A review of supervised object-based land-cover image classification

Abstract Object-based image classification for land-cover mapping purposes using remote-sensing imagery has attracted significant attention in recent years. Numerous studies conducted over the past decade have investigated a broad array of sensors, feature selection, classifiers, and other factors of interest. However, these research results have not yet been synthesized to provide coherent guidance on the effect of different supervised object-based land-cover classification processes. In this study, we first construct a database with 28 fields using qualitative and quantitative information extracted from 254 experimental cases described in 173 scientific papers. Second, the results of the meta-analysis are reported, including general characteristics of the studies (e.g., the geographic range of relevant institutes, preferred journals) and the relationships between factors of interest (e.g., spatial resolution and study area or optimal segmentation scale, accuracy and number of targeted classes), especially with respect to the classification accuracy of different sensors, segmentation scale, training set size, supervised classifiers, and land-cover types. Third, useful data on supervised object-based image classification are determined from the meta-analysis. For example, we find that supervised object-based classification is currently experiencing rapid advances, while development of the fuzzy technique is limited in the object-based framework. Furthermore, spatial resolution correlates with the optimal segmentation scale and study area, and Random Forest (RF) shows the best performance in object-based classification. The area-based accuracy assessment method can obtain stable classification performance, and indicates a strong correlation between accuracy and training set size, while the accuracy of the point-based method is likely to be unstable due to mixed objects. In addition, the overall accuracy benefits from higher spatial resolution images (e.g., unmanned aerial vehicle) or agricultural sites where it also correlates with the number of targeted classes. More than 95.6% of studies involve an area less than 300 ha, and the spatial resolution of images is predominantly between 0 and 2 m. Furthermore, we identify some methods that may advance supervised object-based image classification. For example, deep learning and type-2 fuzzy techniques may further improve classification accuracy. Lastly, scientists are strongly encouraged to report results of uncertainty studies to further explore the effects of varied factors on supervised object-based image classification.

[1]  Shihong Du,et al.  Semantic classification of urban buildings combining VHR image and GIS data: An improved random forest approach , 2015 .

[2]  S. L. J. Oliveira,et al.  Modelling Fire Frequency in a Cerrado Savanna Protected Area , 2014, PloS one.

[3]  Zhang Xiangmin,et al.  Comparison of pixel‐based and object‐oriented image classification approaches—a case study in a coal fire area, Wuda, Inner Mongolia, China , 2006 .

[4]  G. Brent Hall,et al.  Landscape resource mapping for wildlife research using very high resolution satellite imagery , 2013 .

[5]  Thomas Blaschke,et al.  Image Segmentation Methods for Object-based Analysis and Classification , 2004 .

[6]  Roman Arbiol,et al.  Advanced Classification Techniques: A Review , 2007 .

[7]  Liang Cheng,et al.  Cultivated land information extraction from high-resolution unmanned aerial vehicle imagery data , 2014 .

[8]  Lorenzo Bruzzone,et al.  Classification of hyperspectral remote sensing images with support vector machines , 2004, IEEE Transactions on Geoscience and Remote Sensing.

[9]  Faith R. Kearns,et al.  Classification of the wildland-urban interface: A comparison of pixel- and object-based classifications using high-resolution aerial photography , 2008, Comput. Environ. Urban Syst..

[10]  Wenzhong Shi,et al.  Quality assessment for geo‐spatial objects derived from remotely sensed data , 2005 .

[11]  J. Six,et al.  Object-based crop identification using multiple vegetation indices, textural features and crop phenology , 2011 .

[12]  Philippe Blondel,et al.  Evaluation of seabed mapping methods for fine-scale classification of extremely shallow benthic habitats – application to the Venice Lagoon, Italy , 2016 .

[13]  Bernard De Baets,et al.  Random Forests as a tool for estimating uncertainty at pixel-level in SAR image classification , 2012, Int. J. Appl. Earth Obs. Geoinformation.

[14]  Sayan Mukherjee,et al.  Feature Selection for SVMs , 2000, NIPS.

[15]  T. Warner,et al.  Multi-scale GEOBIA with very high spatial resolution digital aerial imagery: scale, texture and image objects , 2011 .

[16]  Antônio Miguel Vieira Monteiro,et al.  Parameter selection for region‐growing image segmentation algorithms using spatial autocorrelation , 2006 .

[17]  S. Goetz,et al.  A meta-analysis of terrestrial aboveground biomass estimation using lidar remote sensing , 2013 .

[18]  Yan Wang,et al.  The Effects of Point or Polygon Based Training Data on RandomForest Classification Accuracy of Wetlands , 2015, Remote. Sens..

[19]  Thomas Blaschke,et al.  Geographic Object-Based Image Analysis – Towards a new paradigm , 2014, ISPRS journal of photogrammetry and remote sensing : official publication of the International Society for Photogrammetry and Remote Sensing.

[20]  Kitsanai Charoenjit,et al.  Estimation of biomass and carbon stock in Para rubber plantations using object-based classification from Thaichote satellite data in Eastern Thailand , 2015 .

[21]  Steven E. Franklin,et al.  A comparison of pixel-based and object-based image analysis with selected machine learning algorithms for the classification of agricultural landscapes using SPOT-5 HRG imagery , 2012 .

[22]  Jerome O'Connell,et al.  Wide-area mapping of small-scale features in agricultural landscapes using airborne remote sensing , 2015, ISPRS journal of photogrammetry and remote sensing : official publication of the International Society for Photogrammetry and Remote Sensing.

[23]  Hideki Saito,et al.  Spectral normalization of SPOT 4 data to adjust for changing leaf phenology within seasonal forests in Cambodia , 2014 .

[24]  R. Pu,et al.  A comparative analysis of high spatial resolution IKONOS and WorldView-2 imagery for mapping urban tree species , 2012 .

[25]  Timothy A. Warner,et al.  Estimation of optimal image object size for the segmentation of forest stands with multispectral IKONOS imagery , 2008 .

[26]  Markku Kuitunen,et al.  What makes segmentation good? A case study in boreal forest habitat mapping , 2013 .

[27]  Josef Strobl,et al.  What’s wrong with pixels? Some recent developments interfacing remote sensing and GIS , 2001 .

[28]  Peter F. Fisher,et al.  Remote sensing of land cover classes as type 2 fuzzy sets , 2010 .

[29]  Gang Chen,et al.  Identification of Forested Landslides Using LiDar Data, Object-based Image Analysis, and Machine Learning Algorithms , 2015, Remote. Sens..

[30]  A. Laliberte,et al.  Comparison of Nearest Neighbor and Rule-based Decision Tree Classification in an Object-oriented Environment , 2006, 2006 IEEE International Symposium on Geoscience and Remote Sensing.

[31]  Thomas Blaschke,et al.  Evaluation of Feature Selection Methods for Object-Based Land Cover Mapping of Unmanned Aerial Vehicle Imagery Using Random Forest and Support Vector Machine Classifiers , 2017, ISPRS Int. J. Geo Inf..

[32]  James D. Wickham,et al.  Pixels, blocks of pixels, and polygons: Choosing a spatial unit for thematic accuracy assessment , 2011 .

[33]  Clement Atzberger,et al.  Object Based Image Analysis and Data Mining applied to a remotely sensed Landsat time-series to map sugarcane over large areas , 2012 .

[34]  Peter Pehani,et al.  Application of In-Segment Multiple Sampling in Object-Based Classification , 2014, Remote. Sens..

[35]  Mikhail F. Kanevski,et al.  A Survey of Active Learning Algorithms for Supervised Remote Sensing Image Classification , 2011, IEEE Journal of Selected Topics in Signal Processing.

[36]  A. S. Belward,et al.  Who launched what, when and why; trends in global land-cover observation capacity from civilian earth observation satellites , 2015 .

[37]  Brian Johnson,et al.  Unsupervised image segmentation evaluation and refinement using a multi-scale approach , 2011 .

[38]  José M. C. Pereira,et al.  Optimal attributes for the object based detection of giant reed in riparian habitats: A comparative study between Airborne High Spatial Resolution and WorldView-2 imagery , 2014, Int. J. Appl. Earth Obs. Geoinformation.

[39]  Albert Rango,et al.  Texture and Scale in Object-Based Analysis of Subdecimeter Resolution Unmanned Aerial Vehicle (UAV) Imagery , 2009, IEEE Transactions on Geoscience and Remote Sensing.

[40]  L. Durieux,et al.  Advances in Geographic Object-Based Image Analysis with ontologies: A review of main contributions and limitations from a remote sensing perspective , 2013 .

[41]  Peng Gong,et al.  Integration of object-based and pixel-based classification for mapping mangroves with IKONOS imagery , 2004 .

[42]  Caiyun Zhang,et al.  Applying data fusion techniques for benthic habitat mapping and monitoring in a coral reef ecosystem , 2015 .

[43]  Timothy A. Warner,et al.  Assessing machine-learning algorithms and image- and lidar-derived variables for GEOBIA classification of mining and mine reclamation , 2015 .

[44]  Frieke Van Coillie,et al.  Feature selection by genetic algorithms in object-based classification of IKONOS imagery for forest mapping in Flanders, Belgium , 2007 .

[45]  Nikos Koutsias,et al.  Object-based classification using Quickbird imagery for delineating forest vegetation polygons in a Mediterranean test site , 2008 .

[46]  Jun Li,et al.  Improved hyperspectral image classification by active learning using pre-designed mixed pixels , 2016, Pattern Recognit..

[47]  C. Woodcock,et al.  Making better use of accuracy data in land change studies: Estimating accuracy and area and quantifying uncertainty using stratified estimation , 2013 .

[48]  Clement Atzberger,et al.  Self-Guided Segmentation and Classification of Multi-Temporal Landsat 8 Images for Crop Type Mapping in Southeastern Brazil , 2015, Remote. Sens..

[49]  Giles M. Foody,et al.  Feature Selection for Classification of Hyperspectral Data by SVM , 2010, IEEE Transactions on Geoscience and Remote Sensing.

[50]  Pedro Antonio Gutiérrez,et al.  Object-Based Image Classification of Summer Crops with Machine Learning Methods , 2014, Remote. Sens..

[51]  Peijun Li,et al.  Urban land cover classification from very high resolution imagery using spectral and invariant moment shape information , 2010 .

[52]  Biswajeet Pradhan,et al.  A comparative assessment between object and pixel-based classification approaches for land use/land cover mapping using SPOT 5 imagery , 2014 .

[53]  Hui Zhang,et al.  Image segmentation evaluation: A survey of unsupervised methods , 2008, Comput. Vis. Image Underst..

[54]  Albert Rango,et al.  International Journal of Applied Earth Observation and Geoinformation a Comparison of Three Feature Selection Methods for Object-based Classification of Sub-decimeter Resolution Ultracam-l Imagery , 2022 .

[55]  Iryna Dronova,et al.  Object-Based Image Analysis in Wetland Research: A Review , 2015, Remote. Sens..

[56]  Fei Deng,et al.  Integration of orthoimagery and lidar data for object-based urban thematic mapping using random forests , 2013 .

[57]  Alfred Stein,et al.  Use of Binary Partition Tree and energy minimization for object-based classification of urban land cover , 2015 .

[58]  Dongmei Chen,et al.  Change detection from remotely sensed images: From pixel-based to object-based approaches , 2013 .

[59]  Stefan W. Maier,et al.  Area-based and location-based validation of classified image objects , 2014, Int. J. Appl. Earth Obs. Geoinformation.

[60]  Javier X. Leon,et al.  Improving the synoptic mapping of coral reef geomorphology using object-based image analysis , 2011, Int. J. Geogr. Inf. Sci..

[61]  Tapas Ranjan Martha,et al.  Segment Optimization and Data-Driven Thresholding for Knowledge-Based Landslide Detection by Object-Based Image Analysis , 2011, IEEE Transactions on Geoscience and Remote Sensing.

[62]  Peter Hofmann,et al.  Defuzzification Strategies for Fuzzy Classifications of Remote Sensing Data , 2016, Remote. Sens..

[63]  A. Rango,et al.  Object-oriented image analysis for mapping shrub encroachment from 1937 to 2003 in southern New Mexico , 2004 .

[64]  Rhett L. Mohler,et al.  Identifying a suitable combination of classification technique and bandwidth(s) for burned area mapping in tallgrass prairie with MODIS imagery , 2012, Int. J. Appl. Earth Obs. Geoinformation.

[65]  Amy Loutfi,et al.  Classification and Segmentation of Satellite Orthoimagery Using Convolutional Neural Networks , 2016, Remote. Sens..

[66]  T. Blaschke,et al.  Object‐based land‐cover classification for the Phoenix metropolitan area: optimization vs. transportability , 2008 .

[67]  Lei Ma,et al.  Training set size, scale, and features in Geographic Object-Based Image Analysis of very high resolution unmanned aerial vehicle imagery , 2015 .

[68]  André Stumpf,et al.  Active Learning in the Spatial Domain for Remote Sensing Image Classification , 2014, IEEE Transactions on Geoscience and Remote Sensing.

[69]  Xia Li,et al.  A novel algorithm for land use and land cover classification using RADARSAT-2 polarimetric SAR data , 2012 .

[70]  Steven E. Franklin,et al.  Multi-scale object-based image analysis and feature selection of multi-sensor earth observation imagery using random forests , 2012 .

[71]  Volker Walter,et al.  Object-based classification of remote sensing data for change detection , 2004 .

[72]  Gherardo Chirici,et al.  A meta-analysis and review of the literature on the k-Nearest Neighbors technique for forestry applications that use remotely sensed data , 2016 .

[73]  Meghan Graham MacLean,et al.  MAP ACCURACY ASSESSMENT ISSUES WHEN USING AN OBJECT-ORIENTED APPROACH , 2012 .

[74]  P. Gong,et al.  Object-based Detailed Vegetation Classification with Airborne High Spatial Resolution Remote Sensing Imagery , 2006 .

[75]  Maycira Costa,et al.  Large-scale habitat mapping of the Brazilian Pantanal wetland: A synthetic aperture radar approach , 2014 .

[76]  Weifeng Li,et al.  Comparing Machine Learning Classifiers for Object-Based Land Cover Classification Using Very High Resolution Imagery , 2014, Remote. Sens..

[77]  Massimiliano Pittore,et al.  Performance Evaluation of Machine Learning Algorithms for Urban Pattern Recognition from Multi-spectral Satellite Images , 2014, Remote. Sens..

[78]  G. Hay,et al.  Special Issue on Geographic Object-Based Image Analysis (GEOBIA). , 2010 .

[79]  André Stumpf,et al.  bject-oriented mapping of urban trees using Random Forest lassifiers , 2013 .

[80]  Uwe Stilla,et al.  Machine Learning Comparison between WorldView-2 and QuickBird-2-Simulated Imagery Regarding Object-Based Urban Land Cover Classification , 2011, Remote. Sens..

[81]  D. Civco,et al.  Optimizing multi-resolution segmentation scale using empirical methods: Exploring the sensitivity of the supervised discrepancy measure Euclidean distance 2 (ED2) , 2014 .

[82]  Klaus Steinnocher,et al.  Influence of image fusion approaches on classification accuracy: a case study , 2006 .

[83]  Ni-Bin Chang,et al.  Mangrove Mapping and Change Detection in Ca Mau Peninsula, Vietnam, Using Landsat Data and Object-Based Image Analysis , 2015, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[84]  O. Csillik,et al.  Automated parameterisation for multi-scale image segmentation on multiple layers , 2014, ISPRS journal of photogrammetry and remote sensing : official publication of the International Society for Photogrammetry and Remote Sensing.

[85]  D. Moher,et al.  Preferred reporting items for systematic reviews and meta-analyses: the PRISMA Statement , 2009, BMJ : British Medical Journal.

[86]  Jorge Torres-Sánchez,et al.  Spatial Quality Evaluation of Resampled Unmanned Aerial Vehicle-Imagery for Weed Mapping , 2015, Sensors.

[87]  Li Manchun,et al.  Review of Remotely Sensed Imagery Classification Patterns Based on Object-oriented Image Analysis , 2006 .

[88]  Hugo Carrão,et al.  Combining per-pixel and object-based classifications for mapping land cover over large areas , 2014 .

[89]  María Pérez-Ortiz,et al.  Selecting patterns and features for between- and within- crop-row weed mapping using UAV-imagery , 2016, Expert Syst. Appl..

[90]  A. Smith,et al.  Image segmentation scale parameter optimization and land cover classification using the Random Forest algorithm , 2010 .

[91]  J. Chanussot,et al.  Hyperspectral Remote Sensing Data Analysis and Future Challenges , 2013, IEEE Geoscience and Remote Sensing Magazine.

[92]  Jong-Min Yeom,et al.  Effect of red-edge and texture features for object-based paddy rice crop classification using RapidEye multi-spectral satellite image data , 2014 .

[93]  Giorgos Mountrakis,et al.  A meta-analysis of remote sensing research on supervised pixel-based land-cover image classification processes: General guidelines for practitioners and future research , 2016 .

[94]  Fulgencio Cánovas-García,et al.  Optimal Combination of Classification Algorithms and Feature Ranking Methods for Object-Based Classification of Submeter Resolution Z/I-Imaging DMC Imagery , 2015, Remote. Sens..

[95]  Thomas Blaschke,et al.  A systematic comparison of different object-based classification techniques using high spatial resolution imagery in agricultural environments , 2016, Int. J. Appl. Earth Obs. Geoinformation.

[96]  Aniruddha Ghosh,et al.  A comparison of selected classification algorithms for mapping bamboo patches in lower Gangetic plains using very high resolution WorldView 2 imagery , 2014, Int. J. Appl. Earth Obs. Geoinformation.

[97]  Dirk Tiede,et al.  ESP: a tool to estimate scale parameter for multiresolution image segmentation of remotely sensed data , 2010, Int. J. Geogr. Inf. Sci..

[98]  Clemens Eisank,et al.  Automated object-based classification of topography from SRTM data , 2012, Geomorphology.

[99]  Julien Radoux,et al.  Accounting for the area of polygon sampling units for the prediction of primary accuracy assessment indices , 2014 .

[100]  K. Seto,et al.  A Meta-Analysis of Global Urban Land Expansion , 2011, PloS one.

[101]  Julien Radoux,et al.  Please Scroll down for Article International Journal of Geographical Information Science Thematic Accuracy Assessment of Geographic Object-based Image Classification Thematic Accuracy Assessment of Geographic Object-based Image Classification , 2022 .

[102]  Manuel A. Aguilar,et al.  GeoEye-1 and WorldView-2 pan-sharpened imagery for object-based classification in urban environments , 2013 .

[103]  Geoffrey J. Hay,et al.  Geographic Object-Based Image Analysis (GEOBIA): A new name for a new discipline , 2008 .

[104]  Jay Gao,et al.  Mapping of Land Degradation from ASTER Data: A Comparison of Object-Based and Pixel-Based Methods , 2008 .

[105]  André Stumpf,et al.  Object-oriented mapping of landslides using Random Forests , 2011 .

[106]  Jungho Im,et al.  Synergistic use of QuickBird multispectral imagery and LIDAR data for object-based forest species classification , 2010 .

[107]  Thomas Blaschke,et al.  Object based image analysis for remote sensing , 2010 .

[108]  Nicolas Lachiche,et al.  Comparison of sampling strategies for object-based classification of urban vegetation from Very High Resolution satellite images , 2016, Int. J. Appl. Earth Obs. Geoinformation.

[109]  Anthony Gar-On Yeh,et al.  Monthly short-term detection of land development using RADARSAT-2 polarimetric SAR imagery , 2015 .

[110]  P. Gong,et al.  Object-based analysis and change detection of major wetland cover types and their classification uncertainty during the low water period at Poyang Lake, China , 2011 .

[111]  Patricia Gober,et al.  Per-pixel vs. object-based classification of urban land cover extraction using high spatial resolution imagery , 2011, Remote Sensing of Environment.

[112]  P. Hofmann,et al.  Quantifying the robustness of fuzzy rule sets in object-based image analysis , 2011 .

[113]  William J. Emery,et al.  Object-Based Convolutional Neural Network for High-Resolution Imagery Classification , 2017, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[114]  D. Goodin,et al.  Mapping land cover and land use from object-based classification: an example from a complex agricultural landscape , 2015 .

[115]  Geoffrey J. Hay,et al.  How wetland type and area differ through scale: A GEOBIA case study in Alberta's Boreal Plains , 2012 .

[116]  Martin Hermy,et al.  External geo-information in the segmentation of VHR imagery improves the detection of imperviousness in urban neighborhoods , 2012, Int. J. Appl. Earth Obs. Geoinformation.

[117]  Sérgio Freire,et al.  Introducing mapping standards in the quality assessment of buildings extracted from very high resolution satellite imagery , 2014 .

[118]  Lindi J. Quackenbush,et al.  Impact of training and validation sample selection on classification accuracy and accuracy assessment when using reference polygons in object-based classification , 2013 .

[119]  Zheng Niu,et al.  Comparison of Hybrid Classifiers for Crop Classification Using Normalized Difference Vegetation Index Time Series: A Case Study for Major Crops in North Xinjiang, China , 2015, PloS one.

[120]  Christopher Conrad,et al.  Analysis of uncertainty in multi-temporal object-based classification , 2015 .

[121]  Paul M. Mather,et al.  Some issues in the classification of DAIS hyperspectral data , 2006 .

[122]  P. Atkinson,et al.  Uncertainty in remote sensing and GIS , 2002 .