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A survey on object detection in optical remote sensing images

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Cheng, Gong, Han, Junwei·2016·ISPRS Journal of Photogrammetry and Remote Sensing

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  1. Ahmadi, 2010, Automatic urban building boundary extraction from high resolution aerial images using an innovative model of active contours, Int. J. Appl. Earth Observ. Geoinform., 12, 150, 10.1016/j.jag.2010.02.001 · DOI
  2. Ardila, 2012, Context-sensitive extraction of tree crown objects in urban areas using VHR satellite images, Int. J. Appl. Earth Obs. Geoinf., 15, 57
  3. Ari, 2014, Detection of compound structures using a Gaussian mixture model with spectral and spatial constraints, IEEE Trans. Geosci. Remote Sens., 52, 6627, 10.1109/TGRS.2014.2299540 · DOI
  4. Aytekın, 2012, Unsupervised building detection in complex urban environments from multispectral satellite imagery, Int. J. Remote Sens., 33, 2152, 10.1080/01431161.2011.606852 · DOI
  5. Aytekin, 2013, Texture-based airport runway detection, IEEE Geosci. Remote Sens. Lett., 10, 471, 10.1109/LGRS.2012.2210189 · DOI
  6. Bai, 2014, VHR object detection based on structural feature extraction and query expansion, IEEE Trans. Geosci. Remote Sens., 52, 6508, 10.1109/TGRS.2013.2296782 · DOI
  7. Baker, 2013, Does spatial resolution matter? A multi-scale comparison of object-based and pixel-based methods for detecting change associated with gas well drilling operations, Int. J. Remote Sens., 34, 1633, 10.1080/01431161.2012.724540 · DOI
  8. Baltsavias, 2004, Object extraction and revision by image analysis using existing geodata and knowledge: current status and steps towards operational systems, ISPRS J. Photogramm. Remote Sens., 58, 129, 10.1016/j.isprsjprs.2003.09.002 · DOI
  9. Barsi, 2003, Artificial neural networks for the detection of road junctions in aerial images, Int. Arch. Photogramm. Remote Sens. Spatial Inform. Sci., 34, 113
  10. Barzohar, 1996, Automatic finding of main roads in aerial images by using geometric-stochastic models and estimation, IEEE Trans. Pattern Anal. Mach. Intell., 18, 707, 10.1109/34.506793 · DOI
  11. Benedek, 2012, Building development monitoring in multitemporal remotely sensed image pairs with stochastic birth-death dynamics, IEEE Trans. Pattern Anal. Mach. Intell., 34, 33, 10.1109/TPAMI.2011.94 · DOI
  12. Benedek, 2015, Multilayer Markov Random Field models for change detection in optical remote sensing images, ISPRS J. Photogramm. Remote Sens., 107, 22, 10.1016/j.isprsjprs.2015.02.006 · DOI
  13. Benz, 2004, Multi-resolution, object-oriented fuzzy analysis of remote sensing data for GIS-ready information, ISPRS J. Photogramm. Remote Sens., 58, 239, 10.1016/j.isprsjprs.2003.10.002 · DOI · Paper page
  14. Bhagavathy, 2006, Modeling and detection of geospatial objects using texture motifs, IEEE Trans. Geosci. Remote Sens., 44, 3706, 10.1109/TGRS.2006.881741 · DOI
  15. Bi, 2012, A visual search inspired computational model for ship detection in optical satellite images, IEEE Geosci. Remote Sens. Lett., 9, 749, 10.1109/LGRS.2011.2180695 · DOI
  16. Blanzieri, 2008, Nearest neighbor classification of remote sensing images with the maximal margin principle, IEEE Trans. Geosci. Remote Sens., 46, 1804, 10.1109/TGRS.2008.916090 · DOI · Paper page
  17. 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
  18. 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
  19. Blaschke, 2011, Collective sensing: integrating geospatial technologies to understand urban systems—an overview, Remote Sens., 3, 1743, 10.3390/rs3081743 · DOI
  20. Bontemps, 2008, An object-based change detection method accounting for temporal dependences in time series with medium to coarse spatial resolution, Remote Sens. Environ., 112, 3181, 10.1016/j.rse.2008.03.013 · DOI
  21. Bovolo, 2008, A novel approach to unsupervised change detection based on a semisupervised SVM and a similarity measure, IEEE Trans. Geosci. Remote Sens., 46, 2070, 10.1109/TGRS.2008.916643 · DOI
  22. Capobianco, 2009, Target detection with semisupervised kernel orthogonal subspace projection, IEEE Trans. Geosci. Remote Sens., 47, 3822, 10.1109/TGRS.2009.2020910 · DOI
  23. Chaudhuri, 2012, Semi-automated road detection from high resolution satellite images by directional morphological enhancement and segmentation techniques, IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens., 5, 1538, 10.1109/JSTARS.2012.2199085 · DOI
  24. Chaudhuri, 2008, An automatic bridge detection technique for multispectral images, IEEE Trans. Geosci. Remote Sens., 46, 2720, 10.1109/TGRS.2008.923631 · DOI
  25. Chen, 2012, Object-based change detection, Int. J. Remote Sens., 33, 4434, 10.1080/01431161.2011.648285 · DOI · Paper page
  26. Chen, 2011, Hyperspectral image classification using dictionary-based sparse representation, IEEE Trans. Geosci. Remote Sens., 49, 3973, 10.1109/TGRS.2011.2129595 · DOI · Paper page
  27. Chen, 2011, Simultaneous joint sparsity model for target detection in hyperspectral imagery, IEEE Geosci. Remote Sens. Lett., 8, 676, 10.1109/LGRS.2010.2099640 · DOI
  28. Chen, 2011, Sparse representation for target detection in hyperspectral imagery, IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens., 5, 629
  29. Chen, 2013, Hyperspectral image classification via kernel sparse representation, IEEE Trans. Geosci. Remote Sens., 51, 217, 10.1109/TGRS.2012.2201730 · DOI · Paper page
  30. Cheng, 2013, Automatic landslide detection from remote-sensing imagery using a scene classification method based on BoVW and pLSA, Int. J. Remote Sens., 34, 45, 10.1080/01431161.2012.705443 · DOI · Paper page
  31. Cheng, 2015, Effective and efficient midlevel visual elements-oriented land-use classification using VHR remote sensing images, IEEE Trans. Geosci. Remote Sens., 53, 4238, 10.1109/TGRS.2015.2393857 · DOI · Paper page
  32. Cheng, 2013, Object detection in remote sensing imagery using a discriminatively trained mixture model, ISPRS J. Photogramm. Remote Sens., 85, 32, 10.1016/j.isprsjprs.2013.08.001 · DOI
  33. Cheng, 2015, Auto-encoder-based shared mid-level visual dictionary learning for scene classification using very high resolution remote sensing images, IET Comput. Vision, 9, 639, 10.1049/iet-cvi.2014.0270 · DOI
  34. Clinton, 2010, Accuracy assessment measures for object-based image segmentation goodness, Photogramm. Eng. Remote Sens., 76, 289, 10.14358/PERS.76.3.289 · DOI · Paper page
  35. Contreras, 2015, Monitoring recovery after earthquakes through the integration of remote sensing, GIS, and ground observations: the case of L’Aquila (Italy), Cartogr. Geogr. Inform. Sci., 43, 115, 10.1080/15230406.2015.1029520 · DOI
  36. Contreras, 2016, Monitoring recovery after earthquakes through the integration of remote sensing, GIS, and ground observations: the case of L’Aquila (Italy), Cartogr. Geogr. Inform. Sci., 43, 115, 10.1080/15230406.2015.1029520 · DOI
  37. Corbane, 2010, A complete processing chain for ship detection using optical satellite imagery, Int. J. Remote Sens., 31, 5837, 10.1080/01431161.2010.512310 · DOI
  38. Cover, 1967, Nearest neighbor pattern classification, IEEE Trans. Inf. Theory, 13, 21, 10.1109/TIT.1967.1053964 · DOI · Paper page
  39. Cramer, 2010, The DGPF test on digital airborne camera evaluation–overview and test design, Photogramm. Eng. Remote Sens., 2010, 73
  40. D’Oleire-Oltmanns, 2014, Detection of gully-affected areas by applying object-based image analysis (OBIA) in the region of Taroudannt, Morocco, Remote Sens., 6, 8287, 10.3390/rs6098287 · DOI
  41. Das, 2011, Use of salient features for the design of a multistage framework to extract roads from high-resolution multispectral satellite images, IEEE Trans. Geosci. Remote Sens., 49, 3906, 10.1109/TGRS.2011.2136381 · DOI
  42. De Morsier, 2013, Semi-supervised novelty detection using svm entire solution path, IEEE Trans. Geosci. Remote Sens., 51, 1939, 10.1109/TGRS.2012.2236683 · DOI
  43. De Pinho, 2012, Land-cover classification of an intra-urban environment using high-resolution images and object-based image analysis, Int. J. Remote Sens., 33, 5973, 10.1080/01431161.2012.675451 · DOI
  44. Deselaers, 2012, Weakly supervised localization and learning with generic knowledge, Int. J. Comput. Vis., 100, 275, 10.1007/s11263-012-0538-3 · DOI · Paper page
  45. Dissanska, 2009, Object-based classification of very high resolution panchromatic images for evaluating recent change in the structure of patterned peatland, Can. J. Remote Sens., 35, 189, 10.5589/m09-002 · DOI
  46. Doleire-Oltmanns, 2013, An object-based workflow to extract landforms at multiple scales from two distinct data types, IEEE Geosci. Remote Sens. Lett., 10, 947, 10.1109/LGRS.2013.2254465 · DOI
  47. Dong, 2015, Target detection based on random forest metric learning, IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens., 8, 1830, 10.1109/JSTARS.2015.2416255 · DOI
  48. Doxani, 2012, Monitoring urban changes based on scale-space filtering and object-oriented classification, Int. J. Appl. Earth Obs. Geoinf., 15, 38
  49. Doxani, 2008, An object-oriented approach to urban land cover change detection, Int. Arch. Photogramm. Remote Sens. Spat. Inform. Sci., 37, 1655
  50. Drăguţ, 2006, Automated classification of landform elements using object-based image analysis, Geomorphology, 81, 330, 10.1016/j.geomorph.2006.04.013 · DOI · Paper page
  51. Drăguţ, 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
  52. Drăguţ, 2012, Automated object-based classification of topography from SRTM data, Geomorphology, 141, 21, 10.1016/j.geomorph.2011.12.001 · DOI
  53. Drăguţ, 2010, ESP: a tool to estimate scale parameter for multiresolution image segmentation of remotely sensed data, Int. J. Geogr. Inform. Sci., 24, 859, 10.1080/13658810903174803 · DOI
  54. Du, 2014, A discriminative metric learning based anomaly detection method, IEEE Trans. Geosci. Remote Sens., 52, 6844, 10.1109/TGRS.2014.2303895 · DOI · Paper page
  55. Dudani, 1976, The distance-weighted k-nearest-neighbor rule, IEEE Trans. Syst., Man, Cybern., 325, 10.1109/TSMC.1976.5408784 · DOI · Paper page
  56. Durieux, 2008, A method for monitoring building construction in urban sprawl areas using object-based analysis of Spot 5 images and existing GIS data, ISPRS J. Photogramm. Remote Sens., 63, 399, 10.1016/j.isprsjprs.2008.01.005 · DOI
  57. 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
  58. Eikvil, 2009, Classification-based vehicle detection in high-resolution satellite images, ISPRS J. Photogramm. Remote Sens., 64, 65, 10.1016/j.isprsjprs.2008.09.005 · DOI
  59. Eisank, 2011, A generic procedure for semantics-oriented landform classification using object-based image analysis, Geomorphometry, 125
  60. Esch, 2008, Improvement of image segmentation accuracy based on multiscale optimization procedure, IEEE Geosci. Remote Sens. Lett., 5, 463, 10.1109/LGRS.2008.919622 · DOI
  61. Fauvel, 2006, Decision fusion for the classification of urban remote sensing images, IEEE Trans. Geosci. Remote Sens., 44, 2828, 10.1109/TGRS.2006.876708 · DOI
  62. Feizizadeh, 2014, Systematic evaluation of fuzzy operators for object-based landslide mapping, South-Eastern Eur. J. Earth Observation Geomatics, 3, 219
  63. Flanders, 2003, Preliminary evaluation of eCognition object-based software for cut block delineation and feature extraction, Can. J. Remote Sens., 29, 441, 10.5589/m03-006 · DOI
  64. Gao, 2011, Optimal region growing segmentation and its effect on classification accuracy, Int. J. Remote Sens., 32, 3747, 10.1080/01431161003777189 · DOI
  65. Ghosh, 2007, A context-sensitive technique for unsupervised change detection based on Hopfield-type neural networks, IEEE Trans. Geosci. Remote Sens., 45, 778, 10.1109/TGRS.2006.888861 · DOI
  66. 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
  67. Grabner, 2008, On-line boosting-based car detection from aerial images, ISPRS J. Photogramm. Remote Sens., 63, 382, 10.1016/j.isprsjprs.2007.10.005 · DOI
  68. Haala, 1999, Extraction of buildings and trees in urban environments, ISPRS J. Photogramm. Remote Sens., 54, 130, 10.1016/S0924-2716(99)00010-6 · DOI
  69. Haapanen, 2004, Delineation of forest/nonforest land use classes using nearest neighbor methods, Remote Sens. Environ., 89, 265, 10.1016/j.rse.2003.10.002 · DOI
  70. Han, 2015, Object detection in optical remote sensing images based on weakly supervised learning and high-level feature learning, IEEE Trans. Geosci. Remote Sens., 53, 3325, 10.1109/TGRS.2014.2374218 · DOI · Paper page
  71. Han, 2014, Efficient, simultaneous detection of multi-class geospatial targets based on visual saliency modeling and discriminative learning of sparse coding, ISPRS J. Photogramm. Remote Sens., 89, 37, 10.1016/j.isprsjprs.2013.12.011 · DOI
  72. Hay, 2003, A comparison of three image-object methods for the multiscale analysis of landscape structure, ISPRS J. Photogramm. Remote Sens., 57, 327, 10.1016/S0924-2716(02)00162-4 · DOI · Paper page
  73. Hay, 2005, An automated object-based approach to the multiscale image segmentation of forest scenes, Int. J. Appl. Earth Observ. Geoinform., 7, 339, 10.1016/j.jag.2005.06.005 · DOI · Paper page
  74. Hinton, 2006, Reducing the dimensionality of data with neural networks, Science, 313, 504, 10.1126/science.1127647 · DOI · Paper page
  75. Hofmann, 2002, Knowledge-based building detection based on laser scanner data and topographic map information, Int. Arch. Photogramm. Remote Sens. Spat. Inform. Sci., 34, 169
  76. 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
  77. Hu, 2007, Road network extraction and intersection detection from aerial images by tracking road footprints, IEEE Trans. Geosci. Remote Sens., 45, 4144, 10.1109/TGRS.2007.906107 · DOI · Paper page
  78. Huang, 2009, Road centreline extraction from high-resolution imagery based on multiscale structural features and support vector machines, Int. J. Remote Sens., 30, 1977, 10.1080/01431160802546837 · DOI
  79. Huang, 2014, Feature coding in image classification: a comprehensive study, IEEE Trans. Pattern Anal. Mach. Intell., 36, 493, 10.1109/TPAMI.2013.113 · DOI
  80. Huertas, 1988, Detecting buildings in aerial images, Comput. Vis. Graph. Image Process., 41, 131, 10.1016/0734-189X(88)90016-3 · DOI · Paper page
  81. Hung, 2012, Multi-class predictive template for tree crown detection, ISPRS J. Photogramm. Remote Sens., 68, 170, 10.1016/j.isprsjprs.2012.01.009 · DOI
  82. 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
  83. Im, 2008, Object-based change detection using correlation image analysis and image segmentation, Int. J. Remote Sens., 29, 399, 10.1080/01431160601075582 · DOI · Paper page
  84. Inglada, 2007, Automatic recognition of man-made objects in high resolution optical remote sensing images by SVM classification of geometric image features, ISPRS J. Photogramm. Remote Sens., 62, 236, 10.1016/j.isprsjprs.2007.05.011 · DOI · Paper page
  85. Irvin, 1989, Methods for exploiting the relationship between buildings and their shadows in aerial imagery, IEEE Trans. Syst., Man, Cybern., 19, 1564, 10.1109/21.44071 · DOI
  86. Jain, 1997, Object detection using Gabor filters, Pattern Recognit., 30, 295, 10.1016/S0031-3203(96)00068-4 · DOI · Paper page
  87. Jain, 1998, Deformable template models: a review, Signal Process., 71, 109, 10.1016/S0165-1684(98)00139-X · DOI · Paper page
  88. Janssen, 1992, Knowledge-based crop classification of a Landsat Thematic Mapper image, Int. J. Remote Sens., 13, 2827, 10.1080/01431169208904084 · DOI
  89. Jin, 2007, Vehicle detection from high-resolution satellite imagery using morphological shared-weight neural networks, Image Vis. Comput., 25, 1422, 10.1016/j.imavis.2006.12.011 · DOI
  90. Jing, 2011, A novel edge detection algorithm based on global minimization active contour model for oil slick infrared aerial image, IEEE Trans. Geosci. Remote Sens., 49, 2005, 10.1109/TGRS.2010.2103671 · DOI
  91. Karantzalos, 2009, Recognition-driven two-dimensional competing priors toward automatic and accurate building detection, IEEE Trans. Geosci. Remote Sens., 47, 133, 10.1109/TGRS.2008.2002027 · DOI
  92. Kasetkasem, 2002, An image change detection algorithm based on Markov random field models, IEEE Trans. Geosci. Remote Sens., 40, 1815, 10.1109/TGRS.2002.802498 · DOI
  93. Kembhavi, 2011, Vehicle detection using partial least squares, IEEE Trans. Pattern Anal. Mach. Intell., 33, 1250, 10.1109/TPAMI.2010.182 · DOI
  94. 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
  95. Kim, 2004, Tracking road centerlines from high resolution remote sensing images by least squares correlation matching, Photogramm. Eng. Remote Sens., 70, 1417, 10.14358/PERS.70.12.1417 · DOI
  96. Laptev, 2000, Automatic extraction of roads from aerial images based on scale space and snakes, Mach. Vis. Appl., 12, 23, 10.1007/s001380050121 · DOI
  97. Le Hégarat-Mascle, 2009, Use of Markov random fields for automatic cloud/shadow detection on high resolution optical images, ISPRS J. Photogramm. Remote Sens., 64, 351, 10.1016/j.isprsjprs.2008.12.007 · DOI
  98. Lei, 2012, Rotation-invariant object detection of remotely sensed images based on texton forest and Hough voting, IEEE Trans. Geosci. Remote Sens., 50, 1206, 10.1109/TGRS.2011.2166966 · DOI
  99. Lei, 2014, Bi-temporal texton forest for land cover transition detection on remotely sensed imagery, IEEE Trans. Geosci. Remote Sens., 52, 1227, 10.1109/TGRS.2013.2248738 · DOI
  100. Leitloff, 2010, Vehicle detection in very high resolution satellite images of city areas, IEEE Trans. Geosci. Remote Sens., 48, 2795, 10.1109/TGRS.2010.2043109 · DOI
  101. Leninisha, 2015, Water flow based geometric active deformable model for road network, ISPRS J. Photogramm. Remote Sens., 102, 140, 10.1016/j.isprsjprs.2015.01.013 · DOI
  102. Leon, 2011, Improving the synoptic mapping of coral reef geomorphology using object-based image analysis, Int. J. Geogr. Inform. Sci., 25, 949, 10.1080/13658816.2010.513980 · DOI
  103. Lhomme, 2009, A new approach to building identification from very-high-spatial-resolution images, Int. J. Remote Sens., 30, 1341, 10.1080/01431160802509017 · DOI
  104. Li, 2015, Robust rooftop extraction from visible band images using higher order CRF, IEEE Trans. Geosci. Remote Sens., 53, 4483, 10.1109/TGRS.2015.2400462 · DOI
  105. 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
  106. Li, 2014, Object-based land-cover classification for metropolitan Phoenix, Arizona, using aerial photography, Int. J. Appl. Earth Obs. Geoinf., 33, 321
  107. Li, 2013, Object-based urban vegetation mapping with high-resolution aerial photography as a single data source, Int. J. Remote Sens., 34, 771, 10.1080/01431161.2012.714508 · DOI
  108. Li, 2010, Straight road edge detection from high-resolution remote sensing images based on the ridgelet transform with the revised parallel-beam Radon transform, Int. J. Remote Sens., 31, 5041, 10.1080/01431160903283835 · DOI
  109. Li, 2015, Feature representation for statistical-learning-based object detection: a review, Pattern Recognit., 48, 3542, 10.1016/j.patcog.2015.04.018 · DOI
  110. Li, 2011, Saliency and gist features for target detection in satellite images, IEEE Trans. Image Process., 20, 2017, 10.1109/TIP.2010.2099128 · DOI
  111. Li, 2001, Automatic detection of fire smoke using artificial neural networks and threshold approaches applied to AVHRR imagery, IEEE Trans. Geosci. Remote Sens., 39, 1859, 10.1109/36.951076 · DOI
  112. Lin, 1998, Building detection and description from a single intensity image, Comput. Vis. Image Understand., 72, 101, 10.1006/cviu.1998.0724 · DOI · Paper page
  113. Lin, 2015, Rotation-invariant object detection in remote sensing images based on radial-gradient angle, IEEE Geosci. Remote Sens. Lett., 12, 746, 10.1109/LGRS.2014.2360887 · DOI
  114. Liow, 1990, Use of shadows for extracting buildings in aerial images, Comput. Vis. Graph. Image Process., 49, 242, 10.1016/0734-189X(90)90139-M · DOI
  115. Lisita, 2013, Identifying potential areas of Cannabis sativa plantations using object-based image analysis of SPOT-5 satellite data, Int. J. Remote Sens., 34, 5409, 10.1080/01431161.2013.790574 · DOI
  116. Liu, 2013, Aircraft recognition in high-resolution satellite images using coarse-to-fine shape prior, IEEE Geosci. Remote Sens. Lett., 10, 573, 10.1109/LGRS.2012.2214022 · DOI
  117. Liu, 2013, Interactive geospatial object extraction in high resolution remote sensing images using shape-based global minimization active contour model, Pattern Recognit. Lett., 34, 1186, 10.1016/j.patrec.2013.03.031 · DOI
  118. Liu, 2014, Airplane detection based on rotation invariant and sparse coding in remote sensing images, Optik-Int. J. Light Electron Opt., 125, 5327, 10.1016/j.ijleo.2014.06.062 · DOI
  119. Liu, 2008, Detection of unexploded ordnance via efficient semisupervised and active learning, IEEE Trans. Geosci. Remote Sens., 46, 2558, 10.1109/TGRS.2008.920468 · DOI
  120. Lizarazo, 2014, Accuracy assessment of object-based image classification: another STEP, Int. J. Remote Sens., 35, 6135, 10.1080/01431161.2014.943328 · DOI
  121. Ma, 2010, Local manifold learning-based-nearest-neighbor for hyperspectral image classification, IEEE Trans. Geosci. Remote Sens., 48, 4099
  122. Macfaden, 2012, High-resolution tree canopy mapping for New York City using LIDAR and object-based image analysis, J. Appl. Remote Sens., 6, 1, 10.1117/1.JRS.6.063567 · DOI
  123. Maillard, 1989, Automatic map-guided extraction of roads from SPOT imagery for cartographic database updating, Int. J. Remote Sens., 10, 1775, 10.1080/01431168908904007 · DOI
  124. Malek, 2014, Efficient framework for palm tree detection in UAV images, IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens., 7, 4692, 10.1109/JSTARS.2014.2331425 · DOI
  125. 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
  126. Martha, 2010, Characterising spectral, spatial and morphometric properties of landslides for semi-automatic detection using object-oriented methods, Geomorphology, 116, 24, 10.1016/j.geomorph.2009.10.004 · DOI
  127. 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
  128. Martha, 2012, Object-oriented analysis of multi-temporal panchromatic images for creation of historical landslide inventories, ISPRS J. Photogramm. Remote Sens., 67, 105, 10.1016/j.isprsjprs.2011.11.004 · DOI
  129. Mayer, 1999, Automatic object extraction from aerial imagery—a survey focusing on buildings, Comput. Vis. Image Understand., 74, 138, 10.1006/cviu.1999.0750 · DOI · Paper page
  130. Mayer, 2006, A test of automatic road extraction approaches, Int. Arch. Photogramm. Remote Sens. Spat. Inform. Sci., 36, 209
  131. Mena, 2003, State of the art on automatic road extraction for GIS update: a novel classification, Pattern Recognit. Lett., 24, 3037, 10.1016/S0167-8655(03)00164-8 · DOI · Paper page
  132. Ming, 2015, Scale parameter selection by spatial statistics for GeOBIA: using mean-shift based multi-scale segmentation as an example, ISPRS J. Photogramm. Remote Sens., 106, 28, 10.1016/j.isprsjprs.2015.04.010 · DOI
  133. Mishra, 2014, Mapping vegetation morphology types in a dry savanna ecosystem: integrating hierarchical object-based image analysis with random forest, Int. J. Remote Sens., 35, 1175, 10.1080/01431161.2013.876120 · DOI
  134. Mokhtarzade, 2007, Road detection from high-resolution satellite images using artificial neural networks, Int. J. Appl. Earth Observ. Geoinform., 9, 32, 10.1016/j.jag.2006.05.001 · DOI
  135. Moon, 2002, Performance analysis of a simple vehicle detection algorithm, Image Vis. Comput., 20, 1, 10.1016/S0262-8856(01)00059-2 · DOI
  136. Moskal, 2011, Monitoring urban tree cover using object-based image analysis and public domain remotely sensed data, Remote Sens., 3, 2243, 10.3390/rs3102243 · DOI
  137. Moustakidis, 2012, SVM-based fuzzy decision trees for classification of high spatial resolution remote sensing images, IEEE Trans. Geosci. Remote Sens., 50, 149, 10.1109/TGRS.2011.2159726 · DOI
  138. Movaghati, 2010, Road extraction from satellite images using particle filtering and extended Kalman filtering, IEEE Trans. Geosci. Remote Sens., 48, 2807, 10.1109/TGRS.2010.2041783 · DOI
  139. 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
  140. Nebiker, 2014, Building change detection from historical aerial photographs using dense image matching and object-based image analysis, Remote Sens., 6, 8310, 10.3390/rs6098310 · DOI
  141. Niu, 2006, A semi-automatic framework for highway extraction and vehicle detection based on a geometric deformable model, ISPRS J. Photogramm. Remote Sens., 61, 170, 10.1016/j.isprsjprs.2006.08.004 · DOI
  142. Ok, 2013, Automated detection of buildings from single VHR multispectral images using shadow information and graph cuts, ISPRS J. Photogramm. Remote Sens., 86, 21, 10.1016/j.isprsjprs.2013.09.004 · DOI
  143. Ok, 2013, Automated detection of arbitrarily shaped buildings in complex environments from monocular VHR optical satellite imagery, IEEE Trans. Geosci. Remote Sens., 51, 1701, 10.1109/TGRS.2012.2207123 · DOI
  144. Pacifici, 2009, A neural network approach using multi-scale textural metrics from very high-resolution panchromatic imagery for urban land-use classification, Remote Sens. Environ., 113, 1276, 10.1016/j.rse.2009.02.014 · DOI · Paper page
  145. Peng, 2005, Model and context-driven building extraction in dense urban aerial images, Int. J. Remote Sens., 26, 1289, 10.1080/01431160512331326675 · DOI
  146. Peng, 2005, An improved snake model for building detection from urban aerial images, Pattern Recognit. Lett., 26, 587, 10.1016/j.patrec.2004.09.033 · DOI
  147. Phinn, 2012, Multi-scale, object-based image analysis for mapping geomorphic and ecological zones on coral reefs, Int. J. Remote Sens., 33, 3768, 10.1080/01431161.2011.633122 · DOI
  148. Qian, 2013, Hyperspectral image classification based on structured sparse logistic regression and three-dimensional wavelet texture features, IEEE Trans. Geosci. Remote Sens., 51, 2276, 10.1109/TGRS.2012.2209657 · DOI · Paper page
  149. Radoux, 2011, Thematic accuracy assessment of geographic object-based image classification, Int. J. Geogr. Inform. Sci., 25, 895, 10.1080/13658816.2010.498378 · DOI
  150. Schapire, 1999, Improved boosting algorithms using confidence-rated predictions, Mach. Learn., 37, 297, 10.1023/A:1007614523901 · DOI · Paper page
  151. Senaras, 2013, Building detection with decision fusion, IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens., 6, 1295, 10.1109/JSTARS.2013.2249498 · DOI
  152. Shi, 2014, Ship detection in high-resolution optical imagery based on anomaly detector and local shape feature, IEEE Trans. Geosci. Remote Sens., 52, 4511, 10.1109/TGRS.2013.2282355 · DOI
  153. Shufelt, 1999, Performance evaluation and analysis of monocular building extraction from aerial imagery, IEEE Trans. Pattern Anal. Mach. Intell., 21, 311, 10.1109/34.761262 · DOI
  154. Shufelt, 1996, Exploiting photogrammetric methods for building extraction in aerial images, Int. Arch. Photogramm. Remote Sens., 31, B6
  155. Sirmaçek, 2009, Urban-area and building detection using SIFT keypoints and graph theory, IEEE Trans. Geosci. Remote Sens., 47, 1156, 10.1109/TGRS.2008.2008440 · DOI · Paper page
  156. Sirmacek, 2011, A probabilistic framework to detect buildings in aerial and satellite images, IEEE Trans. Geosci. Remote Sens., 49, 211, 10.1109/TGRS.2010.2053713 · DOI
  157. Solberg, 1999, Contextual data fusion applied to forest map revision, IEEE Trans. Geosci. Remote Sens., 37, 1234, 10.1109/36.763280 · DOI
  158. Song, 2004, Road extraction using SVM and image segmentation, Photogramm. Eng. Remote Sens., 70, 1365, 10.14358/PERS.70.12.1365 · DOI · Paper page
  159. Stankov, 2013, Building detection in very high spatial resolution multispectral images using the hit-or-miss transform, IEEE Geosci. Remote Sens. Lett., 10, 86, 10.1109/LGRS.2012.2193552 · DOI
  160. Stankov, 2014, Detection of buildings in multispectral very high spatial resolution images using the percentage occupancy hit-or-miss transform, IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens., 7, 4069, 10.1109/JSTARS.2014.2308301 · DOI
  161. Stilla, 1997, Building reconstruction using different views and context knowledge, Int. Arch. Photogramm. Remote Sens., 32, 129
  162. 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
  163. Sugiyama, 2007, Dimensionality reduction of multimodal labeled data by local fisher discriminant analysis, J. Mach. Learn. Res., 8, 1027
  164. Sun, 2012, Automatic target detection in high-resolution remote sensing images using spatial sparse coding bag-of-words model, IEEE Geosci. Remote Sens. Lett., 9, 109, 10.1109/LGRS.2011.2161569 · DOI
  165. Sun, 2010, Automatic detection of geospatial objects using taxonomic semantics, IEEE Geosci. Remote Sens. Lett., 7, 23, 10.1109/LGRS.2009.2027139 · DOI
  166. Tang, 2015, Compressed-domain ship detection on spaceborne optical image using deep neural network and extreme learning machine, IEEE Trans. Geosci. Remote Sens., 53, 1174, 10.1109/TGRS.2014.2335751 · DOI · Paper page
  167. Tao, 2011, Airport detection from large IKONOS images using clustered SIFT keypoints and region information, IEEE Geosci. Remote Sens. Lett., 8, 128, 10.1109/LGRS.2010.2051792 · DOI
  168. Tao, 2012, A fast and robust sparse approach for hyperspectral data classification using a few labeled samples, IEEE Trans. Geosci. Remote Sens., 50, 2287, 10.1109/TGRS.2011.2172617 · DOI
  169. Tchoku, 1996, Detection of drainage channel networks on digital satellite images, Int. J. Remote Sens., 17, 1659, 10.1080/01431169608948730 · DOI
  170. Trinder, 1998, Knowledge-based road interpretation in aerial images, Int. Arch. Photogramm. Remote Sens., 32, 635
  171. Tuermer, 2013, Airborne vehicle detection in dense urban areas using HoG features and disparity maps, IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens., 6, 2327, 10.1109/JSTARS.2013.2242846 · DOI
  172. Tzotsos, 2011, Object-based image analysis through nonlinear scale-space filtering, ISPRS J. Photogramm. Remote Sens., 66, 2, 10.1016/j.isprsjprs.2010.07.001 · DOI
  173. Ünsalan, 2012, Road network detection using probabilistic and graph theoretical methods, IEEE Trans. Geosci. Remote Sens., 50, 4441, 10.1109/TGRS.2012.2190078 · DOI
  174. 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
  175. Walker, 2007, An object-oriented approach to urban forest mapping in Phoenix, Photogramm. Eng. Remote Sens., 73, 577, 10.14358/PERS.73.5.577 · DOI
  176. 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
  177. Wang, 1993, A knowledge-based vision system for detecting land changes at urban fringes, IEEE Trans. Geosci. Remote Sens., 31, 136, 10.1109/36.210454 · DOI
  178. Wang, 1988, A knowledge-based system for highway network extraction, IEEE Trans. Geosci. Remote Sens., 26, 525, 10.1109/36.7677 · DOI
  179. Wang, 2015, Road network extraction: a neural-dynamic framework based on deep learning and a finite state machine, Int. J. Remote Sens., 36, 3144, 10.1080/01431161.2015.1054049 · DOI
  180. Wang, 2011, Road extraction from high-spatial-resolution remotely sensed imagery by combining multi-profile analysis and extended Snakes model, Int. J. Remote Sens., 32, 6349, 10.1080/01431161.2010.508801 · DOI
  181. Weber, 2012, Spatial and spectral morphological template matching, Image Vis. Comput., 30, 934, 10.1016/j.imavis.2012.07.002 · DOI
  182. Wegner, 2011, Building detection from one orthophoto and high-resolution InSAR data using conditional random fields, IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens., 4, 83, 10.1109/JSTARS.2010.2053521 · DOI
  183. Weidner, 1995, Towards automatic building extraction from high-resolution digital elevation models, ISPRS J. Photogramm. Remote Sens., 50, 38, 10.1016/0924-2716(95)98236-S · DOI
  184. Wen, 2015, Efficient feature selection and classification for vehicle detection, IEEE Trans. Circuits Syst. Video Technol., 25, 508, 10.1109/TCSVT.2014.2358031 · DOI
  185. Woodcock, 1987, The factor of scale in remote sensing, Remote Sens. Environ., 21, 311, 10.1016/0034-4257(87)90015-0 · DOI · Paper page
  186. Wright, 2009, Robust face recognition via sparse representation, IEEE Trans. Pattern Anal. Mach. Intell., 31, 210, 10.1109/TPAMI.2008.79 · DOI · Paper page
  187. Xie, 2008, Object-based target search using remotely sensed data: a case study in detecting invasive exotic Australian Pine in south Florida, ISPRS J. Photogramm. Remote Sens., 63, 647, 10.1016/j.isprsjprs.2008.04.003 · DOI
  188. Xu, 2010, Artificial bee colony (ABC) optimized edge potential function (EPF) approach to target recognition for low-altitude aircraft, Pattern Recognit. Lett., 31, 1759, 10.1016/j.patrec.2009.11.018 · DOI
  189. Xu, 2010, Object classification of aerial images with bag-of-visual words, IEEE Geosci. Remote Sens. Lett., 7, 366, 10.1109/LGRS.2009.2035644 · DOI
  190. Yang, 2010, A nonparametric feature extraction and its application to nearest neighbor classification for hyperspectral image data, IEEE Trans. Geosci. Remote Sens., 48, 1279, 10.1109/TGRS.2009.2031812 · DOI
  191. Yang, 2015, Airborne SAR moving target signatures and imagery based on LVD, IEEE Trans. Geosci. Remote Sens., 53, 5958, 10.1109/TGRS.2015.2429678 · DOI
  192. Yang, 2013, Compensation for the NsRCM and phase error after polar format resampling for airborne spotlight SAR raw data of high resolution, IEEE Geosci. Remote Sens. Lett., 10, 165, 10.1109/LGRS.2012.2196676 · DOI
  193. Yang, 2013, Geographic image retrieval using local invariant features, IEEE Trans. Geosci. Remote Sens., 51, 818, 10.1109/TGRS.2012.2205158 · DOI · Paper page
  194. Yao, 2015, A coarse-to-fine model for airport detection from remote sensing images using target-oriented visual saliency and CRF, Neurocomputing, 164, 162, 10.1016/j.neucom.2015.02.073 · DOI
  195. Yin, 2015, Crater detection based on gist features, IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens., 8, 23, 10.1109/JSTARS.2014.2375066 · DOI
  196. Yokoya, 2015, Object detection based on sparse representation and hough voting for optical remote sensing imagery, IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens., 8, 2053, 10.1109/JSTARS.2015.2404578 · DOI
  197. 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
  198. Zhan, 2005, Quality assessment for geo-spatial objects derived from remotely sensed data, Int. J. Remote Sens., 26, 2953, 10.1080/01431160500057764 · DOI
  199. Zhang, 2015, Weakly supervised learning for target detection in remote sensing images, IEEE Geosci. Remote Sens. Lett., 12, 701, 10.1109/LGRS.2014.2358994 · DOI
  200. Zhang, 2011, Semi-automatic road tracking by template matching and distance transformation in urban areas, Int. J. Remote Sens., 32, 8331, 10.1080/01431161.2010.540587 · DOI

200 of 221 shown

Cited by

Metadata

Authors
Cheng, G., & Han, J.
Venue
ISPRS Journal of Photogrammetry and Remote Sensing
Publisher
Elsevier BV
Published
2016-07
Volume
117
Pages
11-28
ISSN
0924-2716 (ISSN-L)
OpenAlex
W2308318555
Cited by
1,475
References
12
FWCI
137.24
Type
journal article
Language
en
Open access
bronze
Licence
Funders
  • National Science Foundation of China (61401357, 61473231)
  • China Postdoctoral Science Foundation (2014M552491, 2015T81050)
  • Aerospace Science Foundation of China (20140153003)

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