ISPRS International Journal of Geo-Information

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20 pages, 4605 KiB

Open AccessArticle

Impact of the Scale on Several Metrics Used in Geographical Object-Based Image Analysis: Does GEOBIA Mitigate the Modifiable Areal Unit Problem (MAUP)?

by Didier Josselin and Romain Louvet

ISPRS Int. J. Geo-Inf. 2019, 8(3), 156; https://doi.org/10.3390/ijgi8030156 - 22 Mar 2019

Cited by 12 | Viewed by 4014

Abstract

Using two GEOBIA (Geographical Object Based Image Analysis) algorithms on a set of segmented images compared to grid partitioning at different scales, we show that statistical metrics related to both objects and sets of pixels are (more or less) subject to the Modifiable [...] Read more.

Using two GEOBIA (Geographical Object Based Image Analysis) algorithms on a set of segmented images compared to grid partitioning at different scales, we show that statistical metrics related to both objects and sets of pixels are (more or less) subject to the Modifiable Areal Unit Problem. Subsequently, even in a same spatial partition, there may be a bias in statistics describing the objects due to some size effect of the pixel samples. For instance, pixels homogeneity based on Grey Level Cooccurrence Matrices (GLCM), Landscape Shape Index, entropy, object compacity, perimeter/area ratio are studied according to scale. The approach consists in studying the behavior of a given statistical metrics through scales and to compare the results on several image segmentations, according to different partitioning processes, from GEOBIA (Baatz & Schäpe algorithm and Self Organizing Maps) or using reference grids. We finally discuss about the relationship between GEOBIA metrics and scale. By analysing object shape and pixels composition from different metrics points of views, we show that GEOBIA does not always mitigate the Modifiable Areal Unit Problem. Full article

(This article belongs to the Special Issue GEOBIA in a Changing World)

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18 pages, 3911 KiB

Open AccessArticle

Multi-Level Morphometric Characterization of Built-up Areas and Change Detection in Siberian Sub-Arctic Urban Area: Yakutsk

by Sébastien Gadal and Walid Ouerghemmi

ISPRS Int. J. Geo-Inf. 2019, 8(3), 129; https://doi.org/10.3390/ijgi8030129 - 4 Mar 2019

Cited by 12 | Viewed by 4137

Abstract

Recognition and characterization of built-up areas in the Siberian sub-Arctic urban territories of Yakutsk are dependent on two main factors: (1) the season (snow and ice from October to the end of April, the flooding period in May, and the summertime), which influences [...] Read more.

Recognition and characterization of built-up areas in the Siberian sub-Arctic urban territories of Yakutsk are dependent on two main factors: (1) the season (snow and ice from October to the end of April, the flooding period in May, and the summertime), which influences the accuracy of urban object detection, and (2) the urban structure, which influences the morphological recognition and characterization of built-up areas. In this study, high repetitiveness remote sensing Sentinel-2A and SPOT 6 high-resolution satellite images were combined to characterize and detect urban built-up areas over the city of Yakutsk. High temporal resolution of Sentinel-2A allows land use change detection and metric spatial resolution of SPOT 6 allows the characterization of built-up areas’ socioeconomic functions and uses. Full article

(This article belongs to the Special Issue GEOBIA in a Changing World)

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17 pages, 11210 KiB

Open AccessArticle

A Generic Framework for Combining Multiple Segmentations in Geographic Object-Based Image Analysis

by Sébastien Lefèvre, David Sheeren and Onur Tasar

ISPRS Int. J. Geo-Inf. 2019, 8(2), 70; https://doi.org/10.3390/ijgi8020070 - 30 Jan 2019

Cited by 5 | Viewed by 4177

Abstract

The Geographic Object-Based Image Analysis (GEOBIA) paradigm relies strongly on the segmentation concept, i.e., partitioning of an image into regions or objects that are then further analyzed. Segmentation is a critical step, for which a wide range of methods, parameters and input data [...] Read more.

The Geographic Object-Based Image Analysis (GEOBIA) paradigm relies strongly on the segmentation concept, i.e., partitioning of an image into regions or objects that are then further analyzed. Segmentation is a critical step, for which a wide range of methods, parameters and input data are available. To reduce the sensitivity of the GEOBIA process to the segmentation step, here we consider that a set of segmentation maps can be derived from remote sensing data. Inspired by the ensemble paradigm that combines multiple weak classifiers to build a strong one, we propose a novel framework for combining multiple segmentation maps. The combination leads to a fine-grained partition of segments (super-pixels) that is built by intersecting individual input partitions, and each segment is assigned a segmentation confidence score that relates directly to the local consensus between the different segmentation maps. Furthermore, each input segmentation can be assigned some local or global quality score based on expert assessment or automatic analysis. These scores are then taken into account when computing the confidence map that results from the combination of the segmentation processes. This means the process is less affected by incorrect segmentation inputs either at the local scale of a region, or at the global scale of a map. In contrast to related works, the proposed framework is fully generic and does not rely on specific input data to drive the combination process. We assess its relevance through experiments conducted on ISPRS 2D Semantic Labeling. Results show that the confidence map provides valuable information that can be produced when combining segmentations, and fusion at the object level is competitive w.r.t. fusion at the pixel or decision level. Full article

(This article belongs to the Special Issue GEOBIA in a Changing World)

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16 pages, 8621 KiB

Open AccessArticle

GEOBIA at the Terapixel Scale: Toward Efficient Mapping of Small Woody Features from Heterogeneous VHR Scenes

by François Merciol, Loïc Faucqueur, Bharath Bhushan Damodaran, Pierre-Yves Rémy, Baudouin Desclée, Fabrice Dazin, Sébastien Lefèvre, Antoine Masse and Christophe Sannier

ISPRS Int. J. Geo-Inf. 2019, 8(1), 46; https://doi.org/10.3390/ijgi8010046 - 18 Jan 2019

Cited by 14 | Viewed by 4590

Abstract

Land cover mapping has benefited a lot from the introduction of the Geographic Object-Based Image Analysis (GEOBIA) paradigm, that allowed to move from a pixelwise analysis to a processing of elements with richer semantic content, namely objects or regions. However, this paradigm requires [...] Read more.

Land cover mapping has benefited a lot from the introduction of the Geographic Object-Based Image Analysis (GEOBIA) paradigm, that allowed to move from a pixelwise analysis to a processing of elements with richer semantic content, namely objects or regions. However, this paradigm requires to define an appropriate scale, that can be challenging in a large-area study where a wide range of landscapes can be observed. We propose here to conduct the multiscale analysis based on hierarchical representations, from which features known as differential attribute profiles are derived over each single pixel. Efficient and scalable algorithms for construction and analysis of such representations, together with an optimized usage of the random forest classifier, provide us with a semi-supervised framework in which a user can drive mapping of elements such as Small Woody Features at a very large area. Indeed, the proposed open-source methodology has been successfully used to derive a part of the High Resolution Layers (HRL) product of the Copernicus Land Monitoring service, thus showing how the GEOBIA framework can be used in a big data scenario made of more than 38,000 Very High Resolution (VHR) satellite images representing more than 120 TB of data. Full article

(This article belongs to the Special Issue GEOBIA in a Changing World)

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17 pages, 8210 KiB

Open AccessArticle

Semi-Automatic Versus Manual Mapping of Cold-Water Coral Carbonate Mounds Located Offshore Norway

by Alexandra Jarna, Nicole J. Baeten, Sigrid Elvenes, Valérie K. Bellec, Terje Thorsnes and Markus Diesing

ISPRS Int. J. Geo-Inf. 2019, 8(1), 40; https://doi.org/10.3390/ijgi8010040 - 16 Jan 2019

Cited by 6 | Viewed by 4496

Abstract

Cold-water coral reefs are hotspots of biological diversity and play an important role as carbonate factories in the global carbon cycle. Reef-building corals can be found in cold oceanic waters around the world. Detailed knowledge on the spatial location and distribution of coral [...] Read more.

Cold-water coral reefs are hotspots of biological diversity and play an important role as carbonate factories in the global carbon cycle. Reef-building corals can be found in cold oceanic waters around the world. Detailed knowledge on the spatial location and distribution of coral reefs is of importance for spatial management, conservation and science. Carbonate mounds (reefs) are readily identifiable in high-resolution multibeam echosounder data but systematic mapping programs have relied mostly on visual interpretation and manual digitizing so far. Developing more automated methods will help to reduce the time spent on this laborious task and will additionally lead to more objective and reproducible results. In this paper, we present an attempt at testing whether rule-based classification can replace manual mapping when mapping cold-water coral carbonate mounds. To that end, we have estimated and compared the accuracies of manual mapping, pixel-based terrain analysis and object-based image analysis. To verify the mapping results, we created a reference dataset of presence/absence points agreed upon by three mapping experts. There were no statistically significant differences in the overall accuracies of the maps produced by the three approaches. We conclude that semi-automated rule-based methods might be a viable option for mapping carbonate mounds with high spatial detail over large areas. Full article

(This article belongs to the Special Issue GEOBIA in a Changing World)

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26 pages, 37044 KiB

Open AccessArticle

Comparison of Independent Component Analysis, Principal Component Analysis, and Minimum Noise Fraction Transformation for Tree Species Classification Using APEX Hyperspectral Imagery

by Zahra Dabiri and Stefan Lang

ISPRS Int. J. Geo-Inf. 2018, 7(12), 488; https://doi.org/10.3390/ijgi7120488 - 19 Dec 2018

Cited by 35 | Viewed by 6977

Abstract

Hyperspectral imagery provides detailed spectral information that can be used for tree species discrimination. The aim of this study is to assess spectral–spatial complexity reduction techniques for tree species classification using an airborne prism experiment (APEX) hyperspectral image. The methodology comprised the following [...] Read more.

Hyperspectral imagery provides detailed spectral information that can be used for tree species discrimination. The aim of this study is to assess spectral–spatial complexity reduction techniques for tree species classification using an airborne prism experiment (APEX) hyperspectral image. The methodology comprised the following main steps: (1) preprocessing (removing noisy bands) and masking out non-forested areas; (2) applying dimensionality reduction techniques, namely, independent component analysis (ICA), principal component analysis (PCA), and minimum noise fraction transformation (MNF), and stacking the selected dimensionality-reduced (DR) components to create new data cubes; (3) super-pixel segmentation on the original image and on each of the dimensionality-reduced data cubes; (4) tree species classification using a random forest (RF) classifier; and (5) accuracy assessment. The results revealed that tree species classification using the APEX hyperspectral imagery and DR data cubes yielded good results (with an overall accuracy of 80% for the APEX imagery and an overall accuracy of more than 90% for the DR data cubes). Among the classification results of the DR data cubes, the ICA-transformed components performed best, followed by the MNF-transformed components and the PCA-transformed components. The best class performance (according to producer’s and user’s accuracy) belonged to Picea abies and Salix alba. The other classes (Populus x (hybrid), Alnus incana, Fraxinus excelsior, and Quercus robur) performed differently depending on the different DR data cubes used as the input to the RF classifier. Full article

(This article belongs to the Special Issue GEOBIA in a Changing World)

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Graphical abstract

25 pages, 3507 KiB

Open AccessFeature PaperArticle

Integrating GEOBIA, Machine Learning, and Volunteered Geographic Information to Map Vegetation over Rooftops

by David C. Griffith and Geoffrey J. Hay

ISPRS Int. J. Geo-Inf. 2018, 7(12), 462; https://doi.org/10.3390/ijgi7120462 - 29 Nov 2018

Cited by 11 | Viewed by 5406

Abstract

The objective of this study is to evaluate operational methods for creating a particular type of urban vegetation map—one focused on vegetation over rooftops (VOR), specifically trees that extend over urban residential buildings. A key constraint was the use of passive remote sensing [...] Read more.

The objective of this study is to evaluate operational methods for creating a particular type of urban vegetation map—one focused on vegetation over rooftops (VOR), specifically trees that extend over urban residential buildings. A key constraint was the use of passive remote sensing data only. To achieve this, we (1) conduct a review of the urban remote sensing vegetation classification literature, and we then (2) discuss methods to derive a detailed map of VOR for a study area in Calgary, Alberta, Canada from a late season, high-resolution airborne orthomosaic based on an integration of Geographic Object-Based Image Analysis (GEOBIA), pre-classification filtering of image-objects using Volunteered Geographic Information (VGI), and a machine learning classifier. Pre-classification filtering lowered the computational burden of classification by reducing the number of input objects by 14%. Accuracy assessment results show that, despite the presence of senescing vegetation with low vegetation index values and deep shadows, classification using a small number of image-object spectral attributes as classification features (n = 9) had similar overall accuracy (88.5%) to a much more complex classification (91.8%) comprising a comprehensive set of spectral, texture, and spatial attributes as classification features (n = 86). This research provides an example of the very specific questions answerable about precise urban locations using a combination of high-resolution passive imagery and freely available VGI data. It highlights the benefits of pre-classification filtering and the judicious selection of features from image-object attributes to reduce processing load without sacrificing classification accuracy. Full article

(This article belongs to the Special Issue GEOBIA in a Changing World)

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47 pages, 7691 KiB

Open AccessArticle

AutoCloud+, a “Universal” Physical and Statistical Model-Based 2D Spatial Topology-Preserving Software for Cloud/Cloud–Shadow Detection in Multi-Sensor Single-Date Earth Observation Multi-Spectral Imagery—Part 1: Systematic ESA EO Level 2 Product Generation at the Ground Segment as Broad Context

by Andrea Baraldi and Dirk Tiede

ISPRS Int. J. Geo-Inf. 2018, 7(12), 457; https://doi.org/10.3390/ijgi7120457 - 26 Nov 2018

Cited by 13 | Viewed by 6223

Abstract

The European Space Agency (ESA) defines Earth observation (EO) Level 2 information product the stack of: (i) a single-date multi-spectral (MS) image, radiometrically corrected for atmospheric, adjacency and topographic effects, with (ii) its data-derived scene classification map (SCM), whose thematic map legend includes [...] Read more.

The European Space Agency (ESA) defines Earth observation (EO) Level 2 information product the stack of: (i) a single-date multi-spectral (MS) image, radiometrically corrected for atmospheric, adjacency and topographic effects, with (ii) its data-derived scene classification map (SCM), whose thematic map legend includes quality layers cloud and cloud–shadow. Never accomplished to date in an operating mode by any EO data provider at the ground segment, systematic ESA EO Level 2 product generation is an inherently ill-posed computer vision (CV) problem (chicken-and-egg dilemma) in the multi-disciplinary domain of cognitive science, encompassing CV as subset-of artificial general intelligence (AI). In such a broad context, the goal of our work is the research and technological development (RTD) of a “universal” AutoCloud+ software system in operating mode, capable of systematic cloud and cloud–shadow quality layers detection in multi-sensor, multi-temporal and multi-angular EO big data cubes characterized by the five Vs, namely, volume, variety, veracity, velocity and value. For the sake of readability, this paper is divided in two. Part 1 highlights why AutoCloud+ is important in a broad context of systematic ESA EO Level 2 product generation at the ground segment. The main conclusions of Part 1 are both conceptual and pragmatic in the definition of remote sensing best practices, which is the focus of efforts made by intergovernmental organizations such as the Group on Earth Observations (GEO) and the Committee on Earth Observation Satellites (CEOS). First, the ESA EO Level 2 product definition is recommended for consideration as state-of-the-art EO Analysis Ready Data (ARD) format. Second, systematic multi-sensor ESA EO Level 2 information product generation is regarded as: (a) necessary-but-not-sufficient pre-condition for the yet-unaccomplished dependent problems of semantic content-based image retrieval (SCBIR) and semantics-enabled information/knowledge discovery (SEIKD) in multi-source EO big data cubes, where SCBIR and SEIKD are part-of the GEO-CEOS visionary goal of a yet-unaccomplished Global EO System of Systems (GEOSS). (b) Horizontal policy, the goal of which is background developments, in a “seamless chain of innovation” needed for a new era of Space Economy 4.0. In the subsequent Part 2 (proposed as Supplementary Materials), the AutoCloud+ software system requirements specification, information/knowledge representation, system design, algorithm, implementation and preliminary experimental results are presented and discussed. Full article

(This article belongs to the Special Issue GEOBIA in a Changing World)

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27 pages, 22984 KiB

Open AccessArticle

Change Detection in Coral Reef Environment Using High-Resolution Images: Comparison of Object-Based and Pixel-Based Paradigms

by Zhenjin Zhou, Lei Ma, Tengyu Fu, Ge Zhang, Mengru Yao and Manchun Li

ISPRS Int. J. Geo-Inf. 2018, 7(11), 441; https://doi.org/10.3390/ijgi7110441 - 12 Nov 2018

Cited by 31 | Viewed by 5946

Abstract

Despite increases in the spatial resolution of satellite imagery prompting interest in object-based image analysis, few studies have used object-based methods for monitoring changes in coral reefs. This study proposes a high accuracy object-based change detection (OBCD) method intended for coral reef environment, [...] Read more.

Despite increases in the spatial resolution of satellite imagery prompting interest in object-based image analysis, few studies have used object-based methods for monitoring changes in coral reefs. This study proposes a high accuracy object-based change detection (OBCD) method intended for coral reef environment, which uses QuickBird and WorldView-2 images. The proposed methodological framework includes image fusion, multi-temporal image segmentation, image differencing, random forests models, and object-area-based accuracy assessment. For validation, we applied the method to images of four coral reef study sites in the South China Sea. We compared the proposed OBCD method with a conventional pixel-based change detection (PBCD) method by implementing both methods under the same conditions. The average overall accuracy of OBCD exceeded 90%, which was approximately 20% higher than PBCD. The OBCD method was free from salt-and-pepper effects and was less prone to images misregistration in terms of change detection accuracy and mapping results. The object-area-based accuracy assessment reached a higher overall accuracy and per-class accuracy than the object-number-based and pixel-number-based accuracy assessment. Full article

(This article belongs to the Special Issue GEOBIA in a Changing World)

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21 pages, 3871 KiB

Open AccessArticle

Comparison of Landscape Metrics for Three Different Level Land Cover/Land Use Maps

by Elif Sertel, Raziye Hale Topaloğlu, Betül Şallı, Irmak Yay Algan and Gül Aslı Aksu

ISPRS Int. J. Geo-Inf. 2018, 7(10), 408; https://doi.org/10.3390/ijgi7100408 - 15 Oct 2018

Cited by 54 | Viewed by 8072

Abstract

This research aims to investigate how different landscape metrics are affected by the enhancement of the thematic classes in land cover/land use (LC/LU) maps. For this aim, three different LC/LU maps based on three different levels of CORINE (Coordination of Information on The [...] Read more.

This research aims to investigate how different landscape metrics are affected by the enhancement of the thematic classes in land cover/land use (LC/LU) maps. For this aim, three different LC/LU maps based on three different levels of CORINE (Coordination of Information on The Environment) nomenclature were created for the selected study area using GEOBIA (Geographic Object Based Image Analysis) techniques. First, second and third level LC/LU maps of the study area have five, thirteen and twenty-seven hierarchical thematic classes, respectively. High-resolution Spot 7 images with 1.5 m spatial resolution were used as the main Earth Observation data to create LC/LU maps. Additional geospatial data from open sources (OpenStreetMap and Wikimapia) were also integrated to the classification in order to identify some of the 2nd and 3rd level LC/LU classes. Classification procedure was initially conducted for Level 3 classes in which we developed decision trees to be used in object-based classification. Afterwards, Level 3 classes were merged to create Level 2 LC/LU map and then Level 2 classes were merged to create the Level 1 LC/LU map according to CORINE nomenclature. The accuracy of Level 1, Level 2, Level 3 maps are calculated as; 93.50%, 89.00%, 85.50% respectively. At the last stage, several landscape metrics such as Number of Patch (NP), Edge Density (ED), Largest Patch Index (LPI), Euclidean Nearest Neighbor Distance (ENN), Splitting Index (SPLIT) and Aggregation Index (AI) metrics and others were calculated for different level LC/LU maps and landscape metrics values were compared to analyze the impact of changing thematic details on landscape metrics. Our results show that, increasing the thematic detail allows landscape characteristics to be defined more precisely and ensure comprehensive assessment of cause and effect relationships between classes. Full article

(This article belongs to the Special Issue GEOBIA in a Changing World)

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24 pages, 8952 KiB

Open AccessArticle

Sentinel-2 Based Temporal Detection of Agricultural Land Use Anomalies in Support of Common Agricultural Policy Monitoring

by Urška Kanjir, Nataša Đurić and Tatjana Veljanovski

ISPRS Int. J. Geo-Inf. 2018, 7(10), 405; https://doi.org/10.3390/ijgi7100405 - 13 Oct 2018

Cited by 45 | Viewed by 7178

Abstract

The European Common Agricultural Policy (CAP) post-2020 timeframe reform will reshape the agriculture land use control procedures from a selected risk fields-based approach into an all-inclusive one. The reform fosters the use of Sentinel data with the objective of enabling greater transparency and [...] Read more.

The European Common Agricultural Policy (CAP) post-2020 timeframe reform will reshape the agriculture land use control procedures from a selected risk fields-based approach into an all-inclusive one. The reform fosters the use of Sentinel data with the objective of enabling greater transparency and comparability of CAP results in different Member States. In this paper, we investigate the analysis of a time series approach using Sentinel-2 images and the suitability of the BFAST (Breaks for Additive Season and Trend) Monitor method to detect changes that correspond to land use anomaly observations in the assessment of agricultural parcel management activities. We focus on identifying certain signs of ineligible (inconsistent) use in permanent meadows and crop fields in one growing season, and in particular those that can be associated with time-defined greenness (vegetation vigor). Depending on the requirements of the BFAST Monitor method and currently time-limited Sentinel-2 dataset for the reliable anomaly study, we introduce customized procedures to support and verify the BFAST Monitor anomaly detection results using the analysis of NDVI (Normalized Difference Vegetation Index) object-based temporal profiles and time-series standard deviation output, where geographical objects of interest are parcels of particular land use. The validation of land use candidate anomalies in view of land use ineligibilities was performed with the information on declared land annual use and field controls, as obtained in the framework of subsidy granting in Slovenia. The results confirm that the proposed combined approach proves efficient to deal with short time series and yields high accuracy rates in monitoring agricultural parcel greenness. As such it can already be introduced to help the process of agricultural land use control within certain CAP activities in the preparation and adaptation phase. Full article

(This article belongs to the Special Issue GEOBIA in a Changing World)

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15 pages, 7042 KiB

Open AccessArticle

An Object-Based Image Analysis Workflow for Monitoring Shallow-Water Aquatic Vegetation in Multispectral Drone Imagery

by Dominique Chabot, Christopher Dillon, Adam Shemrock, Nicholas Weissflog and Eric P. S. Sager

ISPRS Int. J. Geo-Inf. 2018, 7(8), 294; https://doi.org/10.3390/ijgi7080294 - 24 Jul 2018

Cited by 68 | Viewed by 9197

Abstract

High-resolution drone aerial surveys combined with object-based image analysis are transforming our capacity to monitor and manage aquatic vegetation in an era of invasive species. To better exploit the potential of these technologies, there is a need to develop more efficient and accessible [...] Read more.

High-resolution drone aerial surveys combined with object-based image analysis are transforming our capacity to monitor and manage aquatic vegetation in an era of invasive species. To better exploit the potential of these technologies, there is a need to develop more efficient and accessible analysis workflows and focus more efforts on the distinct challenge of mapping submerged vegetation. We present a straightforward workflow developed to monitor emergent and submerged invasive water soldier (Stratiotes aloides) in shallow waters of the Trent-Severn Waterway in Ontario, Canada. The main elements of the workflow are: (1) collection of radiometrically calibrated multispectral imagery including a near-infrared band; (2) multistage segmentation of the imagery involving an initial separation of above-water from submerged features; and (3) automated classification of features with a supervised machine-learning classifier. The approach yielded excellent classification accuracy for emergent features (overall accuracy = 92%; kappa = 88%; water soldier producer’s accuracy = 92%; user’s accuracy = 91%) and good accuracy for submerged features (overall accuracy = 84%; kappa = 75%; water soldier producer’s accuracy = 71%; user’s accuracy = 84%). The workflow employs off-the-shelf graphical software tools requiring no programming or coding, and could therefore be used by anyone with basic GIS and image analysis skills for a potentially wide variety of aquatic vegetation monitoring operations. Full article

(This article belongs to the Special Issue GEOBIA in a Changing World)

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22 pages, 7729 KiB

Open AccessArticle

Spatial Characterization and Mapping of Gated Communities

by Agnes Silva de Araujo and Alfredo Pereira de Queiroz

ISPRS Int. J. Geo-Inf. 2018, 7(7), 248; https://doi.org/10.3390/ijgi7070248 - 25 Jun 2018

Cited by 6 | Viewed by 5665

Abstract

The increase in gated communities is the most important recent urban phenomenon in Latin America. This article proposes a methodology to identify the morphological features and spatial characteristics of gated communities to map them based on the land cover map and the quality [...] Read more.

The increase in gated communities is the most important recent urban phenomenon in Latin America. This article proposes a methodology to identify the morphological features and spatial characteristics of gated communities to map them based on the land cover map and the quality of life index. The importance of this proposal is related to the fact that there are no official statistics on gated communities in most Latin American countries. The proposal was tested in Marília, a medium-sized city in southeastern Brazil. Geographic object-based image analysis with high-resolution satellite images and 2010 demographic census variables were used to support the research procedures. The accuracy of the output was 83.3%. It was found that there is a positive correlation between the quality of life index and the occurrence of high-standard gated communities (golden ghettos). They were mainly identified by the following land cover classes: white painted concrete slabs/light-colored roof tiles, and the existence of pavement, pools, and herbaceous vegetation. In addition to mapping the gated communities, it was possible to classify them according to the categories proposed in the literature (golden ghettos and lifestyle gated communities). Full article

(This article belongs to the Special Issue GEOBIA in a Changing World)

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4220 KiB

Open AccessArticle

Benchmarking the Applicability of Ontology in Geographic Object-Based Image Analysis

by Sachit Rajbhandari, Jagannath Aryal, Jon Osborn, Rob Musk and Arko Lucieer

ISPRS Int. J. Geo-Inf. 2017, 6(12), 386; https://doi.org/10.3390/ijgi6120386 - 28 Nov 2017

Cited by 17 | Viewed by 5172

Abstract

In Geographic Object-based Image Analysis (GEOBIA), identification of image objects is normally achieved using rule-based classification techniques supported by appropriate domain knowledge. However, GEOBIA currently lacks a systematic method to formalise the domain knowledge required for image object identification. Ontology provides a representation [...] Read more.

In Geographic Object-based Image Analysis (GEOBIA), identification of image objects is normally achieved using rule-based classification techniques supported by appropriate domain knowledge. However, GEOBIA currently lacks a systematic method to formalise the domain knowledge required for image object identification. Ontology provides a representation vocabulary for characterising domain-specific classes. This study proposes an ontological framework that conceptualises domain knowledge in order to support the application of rule-based classifications. The proposed ontological framework is tested with a landslide case study. The Web Ontology Language (OWL) is used to construct an ontology in the landslide domain. The segmented image objects with extracted features are incorporated into the ontology as instances. The classification rules are written in Semantic Web Rule Language (SWRL) and executed using a semantic reasoner to assign instances to appropriate landslide classes. Machine learning techniques are used to predict new threshold values for feature attributes in the rules. Our framework is compared with published work on landslide detection where ontology was not used for the image classification. Our results demonstrate that a classification derived from the ontological framework accords with non-ontological methods. This study benchmarks the ontological method providing an alternative approach for image classification in the case study of landslides. Full article

(This article belongs to the Special Issue GEOBIA in a Changing World)

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Review

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19 pages, 7788 KiB

Open AccessFeature PaperReview

GEOBIA Achievements and Spatial Opportunities in the Era of Big Earth Observation Data

by Stefan Lang, Geoffrey J. Hay, Andrea Baraldi, Dirk Tiede and Thomas Blaschke

ISPRS Int. J. Geo-Inf. 2019, 8(11), 474; https://doi.org/10.3390/ijgi8110474 - 24 Oct 2019

Cited by 33 | Viewed by 5643

Abstract

The primary goal of collecting Earth observation (EO) imagery is to map, analyze, and contribute to an understanding of the status and dynamics of geographic phenomena. In geographic information science (GIScience), the term object-based image analysis (OBIA) was tentatively introduced in 2006. When [...] Read more.

The primary goal of collecting Earth observation (EO) imagery is to map, analyze, and contribute to an understanding of the status and dynamics of geographic phenomena. In geographic information science (GIScience), the term object-based image analysis (OBIA) was tentatively introduced in 2006. When it was re-formulated in 2008 as geographic object-based image analysis (GEOBIA), the primary focus was on integrating multiscale EO data with GIScience and computer vision (CV) solutions to cope with the increasing spatial and temporal resolution of EO imagery. Building on recent trends in the context of big EO data analytics as well as major achievements in CV, the objective of this article is to review the role of spatial concepts in the understanding of image objects as the primary analytical units in semantic EO image analysis, and to identify opportunities where GEOBIA may support multi-source remote sensing analysis in the era of big EO data analytics. We (re-)emphasize the spatial paradigm as a key requisite for an image understanding system capable to deal with and exploit the massive data streams we are currently facing; a system which encompasses a combined physical and statistical model-based inference engine, a well-structured CV system design based on a convergence of spatial and colour evidence, semantic content-based image retrieval capacities, and the full integration of spatio-temporal aspects of the studied geographical phenomena. Full article

(This article belongs to the Special Issue GEOBIA in a Changing World)

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