework on a standard laptop computer with a reasonable processing time (less than 2.5 h). Full article
Figures

Open AccessArticle Fast Segmentation and Classification of Very High Resolution Remote Sensing Data Using SLIC Superpixels
Remote Sens. 2017, 9(3), 243; doi:10.3390/rs9030243
Received: 31 December 2016 / Revised: 21 February 2017 / Accepted: 2 March 2017 / Published: 5 March 2017
PDF Full-text (32422 KB) | HTML Full-text | XML Full-text
Abstract
Speed and accuracy are important factors when dealing with time-constraint events for disaster, risk, and crisis-management support. Object-based image analysis can be a time consuming task in extracting information from large images because most of the segmentation algorithms use the pixel-grid for the
[...] Read more.
Speed and accuracy are important factors when dealing with time-constraint events for disaster, risk, and crisis-management support. Object-based image analysis can be a time consuming task in extracting information from large images because most of the segmentation algorithms use the pixel-grid for the initial object representation. It would be more natural and efficient to work with perceptually meaningful entities that are derived from pixels using a low-level grouping process (superpixels). Firstly, we tested a new workflow for image segmentation of remote sensing data, starting the multiresolution segmentation (MRS, using ESP2 tool) from the superpixel level and aiming at reducing the amount of time needed to automatically partition relatively large datasets of very high resolution remote sensing data. Secondly, we examined whether a Random Forest classification based on an oversegmentation produced by a Simple Linear Iterative Clustering (SLIC) superpixel algorithm performs similarly with reference to a traditional object-based classification regarding accuracy. Tests were applied on QuickBird and WorldView-2 data with different extents, scene content complexities, and number of bands to assess how the computational time and classification accuracy are affected by these factors. The proposed segmentation approach is compared with the traditional one, starting the MRS from the pixel level, regarding geometric accuracy of the objects and the computational time. The computational time was reduced in all cases, the biggest improvement being from 5 h 35 min to 13 min, for a WorldView-2 scene with eight bands and an extent of 12.2 million pixels, while the geometric accuracy is kept similar or slightly better. SLIC superpixel-based classification had similar or better overall accuracy values when compared to MRS-based classification, but the results were obtained in a fast manner and avoiding the parameterization of the MRS. These two approaches have the potential to enhance the automation of big remote sensing data analysis and processing, especially when time is an important constraint. Full article
Figures

Open AccessArticle Generating Topographic Map Data from Classification Results
Remote Sens. 2017, 9(3), 224; doi:10.3390/rs9030224
Received: 30 December 2016 / Accepted: 25 February 2017 / Published: 2 March 2017
PDF Full-text (5481 KB) | HTML Full-text | XML Full-text
Abstract
The use of classification results as topographic map data requires cartographic enhancement and checking of the geometric accuracy. Urban areas are of special interest. The conversion of the classification result into topographic map data of high thematic and geometric quality is subject of
[...] Read more.
The use of classification results as topographic map data requires cartographic enhancement and checking of the geometric accuracy. Urban areas are of special interest. The conversion of the classification result into topographic map data of high thematic and geometric quality is subject of this contribution. After reviewing the existing literature on this topic, a methodology is presented. The extraction of point clouds belonging to line segments is solved by the Hough transform. The mathematics for deriving polygons of orthogonal, parallel and general line segments by least squares adjustment is presented. A unique solution for polylines, where the Hough parameters are optimized, is also given. By means of two data sets land cover maps of six classes were produced and then enhanced by the proposed method. The classification used the decision tree method applying a variety of attributes including object heights derived from imagery. The cartographic enhancement is carried out with two different levels of quality. The user’s accuracies for the classes “impervious surface” and “building” were above 85% in the “Level 1” map of Example 1. The geometric accuracy of building corners at the “Level 2” maps is assessed by means of reference data derived from ortho-images. The obtained root mean square errors (RMSE) of the generated coordinates (x, y) were RMSEx = 1.2 m and RMSEy = 0.7 m (Example 1) and RMSEx = 0.8 m and RMSEy = 1.0 m (Example 2) using 31 and 62 check points, respectively. All processing for Level 1 (raster data) could be carried out with a high degree of automation. Level 2 maps (vector data) were compiled for the classes “building” and “road and parking lot”. For urban areas with numerous classes and of large size, universal algorithms are necessary to produce vector data fully automatically. The recent progress in sensors and machine learning methods will support the generation of topographic map data of high thematic and geometric accuracy. Full article
Figures

Open AccessArticle Scalable Bag of Subpaths Kernel for Learning on Hierarchical Image Representations and Multi-Source Remote Sensing Data Classification
Remote Sens. 2017, 9(3), 196; doi:10.3390/rs9030196
Received: 31 December 2016 / Revised: 9 February 2017 / Accepted: 15 February 2017 / Published: 24 February 2017
PDF Full-text (9572 KB) | HTML Full-text | XML Full-text
Abstract
The geographic object-based image analysis (GEOBIA) framework has gained increasing interest for the last decade. One of its key advantages is the hierarchical representation of an image, where object topological features can be extracted and modeled in the form of structured data. We
[...] Read more.
The geographic object-based image analysis (GEOBIA) framework has gained increasing interest for the last decade. One of its key advantages is the hierarchical representation of an image, where object topological features can be extracted and modeled in the form of structured data. We thus propose to use a structured kernel relying on the concept of bag of subpaths to directly cope with such features. The kernel can be approximated using random Fourier features, allowing it to be applied on a large structure size (the number of objects in the structured data) and large volumes of data (the number of pixels or regions for training). With the so-called scalable bag of subpaths kernel (SBoSK), we also introduce a novel multi-source classification approach performing machine learning directly on a hierarchical image representation built from two images at different resolutions under the GEOBIA framework. Experiments run on an urban classification task show that the proposed approach run on a single image improves the classification overall accuracy in comparison with conventional approaches from 2% to 5% depending on the training set size and that fusing two images allows a supplementary 4% accuracy gain. Additional evaluations on public available large-scale datasets illustrate further the potential of SBoSK, with overall accuracy rates improvement ranging from 1% to 11% depending on the considered setup. Full article
Figures

Journal Contact

MDPI AG
Remote Sensing Editorial Office
St. Alban-Anlage 66, 4052 Basel, Switzerland
E-Mail: 
Tel. +41 61 683 77 34
Fax: +41 61 302 89 18
Editorial Board
Contact Details Submit to Remote Sensing Edit a special issue Review for Remote Sensing
Back to Top