Student or educator? Sign up with your school email and get 6 months of Pro free.
CoChat
Sign Up
Cited by 900

The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.

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

Open access
Ma, Lei, Li, Manchun, Ma, Xiaoxue, Cheng, Liang, Du, Peijun, Liu, Yongxue·2017·ISPRS Journal of Photogrammetry and Remote Sensing
DOI 10.1016/j.isprsjprs.2017.06.001
Open this paper in CoChat — free account

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.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1016/j.isprsjprs.2017.06.001

Cites

2 of 2 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Show all 2 references

Research works printed but not linked: 116

  1. Aguilar, 2013, GeoEye-1 and WorldView-2 pan-sharpened imagery for object-based classification in urban environments, Int. J. Remote Sens., 34, 2583, 10.1080/01431161.2012.747018 · DOI
  2. Arbiol, R., Zhang, Y., Palà, V., 2006. Advanced classification techniques: a review. In: ISPRS Commission VII Mid-term Symposium Remote Sensing: From Pixels to Processes, Enschede, pp. 292–296.
  3. Arvor, 2013, Advances in Geographic Object-Based Image Analysis with ontologies: a review of main contributions and limitations from a remote sensing perspective, ISPRS J. Photogramm. Remote Sens., 82, 125, 10.1016/j.isprsjprs.2013.05.003 · DOI
  4. Belward, 2015, Who launched what, when and why; trends in global land-cover observation capacity from civilian earth observation satellites, ISPRS J. Photogramm. Remote Sens., 103, 115, 10.1016/j.isprsjprs.2014.03.009 · DOI · Paper page
  5. Blaschke, 2010, Object based image analysis for remote sensing, ISPRS J. Photogramm. Remote Sens., 65, 2, 10.1016/j.isprsjprs.2009.06.004 · DOI · Paper page
  6. Blaschke, 2014, Geographic object-based image analysis – towards a new paradigm, ISPRS J. Photogramm. Remote Sens., 87, 180, 10.1016/j.isprsjprs.2013.09.014 · DOI · Paper page
  7. Blaschke, T., Strobl, J., 2001. What’s wrong with pixels? Some recent developments interfacing remote sensing and GIS. GeoBIT/GIS 6(1), 12–17.
  8. Borra-Serrano, 2015, Spatial quality evaluation of resampled unmanned aerial vehicle-imagery for weed mapping, Sensors, 15, 19688, 10.3390/s150819688 · DOI
  9. Cánovas-García, 2015, Optimal combination of classification algorithms and feature ranking methods for object-based classification of submeter resolution Z/I-Imaging DMC imagery, Remote Sens., 7, 4651, 10.3390/rs70404651 · DOI
  10. Charoenjit, 2015, Estimation of biomass and carbon stock in Para rubber plantations using object-based classification from Thaichote satellite data in Eastern Thailand, J. Appl. Remote Sens., 9, 096072, 10.1117/1.JRS.9.096072 · DOI
  11. Chirici, 2016, A meta-analysis and review of the literature on the k-Nearest Neighbors technique for forestry applications that use remotely sensed data, Remote Sens. Environ., 176, 282, 10.1016/j.rse.2016.02.001 · DOI
  12. Cleve, 2008, Classification of the wildland–urban interface: a comparison of pixel- and object-based classifications using high-resolution aerial photography, Comput. Environ. Urban Syst., 32, 317, 10.1016/j.compenvurbsys.2007.10.001 · DOI
  13. Van Coillie, 2007, Feature selection by genetic algorithms in object-based classification of IKONOS imagery for forest mapping in Flanders, Belgium, Remote Sens. Environ., 110, 476, 10.1016/j.rse.2007.03.020 · DOI
  14. Colditz, 2006, Influence of image fusion approaches on classification accuracy – a case study, Int. J. Remote Sens., 27, 3311, 10.1080/01431160600649254 · DOI
  15. Corcoran, 2015, The effects of point or polygon based training data on RandomForest classification accuracy of wetlands, Remote Sens., 7, 4002, 10.3390/rs70404002 · DOI
  16. Costa, 2014, Combining per-pixel and object-based classifications for mapping land cover over large areas, Int. J. Remote Sens., 35, 738, 10.1080/01431161.2013.873151 · DOI
  17. Drǎguţ, 2014, Automated parameterisation for multi-scale image segmentation on multiple layers, ISPRS J. Photogramm. Remote Sens., 88, 119, 10.1016/j.isprsjprs.2013.11.018 · DOI · Paper page
  18. Drǎguţ, 2012, Automated object-based classification of topography from SRTM data, Geomorphology, 141, 21, 10.1016/j.geomorph.2011.12.001 · DOI
  19. Drǎguţ, 2010, ESP: a tool to estimate scale parameter for multiresolution image segmentation of remotely sensed data, Int. J. Geogr. Inf. Sci., 24, 859, 10.1080/13658810903174803 · DOI
  20. Dronova, 2015, Object-based image analysis in wetland research: a review, Remote Sens., 7, 6380, 10.3390/rs70506380 · DOI · Paper page
  21. Dronova, 2011, Object-based analysis and change detection of major wetland cover types and their classification uncertainty during the low water period at Poyang Lake, China, Remote Sens. Environ., 115, 3220, 10.1016/j.rse.2011.07.006 · DOI · Paper page
  22. Du, 2015, Semantic classification of urban buildings combining VHR image and GIS data: an improved random forest approach, ISPRS J. Photogramm. Remote Sens., 105, 107, 10.1016/j.isprsjprs.2015.03.011 · DOI
  23. Đurić, 2014, Application of in-segment multiple sampling in object-based classification, Remote Sens., 6, 12138, 10.3390/rs61212138 · DOI
  24. Duro, 2012, 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, Remote Sens. Environ., 118, 259, 10.1016/j.rse.2011.11.020 · DOI · Paper page
  25. Duro, 2012, Multi-scale object-based image analysis and feature selection of multi-sensor earth observation imagery using random forests, Int. J. Remote Sens., 33, 4502, 10.1080/01431161.2011.649864 · DOI
  26. Espindola, 2006, Parameter selection for region-growing image segmentation algorithms using spatial autocorrelation, Int. J. Remote Sens., 27, 3035, 10.1080/01431160600617194 · DOI
  27. Evans, 2014, Large-scale habitat mapping of the Brazilian Pantanal wetland: a synthetic aperture radar approach, Remote Sens. Environ., 155, 89, 10.1016/j.rse.2013.08.051 · DOI
  28. Fernandes, 2014, 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, Int. J. Appl. Earth Obs. Geoinf., 32, 79, 10.1016/j.jag.2014.03.026 · DOI
  29. Fisher, 2010, Remote sensing of land cover classes as type 2 fuzzy sets, Remote Sens. Environ., 114, 309, 10.1016/j.rse.2009.09.004 · DOI
  30. Freire, 2014, Introducing mapping standards in the quality assessment of buildings extracted from very high resolution satellite imagery, ISPRS J. Photogramm. Remote Sens., 90, 1, 10.1016/j.isprsjprs.2013.12.009 · DOI
  31. Gao, 2008, Mapping of land degradation from ASTER data: a comparison of object-based and pixel-based methods, Gisci. Remote Sens., 45, 149, 10.2747/1548-1603.45.2.149 · DOI
  32. Gavazzi, 2016, Evaluation of seabed mapping methods for fine-scale classification of extremely shallow benthic habitats – application to the Venice Lagoon, Italy, Estuarine Coast. Shelf Sci., 170, 45, 10.1016/j.ecss.2015.12.014 · DOI
  33. Ghosh, 2014, A comparison of selected classification algorithms for mapping bamboo patches in lower Gangetic plains using very high resolution WorldView 2 imagery, Int. J. Appl. Earth Obs. Geoinf., 26, 298, 10.1016/j.jag.2013.08.011 · DOI
  34. Goodin, 2015, Mapping land cover and land use from object-based classification: an example from a complex agricultural landscape, Int. J. Remote Sens., 36, 4702, 10.1080/01431161.2015.1088674 · DOI
  35. Hofmann, 2016, Defuzzification strategies for fuzzy classifications of remote sensing data, Remote Sens., 8, 467, 10.3390/rs8060467 · DOI
  36. Hofmann, 2011, Quantifying the robustness of fuzzy rule sets in object-based image analysis, Int. J. Remote Sens., 32, 7359, 10.1080/01431161.2010.523727 · DOI
  37. Melgani, 2004, Classification of hyperspectral remote sensing images with support vector machines, IEEE Trans. Geosci. Remote Sens., 42, 1778, 10.1109/TGRS.2004.831865 · DOI · Paper page
  38. Guan, 2013, Integration of orthoimagery and lidar data for object-based urban thematic mapping using random forests, Int. J. Remote Sens., 34, 5166, 10.1080/01431161.2013.788261 · DOI
  39. Hao, 2015, Comparison of hybrid classifiers for crop classification using normalized difference vegetation index time series: a case study for major crops in North Xinjiang, China, Plos One, 10, e0137748, 10.1371/journal.pone.0137748 · DOI
  40. Hay, 2010, Special issue: geographic object-based image analysis (GEOBIA), Photogramm. Eng. Remote Sens., 76, 121
  41. Hussain, 2013, Change detection from remotely sensed images: from pixel-based to object-based approaches, ISPRS J. Photogramm. Remote Sens., 80, 91, 10.1016/j.isprsjprs.2013.03.006 · DOI · Paper page
  42. Johnson, 2011, Unsupervised image segmentation evaluation and refinement using a multi-scale approach, ISPRS J. Photogramm. Remote Sens., 66, 473, 10.1016/j.isprsjprs.2011.02.006 · DOI · Paper page
  43. Ke, 2010, Synergistic use of QuickBird multispectral imagery and LIDAR data for object-based forest species classification, Remote Sens. Environ., 114, 1141, 10.1016/j.rse.2010.01.002 · DOI · Paper page
  44. Khatami, 2016, A meta-analysis of remote sensing research on supervised pixel-based land-cover image classification processes: general guidelines for practitioners and future research, Remote Sens. Environ., 177, 89, 10.1016/j.rse.2016.02.028 · DOI · Paper page
  45. Kim, 2014, Effect of red-edge and texture features for object-based paddy rice crop classification using RapidEye multi-spectral satellite image data, Int. J. Remote Sens., 35, 7046
  46. Kim, 2011, Multi-scale GEOBIA with very high spatial resolution digital aerial imagery: scale, texture and image objects, Int. J. Remote Sens., 32, 2825, 10.1080/01431161003745608 · DOI
  47. Längkvist, 2016, Classification and segmentation of satellite orthoimagery using convolutional neural networks, Remote Sens., 8, 329, 10.3390/rs8040329 · DOI · Paper page
  48. Löw, 2015, Analysis of uncertainty in multi-temporal object-based classification, ISPRS J. Photogramm. Remote Sens., 105, 91, 10.1016/j.isprsjprs.2015.03.004 · DOI
  49. Laliberte, A., Koppa, J., Fredrickson, E., Rango, A., 2006. Comparison of nearest neighbor and rule-based decision tree classification in an object-oriented environment. In: Geoscience and Remote Sensing Symposium Proceedings (IGARSS), Denver, 2006 IEEE International, July, pp. 3923–3926. · DOI
  50. Laliberte, 2012, A comparison of three feature selection methods for object-based classification of sub-decimeter resolution UltraCam-L imagery, Int. J. Appl. Earth Obs. Geoinf., 15, 70, 10.1016/j.jag.2011.05.011 · DOI
  51. Laliberte, 2009, Texture and scale in object-based analysis of subdecimeter resolution Unmanned Aerial Vehicle (UAV) imagery, IEEE Trans. Geosci. Remote Sens., 47, 761, 10.1109/TGRS.2008.2009355 · DOI · Paper page
  52. Laliberte, 2004, Object-oriented image analysis for mapping shrub encroachment from 1937 to 2003 in southern New Mexico, Remote Sens. Environ., 93, 198, 10.1016/j.rse.2004.07.011 · DOI · Paper page
  53. Langner, 2014, Spectral normalization of SPOT 4 data to adjust for changing leaf phenology within seasonal forests in Cambodia, Remote Sens. Environ., 143, 122, 10.1016/j.rse.2013.12.012 · DOI
  54. Leon, 2011, Improving the synoptic mapping of coral reef geomorphology using object-based image analysis, Int. J. Geogr. Inf. Sci., 25, 949, 10.1080/13658816.2010.513980 · DOI
  55. Li, 2015, Use of Binary Partition Tree and energy minimization for object-based classification of urban land cover, ISPRS J. Photogramm. Remote Sens., 102, 48, 10.1016/j.isprsjprs.2014.12.023 · DOI
  56. Li, 2015, Identification of forested landslides using LiDar data, object-based image analysis, and machine learning algorithms, Remote Sens., 7, 9705, 10.3390/rs70809705 · DOI
  57. Li, 2016, A systematic comparison of different object-based classification techniques using high spatial resolution imagery in agricultural environments, Int. J. Appl. Earth Obs. Geoinf., 49, 87, 10.1016/j.jag.2016.01.011 · DOI
  58. Liu, 2006, Review of remotely sensed imagery classification patterns based on object-oriented image analysis, Chin. Geogr. Sci., 16, 282, 10.1007/s11769-006-0282-0 · DOI
  59. Loosvelt, 2012, Random Forests as a tool for estimating uncertainty at pixel-level in SAR image classification, Int. J. Appl. Earth Obs. Geoinf., 19, 173, 10.1016/j.jag.2012.05.011 · DOI
  60. Ma, 2014, Cultivated land information extraction from high-resolution unmanned aerial vehicle imagery data, J. Appl. Remote Sens., 8, 1, 10.1117/1.JRS.8.083673 · DOI
  61. Ma, 2015, Training set size, scale, and features in Geographic Object-Based Image Analysis of very high resolution unmanned aerial vehicle imagery, ISPRS J. Photogramm. Remote Sens., 102, 14, 10.1016/j.isprsjprs.2014.12.026 · DOI
  62. Ma, 2017, Evaluation of feature selection methods for object-based land cover mapping of Unmanned Aerial Vehicle imagery using random forest and support vector machine classifiers, ISPRS Int. J. Geo-Inf., 6, 51, 10.3390/ijgi6020051 · DOI
  63. Maclean, M.G., Congalton, R.G., 2012. Map accuracy assessment issues when using an object-oriented approach. In: ASPRS 2012 Annual Conference, Sacramento, CA, 19–23 March. <http://info.asprs.org/publications/proceedings/Sacramento2012/files/MacLean.pdf>.
  64. Mallinis, 2008, Object-based classification using Quickbird imagery for delineating forest vegetation polygons in a Mediterranean test site, ISPRS J. Photogramm. Remote Sens., 63, 237, 10.1016/j.isprsjprs.2007.08.007 · DOI · Paper page
  65. Martha, 2011, Segment optimization and data-driven thresholding for knowledge-based landslide detection by object-based image analysis, IEEE Trans. Geosci. Remote Sens., 49, 4928, 10.1109/TGRS.2011.2151866 · DOI · Paper page
  66. Maxwell, 2015, Assessing machine-learning algorithms and image-and lidar-derived variables for GEOBIA classification of mining and mine reclamation, Int. J. Remote Sens., 36, 954, 10.1080/01431161.2014.1001086 · DOI
  67. Mohler, 2012, Identifying a suitable combination of classification technique and bandwidth(s) for burned area mapping in tallgrass prairie with MODIS imagery, Int. J. Appl. Earth Obs. Geoinf., 14, 103, 10.1016/j.jag.2011.08.008 · DOI
  68. Myint, 2011, Per-pixel vs. object-based classification of urban land cover extraction using high spatial resolution imagery, Remote Sens. Environ., 115, 1145, 10.1016/j.rse.2010.12.017 · DOI · Paper page
  69. Novack, 2011, Machine learning comparison between WorldView-2 and QuickBird-2-simulated imagery regarding object-based urban land cover classification, Remote Sens., 3, 2263, 10.3390/rs3102263 · DOI
  70. O Connell, 2015, Wide-area mapping of small-scale features in agricultural landscapes using airborne remote sensing, ISPRS J. Photogramm. Remote Sens., 109, 165, 10.1016/j.isprsjprs.2015.09.007 · DOI
  71. Olofsson, 2013, Making better use of accuracy data in land change studies: estimating accuracy and area and quantifying uncertainty using stratified estimation, Remote Sens. Environ., 129, 122, 10.1016/j.rse.2012.10.031 · DOI · Paper page
  72. Pérez-Ortiz, 2016, Selecting patterns and features for between- and within- crop-row weed mapping using UAV-imagery, Expert Syst. Appl., 47, 85, 10.1016/j.eswa.2015.10.043 · DOI
  73. Pal, 2010, Feature selection for classification of hyperspectral data by SVM, IEEE Trans. Geosci. Remote Sens., 48, 2297, 10.1109/TGRS.2009.2039484 · DOI · Paper page
  74. Pal, 2006, Some issues in the classification of DAIS hyperspectral data, Int. J. Remote Sens., 105, 2895, 10.1080/01431160500185227 · DOI
  75. Peña-Barragán, 2011, Object-based crop identification using multiple vegetation indices, textural features and crop phenology, Remote Sens. Environ., 115, 1301, 10.1016/j.rse.2011.01.009 · DOI · Paper page
  76. Peña, 2014, Object-based image classification of summer crops with machine learning methods, Remote Sens., 6, 5019, 10.3390/rs6065019 · DOI
  77. Pereira Júnior, 2014, Modelling fire frequency in a cerrado savanna protected area, PLoS ONE, 9, e102380, 10.1371/journal.pone.0102380 · DOI
  78. Powers, 2012, How wetland type and area differ through scale: a GEOBIA case study in Alberta's Boreal Plains, Remote Sens. Environ., 117, 135, 10.1016/j.rse.2011.07.009 · DOI
  79. Pu, 2012, A comparative analysis of high spatial resolution IKONOS and WorldView-2 imagery for mapping urban tree species, Remote Sens. Environ., 124, 516, 10.1016/j.rse.2012.06.011 · DOI
  80. Puissant, 2014, Object-oriented mapping of urban trees using Random Forest classifiers, Int. J. Appl. Earth Obs. Geoinf., 26, 235, 10.1016/j.jag.2013.07.002 · DOI
  81. Qi, 2012, A novel algorithm for land use and land cover classification using RADARSAT-2 polarimetric SAR data, Remote Sens. Environ., 118, 21, 10.1016/j.rse.2011.11.001 · DOI
  82. Qi, 2015, Monthly short-term detection of land development using RADARSAT-2 polarimetric SAR imagery, Remote Sens. Environ., 164, 179, 10.1016/j.rse.2015.04.018 · DOI
  83. Qian, 2014, Comparing machine learning classifiers for object-based land cover classification using very high resolution imagery, Remote Sens., 7, 153, 10.3390/rs70100153 · DOI · Paper page
  84. Räsänen, 2013, What makes segmentation good? A case study in boreal forest habitat mapping, Int. J. Remote Sens., 34, 8603, 10.1080/01431161.2013.845318 · DOI
  85. Radoux, 2014, Accounting for the area of polygon sampling units for the prediction of primary accuracy assessment indices, Remote Sens. Environ., 142, 9, 10.1016/j.rse.2013.10.030 · DOI
  86. Radoux, 2011, Thematic accuracy assessment of geographic object-based image classification, Int. J. Geogr. Inf. Sci., 25, 895, 10.1080/13658816.2010.498378 · DOI
  87. Recio, 2013, Landscape resource mapping for wildlife research using very high resolution satellite imagery, Methods Ecol. Evol., 4, 982, 10.1111/2041-210X.12094 · DOI
  88. Rougier, 2016, Comparison of sampling strategies for object-based classification of urban vegetation from Very High Resolution satellite images, Int. J. Appl. Earth Obs. Geoinf., 51, 60, 10.1016/j.jag.2016.04.005 · DOI
  89. Samat, 2015, Improved hyperspectral image classification by active learning using pre-designed mixed pixels, Pattern Recognit., 51, 43, 10.1016/j.patcog.2015.08.019 · DOI
  90. Schultz, 2015, Self-guided segmentation and classification of multi-temporal Landsat 8 images for crop type mapping in Southeastern Brazil, Remote Sens., 7, 14482, 10.3390/rs71114482 · DOI
  91. Seto, 2011, A meta-analysis of global urban land expansion, PLoS ONE, 6, e23777, 10.1371/journal.pone.0023777 · DOI · Paper page
  92. Smith, 2010, Image segmentation scale parameter optimization and land cover classification using the Random Forest algorithm, Spat. Sci., 55, 69, 10.1080/14498596.2010.487851 · DOI
  93. Son, 2015, Mangrove mapping and change detection in Ca Mau Peninsula, Vietnam, using Landsat data and object-based image analysis, IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., 8, 503, 10.1109/JSTARS.2014.2360691 · DOI
  94. Stehman, 2011, Pixels, blocks of pixels, and polygons: choosing a spatial unit for thematic accuracy assessment, Remote Sens. Environ., 115, 3044, 10.1016/j.rse.2011.06.007 · DOI
  95. Stumpf, 2011, Object-oriented mapping of landslides using Random Forests, Remote Sens. Environ., 115, 2564, 10.1016/j.rse.2011.05.013 · DOI · Paper page
  96. Stumpf, 2014, Active learning in the spatial domain for remote sensing image classification, IEEE Trans. Geosci. Remote Sens., 52, 2492, 10.1109/TGRS.2013.2262052 · DOI
  97. Tehrany, 2014, A comparative assessment between object and pixel-based classification approaches for land use/land cover mapping using SPOT 5 imagery, Geocarto Int., 29, 351, 10.1080/10106049.2013.768300 · DOI
  98. Tuia, 2011, A survey of active learning algorithms for supervised remote sensing image classification, IEEE J. Sel. Top. Signal Process., 5, 606, 10.1109/JSTSP.2011.2139193 · DOI · Paper page
  99. Verbeeck, 2012, External geo-information in the segmentation of VHR imagery improves the detection of imperviousness in urban neighborhoods, Int. J. Appl. Earth Obs. Geoinf., 18, 428, 10.1016/j.jag.2012.03.015 · DOI
  100. Vieira, 2012, Object Based Image Analysis and Data Mining applied to a remotely sensed Landsat time-series to map sugarcane over large areas, Remote Sens. Environ., 123, 553, 10.1016/j.rse.2012.04.011 · DOI
  101. Walter, 2004, Object-based classification of remote sensing data for change detection, ISPRS J. Photogramm. Remote Sens., 58, 225, 10.1016/j.isprsjprs.2003.09.007 · DOI · Paper page
  102. Walker, 2008, Object-based land-cover classification for the Phoenix metropolitan area: optimization vs. transportability, Int. J. Remote Sens., 29, 2021, 10.1080/01431160701408337 · DOI
  103. Wang, 2004, Integration of object-based and pixel-based classification for mapping mangroves with IKONOS imagery, Int. J. Remote Sens., 25, 5655, 10.1080/014311602331291215 · DOI
  104. Weston, 2000, Feature selection for SVMs, Adv. Neural. Inf. Process. Syst., 13, 668
  105. Whiteside, 2014, Area-based and location-based validation of classified image objects, Int. J. Appl. Earth Obs. Geoinf., 28, 117, 10.1016/j.jag.2013.11.009 · DOI
  106. Wieland, 2014, Performance evaluation of machine learning algorithms for urban pattern recognition from multi-spectral satellite images, Remote Sens., 6, 2912, 10.3390/rs6042912 · DOI
  107. Witharana, 2014, Optimizing multi-resolution segmentation scale using empirical methods: exploring the sensitivity of the supervised discrepancy measure Euclidean distance 2 (ED2), ISPRS J. Photogramm. Remote Sens., 87, 108, 10.1016/j.isprsjprs.2013.11.006 · DOI
  108. Xu, 2010, Urban land cover classification from very high resolution imagery using spectral and invariant moment shape information, Can. J. Remote. Sens., 36, 248, 10.5589/m10-042 · DOI
  109. Yan, 2006, Comparison of pixel-based and object-oriented image classification approaches – a case study in a coal fire area, Wuda, Inner Mongolia, China, Int. J. Remote Sens., 27, 4039, 10.1080/01431160600702632 · DOI · Paper page
  110. Yu, 2006, Object-based detailed vegetation classification with airborne high spatial resolution remote sensing imagery, Photogramm. Eng. Remote Sens., 72, 799, 10.14358/PERS.72.7.799 · DOI · Paper page
  111. Zhan, 2005, Quality assessment for geo-spatial objects derived from remotely sensed data, Int. J. Remote Sens., 26, 2953, 10.1080/01431160500057764 · DOI
  112. Zhang, 2015, Applying data fusion techniques for benthic habitat mapping and monitoring in a coral reef ecosystem, ISPRS J. Photogramm. Remote Sens., 104, 213, 10.1016/j.isprsjprs.2014.06.005 · DOI
  113. Zhang, 2008, Image segmentation evaluation: a survey of unsupervised methods, Comput. Vision Image Understanding, 110, 260, 10.1016/j.cviu.2007.08.003 · DOI · Paper page
  114. Zhen, 2013, Impact of training and validation sample selection on classification accuracy and accuracy assessment when using reference polygons in object-based classification, Int. J. Remote Sens., 34, 6914, 10.1080/01431161.2013.810822 · DOI
  115. Zhao, W., Du, S., Emery, W.J., 2017. Object-based convolutional neural network for high-resolution imagery classification. IEEE J. Sel. Top. Appl. Earth Obs. Rem. Sens. PP(99), 1–11.
  116. Zolkos, 2013, A meta-analysis of terrestrial aboveground biomass estimation using lidar remote sensing, Remote Sens. Environ., 128, 289, 10.1016/j.rse.2012.10.017 · DOI · Paper page

Cited by

Metadata

Authors
Ma, L., Li, M., Ma, X., Cheng, L., Du, P., & Liu, Y.
Venue
ISPRS Journal of Photogrammetry and Remote Sensing
Publisher
Elsevier BV
Published
2017-08
Volume
130
Pages
277-293
ISSN
0924-2716 (ISSN-L)
DOI
10.1016/j.isprsjprs.2017.06.001
OpenAlex
W2648242067
Cited by
900
References
2
Type
paratext
Language
en
Open access
hybrid
Licence
Funders
  • China Postdoctoral Science Foundation (2016M600392)
  • Program B for Outstanding PhD Candidate of Nanjing University (201502B008)
  • Special Research Fund of the Ministry of Land and Resources for NonProfit Sector (201411014-03)
  • Fundamental Research Funds for the Central Universities (020914380031)

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.