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Special Issue "Advances in Geographic Object-Based Image Analysis (GEOBIA)"

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

Deadline for manuscript submissions: closed (15 July 2014)

Special Issue Editors

Guest Editor
Dr. Geoffrey J. Hay

Department of Geography, University of Calgary, 2500 University Dr. N.W. Calgary, Alberta T2N 1N4, Canada
Website | E-Mail
Fax: + 403 282 6561
Guest Editor
Prof. Dr. Thomas Blaschke

Centre for Geoinformatics, Salzburg University, Hellbrunnerstrasse 34, A-5020 Salzburg, Austria
Website | E-Mail
Fax: +43 662 8044 182

Special Issue Information

Dear Colleagues,

This Special Issue of Remote Sensing focuses on innovative, novel, and leading-edge advances in Geographic Object-Based Image Analysis (GEOBIA) research, theories, methods, and applications. The Special Issue also invites discussion regarding future research trends.

GEOBIA has been described as a sub-discipline of GIScience devoted to developing automated methods of partitioning high-resolution, remote-sensing imagery into meaningful image-objects, and assessing their characteristics through scale. At its most fundamental level, GEOBIA involves image-segmentation, attribution, classification, and the ability to query and link image-objects in space and time within an Earth-centric framework. As a result, GEOBIA is increasingly recognized as an evolving integrative Geospatial paradigm that builds on concepts incorporated from a broad range of disciplines including: Remote Sensing, Geographic Information Systems (GIS), Image Processing, Knowledge Discovery in Databases (KDD), Geospatial Statistics, Cartography, Photogrammetry, Landscape Ecology, Geography, and many others.

Over the last decade, advances in GEOBIA research have led to four highly successful bi-yearly international GEOBIA conferences (Austria 2006; Canada 2008; Belgium 2010 and Brazil 2012) with a fifth scheduled for 2014 (in Greece), along with countless workshops; four related special journal issues (PERS—2010; RS—2011, JAG—2012; GSRS-IEEE—2013), thousands of peer-reviewed journal papers, and a growing number of books and university theses. There is also a Wiki with many thousands of views, numerous YouTube videos, and more than 20 FOSS (free and open-source software) and commercial software packages from leading remote sensing researchers, teams, and vendors, as well as a growing international community of sophisticated and demanding practitioners and innovators.

In order to highlight recent advances in GEOBIA from this growing community and their evolving body of knowledge, submissions are encouraged to cover a broad range of topics, which may include, but are not limited to, the following activities:

  • Algorithm development, automation, implementation, and validation
  • Challenges related to multi-sensor/data integration and calibration
  • Change detection and monitoring
  • Classification/error assessment, uncertainty, statistical analysis
  • Data processing/mining methods
  • Integrative technologies/platforms
  • Large area applications, i.e., regions, countries, continents
  • Multi-disciplinary case studies
  • Ontologies and classification
  • Paradigm development, epistemologies
  • Rule-set sharing, adaptation, integration, error-tracking, and responsibility
  • Scale issues, i.e., resampling, hierarchical analysis, MAUP
  • Training/testing sample collection and social engagement with VGIs and the GeoWeb
  • Visualization issues and applications
Dr. Geoffrey J. Hay
Prof. Dr. Thomas Blaschke
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).


Keywords

  • integration of GIS and remote sensing
  • GEOBIA
  • geographic information science
  • sustainable landscapes
  • energy landscapes
  • smart spaces
  • geospatial analysis
  • landscape ecology
  • geoinformatics

Published Papers (18 papers)

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Research

Open AccessArticle A Versatile, Production-Oriented Approach to High-Resolution Tree-Canopy Mapping in Urban and Suburban Landscapes Using GEOBIA and Data Fusion
Remote Sens. 2014, 6(12), 12837-12865; doi:10.3390/rs61212837
Received: 15 July 2014 / Revised: 8 December 2014 / Accepted: 16 December 2014 / Published: 22 December 2014
Cited by 8 | PDF Full-text (7247 KB) | HTML Full-text | XML Full-text
Abstract
The benefits of tree canopy in urban and suburban landscapes are increasingly well known: stormwater runoff control, air-pollution mitigation, temperature regulation, carbon storage, wildlife habitat, neighborhood cohesion, and other social indicators of quality of life. However, many urban areas lack high-resolution tree canopy
[...] Read more.
The benefits of tree canopy in urban and suburban landscapes are increasingly well known: stormwater runoff control, air-pollution mitigation, temperature regulation, carbon storage, wildlife habitat, neighborhood cohesion, and other social indicators of quality of life. However, many urban areas lack high-resolution tree canopy maps that document baseline conditions or inform tree-planting programs, limiting effective study and management. This paper describes a GEOBIA approach to tree-canopy mapping that relies on existing public investments in LiDAR, multispectral imagery, and thematic GIS layers, thus eliminating or reducing data acquisition costs. This versatile approach accommodates datasets of varying content and quality, first using LiDAR derivatives to identify aboveground features and then a combination of LiDAR and imagery to differentiate trees from buildings and other anthropogenic structures. Initial tree canopy objects are then refined through contextual analysis, morphological smoothing, and small-gap filling. Case studies from locations in the United States and Canada show how a GEOBIA approach incorporating data fusion and enterprise processing can be used for producing high-accuracy, high-resolution maps for large geographic extents. These maps are designed specifically for practical application by planning and regulatory end users who expect not only high accuracy but also high realism and visual coherence. Full article
(This article belongs to the Special Issue Advances in Geographic Object-Based Image Analysis (GEOBIA))
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Open AccessArticle Application of In-Segment Multiple Sampling in Object-Based Classification
Remote Sens. 2014, 6(12), 12138-12165; doi:10.3390/rs61212138
Received: 14 July 2014 / Revised: 11 November 2014 / Accepted: 27 November 2014 / Published: 5 December 2014
Cited by 1 | PDF Full-text (3793 KB) | HTML Full-text | XML Full-text
Abstract
When object-based analysis is applied to very high-resolution imagery, pixels within the segments reveal large spectral inhomogeneity; their distribution can be considered complex rather than normal. When normality is violated, the classification methods that rely on the assumption of normally distributed data are
[...] Read more.
When object-based analysis is applied to very high-resolution imagery, pixels within the segments reveal large spectral inhomogeneity; their distribution can be considered complex rather than normal. When normality is violated, the classification methods that rely on the assumption of normally distributed data are not as successful or accurate. It is hard to detect normality violations in small samples. The segmentation process produces segments that vary highly in size; samples can be very big or very small. This paper investigates whether the complexity within the segment can be addressed using multiple random sampling of segment pixels and multiple calculations of similarity measures. In order to analyze the effect sampling has on classification results, statistics and probability value equations of non-parametric two-sample Kolmogorov-Smirnov test and parametric Student’s t-test are selected as similarity measures in the classification process. The performance of both classifiers was assessed on a WorldView-2 image for four land cover classes (roads, buildings, grass and trees) and compared to two commonly used object-based classifiers—k-Nearest Neighbor (k-NN) and Support Vector Machine (SVM). Both proposed classifiers showed a slight improvement in the overall classification accuracies and produced more accurate classification maps when compared to the ground truth image. Full article
(This article belongs to the Special Issue Advances in Geographic Object-Based Image Analysis (GEOBIA))
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Open AccessArticle Object-Based Land-Cover Mapping with High Resolution Aerial Photography at a County Scale in Midwestern USA
Remote Sens. 2014, 6(11), 11372-11390; doi:10.3390/rs61111372
Received: 16 July 2014 / Revised: 15 October 2014 / Accepted: 24 October 2014 / Published: 14 November 2014
Cited by 7 | PDF Full-text (2858 KB) | HTML Full-text | XML Full-text
Abstract
There are growing demands for detailed and accurate land cover maps in land system research and planning. Macro-scale land cover maps normally cannot satisfy the studies that require detailed land cover maps at micro scales. In the meantime, applying conventional pixel-based classification methods
[...] Read more.
There are growing demands for detailed and accurate land cover maps in land system research and planning. Macro-scale land cover maps normally cannot satisfy the studies that require detailed land cover maps at micro scales. In the meantime, applying conventional pixel-based classification methods in classifying high-resolution aerial imagery is ineffective to develop high accuracy land-cover maps, especially in spectrally heterogeneous and complicated urban areas. Here we present an object-based approach that identifies land-cover types from 1-meter resolution aerial orthophotography and a 5-foot DEM. Our study area is Tippecanoe County in the State of Indiana, USA, which covers about a 1300 km2 land area. We used a countywide aerial photo mosaic and normalized digital elevation model as input datasets in this study. We utilized simple algorithms to minimize computation time while maintaining relatively high accuracy in land cover mapping at a county scale. The aerial photograph was pre-processed using principal component transformation to reduce its spectral dimensionality. Vegetation and non-vegetation were separated via masks determined by the Normalized Difference Vegetation Index. A combination of segmentation algorithms with lower calculation intensity was used to generate image objects that fulfill the characteristics selection requirements. A hierarchical image object network was formed based on the segmentation results and used to assist the image object delineation at different spatial scales. Finally, expert knowledge regarding spectral, contextual, and geometrical aspects was employed in image object identification. The resultant land cover map developed with this object-based image analysis has more information classes and higher accuracy than that derived with pixel-based classification methods. Full article
(This article belongs to the Special Issue Advances in Geographic Object-Based Image Analysis (GEOBIA))
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Open AccessArticle Supporting Urban Energy Efficiency with Volunteered Roof Information and the Google Maps API
Remote Sens. 2014, 6(10), 9691-9711; doi:10.3390/rs6109691
Received: 4 July 2014 / Revised: 29 August 2014 / Accepted: 29 September 2014 / Published: 13 October 2014
PDF Full-text (5327 KB) | HTML Full-text | XML Full-text
Abstract
The Heat Energy Assessment Technologies (HEAT) project uses high-resolution airborne thermal imagery, Geographic Object-Based Image Analysis (GEOBIA), and a Geoweb environment to allow the residents of Calgary, Alberta, Canada to visualize the amount and location of waste heat leaving their houses, communities, and
[...] Read more.
The Heat Energy Assessment Technologies (HEAT) project uses high-resolution airborne thermal imagery, Geographic Object-Based Image Analysis (GEOBIA), and a Geoweb environment to allow the residents of Calgary, Alberta, Canada to visualize the amount and location of waste heat leaving their houses, communities, and the city. To ensure the accuracy of these measures, the correct emissivity of roof materials needs to be known. However, roof material information is not readily available in the Canadian public domain. To overcome this challenge, a unique Volunteered Geographic Information (VGI) application was developed using Google Street View that engages citizens to classify the roof materials of single dwelling residences in a simple and intuitive manner. Since data credibility, quality, and accuracy are major concerns when using VGI, a private Multiple Listing Services (MLS) dataset was used for cross-verification. From May–November 2013, 1244 volunteers from 85 cities and 14 countries classified 1815 roofs in the study area. Results show (I) a 72% match between the VGI and MLS data; and (II) in the majority of cases, roofs with greater than, or equal to five contributions have the same material defined in both datasets. Additionally, this research meets new challenges to the GEOBIA community to incorporate existing GIS vector data within an object-based workflow and engages the public to provide volunteered information for urban objects from which new geo-intelligence is created in support of urban energy efficiency. Full article
(This article belongs to the Special Issue Advances in Geographic Object-Based Image Analysis (GEOBIA))
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Open AccessArticle Transforming Image-Objects into Multiscale Fields: A GEOBIA Approach to Mitigate Urban Microclimatic Variability within H-Res Thermal Infrared Airborne Flight-Lines
Remote Sens. 2014, 6(10), 9435-9457; doi:10.3390/rs6109435
Received: 1 July 2014 / Revised: 12 September 2014 / Accepted: 17 September 2014 / Published: 1 October 2014
Cited by 2 | PDF Full-text (1836 KB) | HTML Full-text | XML Full-text
Abstract
In an effort to minimize complex urban microclimatic variability within high-resolution (H-Res) airborne thermal infrared (TIR) flight-lines, we describe the Thermal Urban Road Normalization (TURN) algorithm, which is based on the idea of pseudo invariant features. By assuming a homogeneous road temperature
[...] Read more.
In an effort to minimize complex urban microclimatic variability within high-resolution (H-Res) airborne thermal infrared (TIR) flight-lines, we describe the Thermal Urban Road Normalization (TURN) algorithm, which is based on the idea of pseudo invariant features. By assuming a homogeneous road temperature within a TIR scene, we hypothesize that any variation observed in road temperature is the effect of local microclimatic variability. To model microclimatic variability, we define a road-object class (Road), compute the within-Road temperature variability, sample it at different spatial intervals (i.e., 10, 20, 50, and 100 m) then interpolate samples over each flight-line to create an object-weighted variable temperature field (a TURN-surface). The optimal TURN-surface is then subtracted from the original TIR image, essentially creating a microclimate-free scene. Results at different sampling intervals are assessed based on their: (i) ability to visually and statistically reduce overall scene variability and (ii) computation speed. TURN is evaluated on three non-adjacent TABI-1800 flight-lines (~182 km2) that were acquired in 2012 at night over The City of Calgary, Alberta, Canada. TURN also meets a recent GEOBIA (Geospatial Object Based Image Analysis) challenge by incorporating existing GIS vector objects within the GEOBIA workflow, rather than relying exclusively on segmentation methods. Full article
(This article belongs to the Special Issue Advances in Geographic Object-Based Image Analysis (GEOBIA))
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Open AccessArticle Earth Observation-Based Dwelling Detection Approaches in a Highly Complex Refugee Camp Environment — A Comparative Study
Remote Sens. 2014, 6(10), 9277-9297; doi:10.3390/rs6109277
Received: 31 March 2014 / Revised: 27 August 2014 / Accepted: 14 September 2014 / Published: 29 September 2014
PDF Full-text (16210 KB) | HTML Full-text | XML Full-text
Abstract
For effective management of refugee camps or camps for internally displaced persons (IDPs) relief organizations need up-to-date information on the camp situation. In cases where detailed field assessments are not available, Earth observation (EO) data can provide important information to get a better
[...] Read more.
For effective management of refugee camps or camps for internally displaced persons (IDPs) relief organizations need up-to-date information on the camp situation. In cases where detailed field assessments are not available, Earth observation (EO) data can provide important information to get a better overview about the general situation on the ground. In this study, different approaches for dwelling detection were tested using the example of a highly complex camp site in Somalia. On the basis of GeoEye-1 imagery, semi-automatic object-based and manual image analysis approaches were applied, compared and evaluated regarding their analysis results (absolute numbers, population estimation, spatial pattern), statistical correlations and production time. Although even the results of the visual image interpretation vary considerably between the interpreters, there is a similar pattern resulting from all methods, which shows same tendencies for dense and sparse populated areas. The statistical analyses revealed that all approaches have problems in the more complex areas, whereas there is a higher variance in manual interpretations with increasing complexity. The application of advanced rule sets in an object-based environment allowed a more consistent feature extraction in the area under investigation that can be obtained at a fraction of the time compared to visual image interpretation if large areas have to be observed. Full article
(This article belongs to the Special Issue Advances in Geographic Object-Based Image Analysis (GEOBIA))
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Open AccessArticle Multi-Level Spatial Analysis for Change Detection of Urban Vegetation at Individual Tree Scale
Remote Sens. 2014, 6(9), 9086-9103; doi:10.3390/rs6099086
Received: 27 May 2014 / Revised: 28 August 2014 / Accepted: 10 September 2014 / Published: 23 September 2014
Cited by 5 | PDF Full-text (7583 KB) | HTML Full-text | XML Full-text
Abstract
Spurious change is a common problem in urban vegetation change detection by using multi-temporal remote sensing images of high resolution. This usually results from the false-absent and false-present vegetation patches in an obscured and/or shaded scene. The presented approach focuses on object-based change
[...] Read more.
Spurious change is a common problem in urban vegetation change detection by using multi-temporal remote sensing images of high resolution. This usually results from the false-absent and false-present vegetation patches in an obscured and/or shaded scene. The presented approach focuses on object-based change detection with joint use of spatial and spectral information, referring to it as multi-level spatial analyses. The analyses are conducted in three phases: (1) The pixel-level spatial analysis is performed by adding the density dimension into a multi-feature space for classification to indicate the spatial dependency between pixels; (2) The member-level spatial analysis is conducted by the self-adaptive morphology to readjust the incorrectly classified members according to the spatial dependency between members; (3) The object-level spatial analysis is reached by the self-adaptive morphology involved with the additional rule of sharing boundaries. Spatial analysis at this level will help detect spurious change objects according to the spatial dependency between objects. It is revealed that the error from the automatically extracted vegetation objects with the pixel- and member-level spatial analyses is no more than 2.56%, compared with 12.15% without spatial analysis. Moreover, the error from the automatically detected spurious changes with the object-level spatial analysis is no higher than 3.26% out of all the dynamic vegetation objects, meaning that the fully automatic detection of vegetation change at a joint maximum error of 5.82% can be guaranteed. Full article
(This article belongs to the Special Issue Advances in Geographic Object-Based Image Analysis (GEOBIA))
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Open AccessArticle Mapping Banana Plants from High Spatial Resolution Orthophotos to Facilitate Plant Health Assessment
Remote Sens. 2014, 6(9), 8261-8286; doi:10.3390/rs6098261
Received: 31 March 2014 / Revised: 21 August 2014 / Accepted: 22 August 2014 / Published: 2 September 2014
Cited by 2 | PDF Full-text (11857 KB) | HTML Full-text | XML Full-text
Abstract
The Banana Bunchy Top Virus (Genus: Babuvirus) reduces plant growth and prevents banana production. Because of the very large number of properties with banana plants in South East Queensland, Australia, a mapping approach was developed to delineate individual and clusters of banana
[...] Read more.
The Banana Bunchy Top Virus (Genus: Babuvirus) reduces plant growth and prevents banana production. Because of the very large number of properties with banana plants in South East Queensland, Australia, a mapping approach was developed to delineate individual and clusters of banana plants to help plant identification and enable prioritization of plant inspections for Banana Bunchy Top Virus. Due to current outbreaks in South East Queensland, there are concerns that the virus may spread to the major banana growing districts further north. The mapping approach developed was based on very high spatial resolution airborne orthophotos. Object-based image analysis was used to: (1) detect banana plants using edge and line detection approaches; (2) produce accurate and realistic outlines around classified banana plants; and (3) evaluate the mapping results. The mapping approach was developed based on 10 image tiles of 1 km × 1 km and was applied to orthophotos (3600 image tiles) from September 2011 covering the entire Sunshine Coast Region in South East Queensland. Based on field inspections of the classified maps, a user’s mapping accuracy of 88% (n = 146) was achieved. The results will facilitate the detection of banana plants and increase the inspection rate of Banana Bunchy Top Virus in the future. Full article
(This article belongs to the Special Issue Advances in Geographic Object-Based Image Analysis (GEOBIA))
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Open AccessArticle Building Change Detection from Historical Aerial Photographs Using Dense Image Matching and Object-Based Image Analysis
Remote Sens. 2014, 6(9), 8310-8336; doi:10.3390/rs6098310
Received: 9 July 2014 / Revised: 22 August 2014 / Accepted: 25 August 2014 / Published: 2 September 2014
Cited by 8 | PDF Full-text (27439 KB) | HTML Full-text | XML Full-text
Abstract
A successful application of dense image matching algorithms to historical aerial photographs would offer a great potential for detailed reconstructions of historical landscapes in three dimensions, allowing for the efficient monitoring of various landscape changes over the last 50+ years. In this paper
[...] Read more.
A successful application of dense image matching algorithms to historical aerial photographs would offer a great potential for detailed reconstructions of historical landscapes in three dimensions, allowing for the efficient monitoring of various landscape changes over the last 50+ years. In this paper we propose the combination of image-based dense DSM (digital surface model) reconstruction from historical aerial imagery with object-based image analysis for the detection of individual buildings and the subsequent analysis of settlement change. Our proposed methodology is evaluated using historical greyscale and color aerial photographs and numerous reference data sets of Andermatt, a historical town and tourism destination in the Swiss Alps. In our paper, we first investigate the DSM generation performance of different sparse and dense image matching algorithms. They demonstrate the superiority of dense matching algorithms and of the resulting historical DSMs with root mean square error values of 1–1.5 GSD (ground sampling distance) and yield point densities comparable to those of recent airborne LiDAR DSMs. In the second part, we present an object-based building detection workflow mainly based on the historical DSMs and the historical imagery itself. Additional inputs are a current digital terrain model and a cadastral building database. For the case of densely matched DSMs, the evaluation yields building detection rates of 92% for grayscale and 94% for color imagery. Full article
(This article belongs to the Special Issue Advances in Geographic Object-Based Image Analysis (GEOBIA))
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Open AccessArticle Hierarchical Segmentation Framework for Identifying Natural Vegetation: A Case Study of the Tehachapi Mountains, California
Remote Sens. 2014, 6(8), 7276-7302; doi:10.3390/rs6087276
Received: 31 January 2014 / Revised: 23 May 2014 / Accepted: 24 July 2014 / Published: 5 August 2014
Cited by 1 | PDF Full-text (44283 KB) | HTML Full-text | XML Full-text
Abstract
Two critical limitations of very high resolution imagery interpretations for time-series analysis are higher imagery variances and large data sizes. Although object-based analyses with a multi-scale framework for diverse object sizes are one potential solution, more data requirements and large amounts of testing
[...] Read more.
Two critical limitations of very high resolution imagery interpretations for time-series analysis are higher imagery variances and large data sizes. Although object-based analyses with a multi-scale framework for diverse object sizes are one potential solution, more data requirements and large amounts of testing at high costs are required. In this study, I applied a three-level hierarchical vegetation framework for reducing those costs, and a three-step procedure was used to evaluate its effects on a digital orthophoto quadrangles with 1 m spatial resolution. Step one and step two were for image segmentation optimized for delineation of tree density, which involved global Otsu’s method followed by the random walker algorithm. Step three was for detailed species delineations, which were derived from multiresolution segmentation, in two test areas. Step one and step two were able to delineating tree density segments and label species association robustly, compared to previous hierarchical frameworks. However, step three was limited by less image information to produce detailed, reasonable image objects with optimal scale parameters for species labeling. This hierarchical vegetation framework has potential to develop baseline data for evaluating climate change impacts on vegetation at lower cost using widely available data and a personal laptop. Full article
(This article belongs to the Special Issue Advances in Geographic Object-Based Image Analysis (GEOBIA))
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Open AccessArticle Efficient Learning of Spatial Patterns with Multi-Scale Conditional Random Fields for Region-Based Classification
Remote Sens. 2014, 6(8), 6727-6764; doi:10.3390/rs6086727
Received: 19 March 2014 / Revised: 25 June 2014 / Accepted: 30 June 2014 / Published: 24 July 2014
PDF Full-text (6234 KB) | HTML Full-text | XML Full-text
Abstract
Automatic image classification is of major importance for a wide range of applications and is supported by a complex process that usually requires the identification of individual regions and spatial patterns (contextual information) among neighboring regions within images. Hierarchical conditional random fields (CRF)
[...] Read more.
Automatic image classification is of major importance for a wide range of applications and is supported by a complex process that usually requires the identification of individual regions and spatial patterns (contextual information) among neighboring regions within images. Hierarchical conditional random fields (CRF) consider both multi-scale and contextual information in a unified discriminative probabilistic framework, yet they suffer from two main drawbacks. On the one hand, their current classification performance still leaves space for improvement, mostly due to the use of very simple or inappropriate pairwise energy expressions to model complex spatial patterns; on the other hand, their training remains complex, particularly for multi-class problems. In this work, we investigated alternative pairwise energy expressions to better account for class transitions and developed an efficient parameters learning strategy for the resultant expression. We propose: (i) a multi-scale CRF model with novel energies that involves information related to the multi-scale image structure; and (ii) an efficient maximum margin parameters learning procedure where the complex learning problem is decomposed into simpler individual multi-class sub-problems. During experiments conducted on several well-known satellite image data sets, the suggested multi-scale CRF exhibited between a 1% and 15% accuracy improvement compared to other works. We also found that, on different multi-scale decompositions, the total number of regions and their average size have a direct impact on the classification results. Full article
(This article belongs to the Special Issue Advances in Geographic Object-Based Image Analysis (GEOBIA))
Open AccessArticle Geographic Object-Based Image Analysis Using Optical Satellite Imagery and GIS Data for the Detection of Mining Sites in the Democratic Republic of the Congo
Remote Sens. 2014, 6(7), 6636-6661; doi:10.3390/rs6076636
Received: 28 March 2014 / Revised: 24 June 2014 / Accepted: 27 June 2014 / Published: 21 July 2014
PDF Full-text (11793 KB) | HTML Full-text | XML Full-text
Abstract
Earth observation is an important source of information in areas that are too remote, too insecure or even both for traditional field surveys. A multi-scale analysis approach is developed to monitor the Kivu provinces in the Democratic Republic of the Congo (DRC) to
[...] Read more.
Earth observation is an important source of information in areas that are too remote, too insecure or even both for traditional field surveys. A multi-scale analysis approach is developed to monitor the Kivu provinces in the Democratic Republic of the Congo (DRC) to identify hot spots of mining activities and provide reliable information about the situation in and around two selected mining sites, Mumba-Bibatama and Bisie. The first is the test case for the approach and the detection of unknown mining sites, whereas the second acts as reference case since it is the largest and most well-known location for cassiterite extraction in eastern Congo. Thus it plays a key-role within the context of the conflicts in this region. Detailed multi-temporal analyses of very high-resolution (VHR) satellite data demonstrates the capabilities of Geographic Object-Based Image Analysis (GEOBIA) techniques for providing information about the situation during a mining ban announced by the Congolese President between September 2010 and March 2011. Although the opening of new surface patches can serve as an indication for activities in the area, the pure change between the two satellite images does not in itself produce confirming evidence. However, in combination with observations on the ground, it becomes evident that mining activities continued in Bisie during the ban, even though the production volume went down considerably. Full article
(This article belongs to the Special Issue Advances in Geographic Object-Based Image Analysis (GEOBIA))
Open AccessArticle Land-Use Mapping in a Mixed Urban-Agricultural Arid Landscape Using Object-Based Image Analysis: A Case Study from Maricopa, Arizona
Remote Sens. 2014, 6(7), 6089-6110; doi:10.3390/rs6076089
Received: 4 March 2014 / Revised: 28 May 2014 / Accepted: 19 June 2014 / Published: 30 June 2014
Cited by 6 | PDF Full-text (3804 KB) | HTML Full-text | XML Full-text
Abstract
Land-use mapping is critical for global change research. In Central Arizona, U.S.A., the spatial distribution of land use is important for sustainable land management decisions. The objective of this study was to create a land-use map that serves as a model for the
[...] Read more.
Land-use mapping is critical for global change research. In Central Arizona, U.S.A., the spatial distribution of land use is important for sustainable land management decisions. The objective of this study was to create a land-use map that serves as a model for the city of Maricopa, an expanding urban region in the Sun Corridor of Arizona. We use object-based image analysis to map six land-use types from ASTER imagery, and then compare this with two per-pixel classifications. Our results show that a single segmentation, combined with intermediary classifications and merging, morphing, and growing image-objects, can lead to an accurate land-use map that is capable of utilizing both spatial and spectral information. We also employ a moving-window diversity assessment to help with analysis and improve post-classification modifications. Full article
(This article belongs to the Special Issue Advances in Geographic Object-Based Image Analysis (GEOBIA))
Open AccessArticle A Python-Based Open Source System for Geographic Object-Based Image Analysis (GEOBIA) Utilizing Raster Attribute Tables
Remote Sens. 2014, 6(7), 6111-6135; doi:10.3390/rs6076111
Received: 31 March 2014 / Revised: 5 June 2014 / Accepted: 5 June 2014 / Published: 30 June 2014
Cited by 9 | PDF Full-text (16315 KB) | HTML Full-text | XML Full-text
Abstract
A modular system for performing Geographic Object-Based Image Analysis (GEOBIA), using entirely open source (General Public License compatible) software, is presented based around representing objects as raster clumps and storing attributes as a raster attribute table (RAT). The system utilizes a number of
[...] Read more.
A modular system for performing Geographic Object-Based Image Analysis (GEOBIA), using entirely open source (General Public License compatible) software, is presented based around representing objects as raster clumps and storing attributes as a raster attribute table (RAT). The system utilizes a number of libraries, developed by the authors: The Remote Sensing and GIS Library (RSGISLib), the Raster I/O Simplification (RIOS) Python Library, the KEA image format and TuiView image viewer. All libraries are accessed through Python, providing a common interface on which to build processing chains. Three examples are presented, to demonstrate the capabilities of the system: (1) classification of mangrove extent and change in French Guiana; (2) a generic scheme for the classification of the UN-FAO land cover classification system (LCCS) and their subsequent translation to habitat categories; and (3) a national-scale segmentation for Australia. The system presented provides similar functionality to existing GEOBIA packages, but is more flexible, due to its modular environment, capable of handling complex classification processes and applying them to larger datasets. Full article
(This article belongs to the Special Issue Advances in Geographic Object-Based Image Analysis (GEOBIA))
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Open AccessArticle Change Detection Algorithm for the Production of Land Cover Change Maps over the European Union Countries
Remote Sens. 2014, 6(7), 5976-5994; doi:10.3390/rs6075976
Received: 31 March 2014 / Revised: 6 June 2014 / Accepted: 12 June 2014 / Published: 27 June 2014
Cited by 7 | PDF Full-text (1586 KB) | HTML Full-text | XML Full-text
Abstract
Contemporary satellite Earth Observation systems provide growing amounts of very high spatial resolution data that can be used in various applications. An increasing number of sensors make it possible to monitor selected areas in great detail. However, in order to handle the volume of
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Contemporary satellite Earth Observation systems provide growing amounts of very high spatial resolution data that can be used in various applications. An increasing number of sensors make it possible to monitor selected areas in great detail. However, in order to handle the volume of data, a high level of automation is required. The semi-automatic change detection methodology described in this paper was developed to annually update land cover maps prepared in the context of the Geoland2. The proposed algorithm was tailored to work with different very high spatial resolution images acquired over different European landscapes. The methodology is a fusion of various change detection methods ranging from: (1) layer arithmetic; (2) vegetation indices (NDVI) differentiating; (3) texture calculation; and methods based on (4) canonical correlation analysis (multivariate alteration detection (MAD)). User intervention during the production of the change map is limited to the selection of the input data, the size of initial segments and the threshold for texture classification (optionally). To achieve a high level of automation, statistical thresholds were applied in most of the processing steps. Tests showed an overall change recognition accuracy of 89%, and the change type classification methodology can accurately classify transitions between classes. Full article
(This article belongs to the Special Issue Advances in Geographic Object-Based Image Analysis (GEOBIA))
Open AccessArticle Data Transformation Functions for Expanded Search Spaces in Geographic Sample Supervised Segment Generation
Remote Sens. 2014, 6(5), 3791-3821; doi:10.3390/rs6053791
Received: 30 January 2014 / Revised: 28 March 2014 / Accepted: 16 April 2014 / Published: 28 April 2014
Cited by 4 | PDF Full-text (5029 KB) | HTML Full-text | XML Full-text
Abstract
Sample supervised image analysis, in particular sample supervised segment generation, shows promise as a methodological avenue applicable within Geographic Object-Based Image Analysis (GEOBIA). Segmentation is acknowledged as a constituent component within typically expansive image analysis processes. A general extension to the basic formulation
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Sample supervised image analysis, in particular sample supervised segment generation, shows promise as a methodological avenue applicable within Geographic Object-Based Image Analysis (GEOBIA). Segmentation is acknowledged as a constituent component within typically expansive image analysis processes. A general extension to the basic formulation of an empirical discrepancy measure directed segmentation algorithm parameter tuning approach is proposed. An expanded search landscape is defined, consisting not only of the segmentation algorithm parameters, but also of low-level, parameterized image processing functions. Such higher dimensional search landscapes potentially allow for achieving better segmentation accuracies. The proposed method is tested with a range of low-level image transformation functions and two segmentation algorithms. The general effectiveness of such an approach is demonstrated compared to a variant only optimising segmentation algorithm parameters. Further, it is shown that the resultant search landscapes obtained from combining mid- and low-level image processing parameter domains, in our problem contexts, are sufficiently complex to warrant the use of population based stochastic search methods. Interdependencies of these two parameter domains are also demonstrated, necessitating simultaneous optimization. Full article
(This article belongs to the Special Issue Advances in Geographic Object-Based Image Analysis (GEOBIA))
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Open AccessArticle Quantifying Spatial Heterogeneity in Urban Landscapes: Integrating Visual Interpretation and Object-Based Classification
Remote Sens. 2014, 6(4), 3369-3386; doi:10.3390/rs6043369
Received: 10 January 2014 / Revised: 21 March 2014 / Accepted: 26 March 2014 / Published: 16 April 2014
Cited by 9 | PDF Full-text (1933 KB) | HTML Full-text | XML Full-text
Abstract
Describing and quantifying the spatial heterogeneity of land cover in urban systems is crucial for developing an ecological understanding of cities. This paper presents a new approach to quantifying the fine-scale heterogeneity in urban landscapes that capitalizes on the strengths of two commonly
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Describing and quantifying the spatial heterogeneity of land cover in urban systems is crucial for developing an ecological understanding of cities. This paper presents a new approach to quantifying the fine-scale heterogeneity in urban landscapes that capitalizes on the strengths of two commonly used approaches—visual interpretation and object-based image analysis. This new approach integrates the ability of humans to detect pattern with an object-based image analysis that accurately and efficiently quantifies the components that give rise to that pattern. Patches that contain a mix of built and natural land cover features were first delineated through visual interpretation. These patches served as pre-defined boundaries for finer-scale segmentation and classification of within-patch land cover features which were classified using object-based image analysis. Patches were then classified based on the within-patch proportion cover of features. We applied this approach to the Gwynns Falls watershed in Baltimore, Maryland, USA. The object-based classification approach proved to be effective for classifying within-patch land cover features. The overall accuracy of the classification maps of 1999 and 2004 were 92.3% and 93.7%, respectively. This exercise demonstrates that by integrating visual interpretation with object-based classification, the fine-scale spatial heterogeneity in urban landscapes and land cover change can be described and quantified in a more efficient and ecologically meaningful way than either purely automated or visual methods alone. This new approach provides a tool that allows us to quantify the structure of the urban landscape including both built and non-built components that will better accommodate ecological research linking system structure to ecological processes. Full article
(This article belongs to the Special Issue Advances in Geographic Object-Based Image Analysis (GEOBIA))
Open AccessArticle Segmentation-Based Filtering of Airborne LiDAR Point Clouds by Progressive Densification of Terrain Segments
Remote Sens. 2014, 6(2), 1294-1326; doi:10.3390/rs6021294
Received: 14 November 2013 / Revised: 17 January 2014 / Accepted: 22 January 2014 / Published: 7 February 2014
Cited by 13 | PDF Full-text (3973 KB) | HTML Full-text | XML Full-text
Abstract
Filtering is one of the core post-processing steps for Airborne Laser Scanning (ALS) point clouds. A segmentation-based filtering (SBF) method is proposed herein. This method is composed of three key steps: point cloud segmentation, multiple echoes analysis, and iterative judgment. Moreover, the third
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Filtering is one of the core post-processing steps for Airborne Laser Scanning (ALS) point clouds. A segmentation-based filtering (SBF) method is proposed herein. This method is composed of three key steps: point cloud segmentation, multiple echoes analysis, and iterative judgment. Moreover, the third step is our main contribution. Particularly, the iterative judgment is based on the framework of the classic progressive TIN (triangular irregular network) densification (PTD) method, but with basic processing unit being a segment rather than a single point. Seven benchmark datasets provided by ISPRS Working Group III/3 are utilized to test the SBF algorithm and the classic PTD method. Experimental results suggest that, compared with the PTD method, the SBF approach is capable of preserving discontinuities of landscapes and removing the lower parts of large objects attached on the ground surface. As a result, the SBF approach is able to reduce omission errors and total errors by 18.26% and 11.47% respectively, which would significantly decrease the cost of manual operation required in post-processing. Full article
(This article belongs to the Special Issue Advances in Geographic Object-Based Image Analysis (GEOBIA))

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