Review of shadow detection and de-shadowing methods in remote sensing

Chinese Geographical Science

Volumn 23, Issue 4, 2013, Pages 403-420

  Shahtahmassebi, Amirreza ^a   Yang, Ning ^a   Wang, Ke ^a   Moore, Nathan ^b   Shen, Zhangquan ^a  

^a ZHEJIANG UNIVERSITY   (China)

^b MICHIGAN STATE UNIVERSITY   (United States)

Author keywords

de shadowing; detection; forest; shadow; urban

Indexed keywords

ACCURACY ASSESSMENT; ALGORITHM; DETECTION METHOD; ERROR CORRECTION; MONTANE FOREST; MOUNTAIN REGION; REMOTE SENSING; SATELLITE IMAGERY; SPATIAL RESOLUTION; URBAN AREA;

EID: 84883083537     PISSN: 10020063     EISSN: 1993064X     Source Type: Journal    
DOI: 10.1007/s11769-013-0613-x     Document Type: Review

References (96)

1. Addink E A, Stein A, 1999. A comparison of conventional and geostatistical methods to replace clouded pixels in NOAA-AVHRR images. International Journal of Remote Sensing, 20(5): 961-977. doi: 10.1080/014311699213028
2. Ahmad F, 2012. A review of remote sensing data change detection: Comparison of Faisalabad and Multan Districts, Punjab Province, Pakistan. Journal of Geography and Regional Planning, 5(9): 263-251. doi: 10.5897/JGRP11.121
3. Al-Najdawi N, Bez H E, Singhai J et al., 2012. A survey of cast shadow detection algorithms. Pattern Recognition Letters, 33(6): 752-764. doi: 10.1016/j.patrec.2011.12.013
4. Apan A A, 1997. Land cover mapping for tropical forest rehabilitation planning using remotely-sensed data. International Journal of Remote Sensing, 18(5): 1029-1049. doi: 10.1080/014311697218557
5. Arellano P, 2003. Missing Information in Remote Sensing: Wavelet Approach to Detect and Remove Clouds and Their Shadows. Enshede, the Netherlands: International Institute Geo-Information Science and Earth Observation.
6. Arevalo V, González J, Ambrosio G, 2005. Detecting Shadow QuickBird satellite images. ISPRS Commission VII Mid-term Symposium' Remote Sensing: From Pixels to Processes'. Enschede, the Netherlands, 8-11 May.
7. Arevalo V, González J, Ambrosio G, 2008. Shadow detection in colour high-resolution satellite images. International Journal of Remote Sensing, 29(7): 1945-1963. doi: 10.1080/01431160701395302
8. Arora M K, Mathur S, 2001. Multi-source classification using artificial neural network in a rugged terrain. Geocarto International, 16(3): 37-44. doi: 10.1080/10106040108542202
9. Asner G P, Warner A S, 2003. Canopy shadow in IKONOS satellite observations of tropical forests and savannas. Remote Sensing of Environment, 87(4): 521-533. doi: 10.1016/j.rse.2003.08.006
10. ATCOR. Leica geosystems geospatial imaging, LLC. Available at: http://www.directionsmag.com.
11. Bishop M, Shroder J J, Colby D J, 2003. Remote sensing and geomorphometry for studying relief production in high mountains. Geomorphology, 55(1-4): 345-361. doi: 10.1016/S0169-555X(03)00149-1
12. Blesius L, Weirich F, 2005. The use of the Minnaert correction for land-cover classification in mountainous terrain. International Journal of Remote Sensing, 26(17): 3831-3851. doi: 10.1080/01431160500104194
13. Carvalho L M T, 2001. Mapping and Monitoring Forest Remnants: Amultiscale Analysis of Spatio-temporal Data. Netherlands: Wagenigen University.
14. Chen Y, Wen D, Jing L et al., 2007. Shadow information recovery in urban areas from very high resolution satellite imagery. International Journal of Remote Sensing, 28(15): 3249-3254. doi: 10.1080/101431160600954621
15. Cheng F, Thiel K H, 1995. Delimiting the building heights in a city from the shadow in a panchromatic SPOT image. Part 1: Test of forty two buildings. International Journal of Remote Sensing, 16(3): 409-415. doi: 10.1080/01431169508954409
16. Choi K Y, Milton E J, 1999. A multispectral transform for the suppression of cloud shadows. In: Proceedings: Fourth International Airborne Remote Sensing Conference and Exhibition/21st Canadian Symposium on Remote Sensing. Ottawa, Canada: ERIM International Inc.: 762-769.
17. Colby D J, 1991. Topographic normalization in rugged terrain. Photogrammetric Engineering & Remote Sensing, 57(5): 531-537.
18. Conese C, Gilabert M A, Maselli F et al., 1993. Topographic normalization of TM scenes through the use of an atmospheric correction method and digital terrain models. Photogrammetric Engineering & Remote Sensing, 59(12): 1745-1753.
19. Dare P M, 2005. Shadow analysis in high-resolution satellite imagery of urban areas. Photogrammetric Engineering & Remote Sensing, 71(2): 169-177.
20. Dorren L, Luuk K A, Maier B et al., 2003. Improved Landsat-based forest mapping in steep mountainous terrain using object-based classification. Forest Ecology and Management, 183(1-3): 31-46. doi: 10.1016/S0378-1127(03)00113-0
21. Dozier J, 1989. Spectral signature of alpine snow cover from the Landsat Thematic Mapper. Remote Sensing Environment, 28: 9-22.
22. Eiumnoh A, Shrestha P, 2000. Application of DEM data to Land-sat image classification: Evaluation in a tropical wet-dry landscape of Thailand. Photogrammetric Engineering & Remote Sensing, 66(3): 297-304.
23. Ekstrand S, 1996. Landsat TM based forest damage assessment correction for topographic effects. Photogrammetric Engineering & Remote Sensing, 62(2): 151-161.
24. Fahsi A, Tsegaye T, Tadesse W et al., 2000. Incorporation of digital elevation models with Landsat-TM data to improve land cover classification. Forest Ecology and Management, 128(1-2): 57-64. doi: 10.1016/S0378-1127(99) 00272-8
25. Gao Y, Zhang W, 2009. LULC classification and Topographic Correction of Landsat-7 ETM + Imagery in the Yangjia River Watershed: The influence of DEM Resolution Sensors. Sensor, 9(3): 1980-1995. doi: 10.3390/s90301980
26. Gevers T, Smeulders A W M, 1999. Colour-based object recognition. Pattern Recognition, 32(3): 453-464. doi: 10.1016/S0031-3203(98)00036-3
27. Giles P T, Chapman M A, Franklin S E, 1994. Incorporation of a digital elevation model derived from stereoscopic satellite imagery in automated terrain analysis. Computers and Geosciences, 20(4): 441-460. doi: 10.1016/0098-3004(94) 90078-7
28. Giles P, 2001. Remote sensing and cast shadows in mountainous terrain. Photogrammetric Engineering & Remote Sensing, 67(7): 833-839.
29. Gitas I Z, Deverux B J, 2006. The role of topographic correction in mapping recently burned Mediterranean forest areas from LANDSAT TM images. International Journal of Remote Sensing, 27(1): 41-45. doi: 10.1080/ 01431160500182992
30. Goetz S J, Wright R K, Smith A J et al., 2003. IKONOS imagery for resource management: Tree cover, impervious surfaces, and riparian buffer analyses in the mid-Atlantic region. Remote Sensing of Environment, 88(1-2): 195-208. doi: 10.1016/j.rse.2003.07.010
31. Gu D, Gillespie A, 1998. Topographic normalization of Landsat TM images of forest based on Subpixel Sun-Canopy-Sensor Geometry. Remote Sensing of Environment, 64(2): 166-175. doi: 10.1016/S0034-4257(97)00177-6
32. Hansen M C, Loveland T R, 2012. A review of large area monitoring of land cover change using Landsat data. Remote sensing of Environment, 122(Landsat Legacy Special Issue): 66-74. doi: 10.1016/j.rse.2011.08.024
33. Hegarat-Mascle S L, Andre C, 2009. Use of Markov Random Fields for automatic clould/shadow detection on high resolution optical images. Journal of Photogrammetry and Remote Sensing, 64(4): 351-366. doi: 10.1016/j.isprsjprs. 2008.12.007
34. Heiskanen J, Kajuutti K, Jackson M et al., 2002. Assessment of glaciological parameters using Landsat satellite data in Svartisen, Northern Norway. Proceedings of European Association of Remote sensing Laboratories (EARSel) Workshop on Observing Our Cryosphere from Space: Techniques and Methods for Monitoring Snow and Ice with Regard to Climate Change. Bern Switzerland, 11-13 March, 34-42.
35. Hendriks J, Pellikka P, 2004. Estimation of reflectance from a glacier surface by comparing spectrometer measurements with satellite-derived reflectances. Journal of Glaciology, 38(2): 139-154.
36. Holben B, Justice C, 1981. An examination of spectral band ratioing to reduce the topographic effect on remotely sensed data. International Journal of Remote Sensing, 2(2): 115-133. doi: 10.1080/01431168108948349
37. Huang W, Xiao Y, Lu S, 2011. Shadow detection of the high-resolution remote sensing image based on pulse coupled neural network. 7th Symposium on Multispectral Image Processing and Pattern Recognition (MIPPR) - Remote Sensing Image Processing, Geographic Information Systems, and Other Applications. Guilin, China.
38. Jensen J, 2007. Introductory Digital Image Processing. Beijing: Science Press and Pearson Education Asia Limited, China, 127-173, 220-221.
39. Jin S, Homer C, Yang L et al., 2013 Automated cloud and shadow detection and filling using two-date Landsat imagery in the USA. International Journal of Remote Sensing, 34(5): 1540-1560. doi: 10.1080/01431161.2012.720045
40. Kouchi K, Yamazaki F, 2007. Characteristics of tsunami-affected areas in moderate-resolution satellite images. IEEE Transactions on Geoscience and Remote Sensing, 45(6): 1650-1657. doi: 10.1109/TGRS.2006.886968
41. Law K H, Nichol J, 2004. Topographic correction for differential illumination effects on IKONOS on satellite imagery. ISPRS Congress Istanbul. Turkey, 641-646.
42. Leblon B, Gallant L, Granberg H, 1996. Effects of shadowing types on ground-measured visible and near-infrared shadow reflectances. Remote Sensing of Environment, 58(3): 322-328. doi: 10.1016/S0034-4257(96)00079-X
43. LeciaGeosystems, 2008. ATCOR - Frequently Asked Questions: 6. what should be the resolution of my DEM be for ACTOR3? Available at: http://www.geosystems. de/atcor/faqs/faq-answers.html
44. Liu J, Fang T, Li D, 2011. Shadow detection in remotely sensed images based on self-adaptive feature selection. IEEE Transactions on Geoscience and Remote Sensing, 49(12): 5092-5103. doi: 10.1109/TGRS.2011.2158221
45. Liu W, Yamazaki F, 2012. Object-based shadow extraction and correction of high-resolution optical satellite images. IEEE Journal of Selected Topics in Applied Earth Observation and Remote Sesning, 5(4): 1296-1302. doi: 10.1109/JSTARS.2012.2189558
46. Lu D, 2006. The potential and challenge of remote sensing-based biomass estimation. International Journal of Remote Sensing, 27(7): 1297-1328. doi: 10.1080/01431160500486732
47. Lu D, 2007. Detection and substitution of clouds/hazes and their cast shadows on IKONOS images. International Journal of Remote Sensing, 28(18): 4027-4035. doi: 10.1080/01431160701227703
48. Lu D, Weng Q, 2007. A survey of image classification methods and techniques for improving classification performance. International Journal of Remote Sensing, 28(5): 823-870. doi: 10.1080/01431160600746456
49. Martinuzzi S, Gould W A, Ramos-González O M, 2007. Creating cloud-free Landsat ETM+ data sets in tropical landscapes: Cloud and cloud-shadow removal, United States Department of Agriculture (USDA), General Technical Report IIFT-GTR-32. Available at: http://www.fs.fed.us/global/iitf/pubs/iitf- gtr32.pdf.
50. Massalabi A, He D C, Beaudry G B, 2004. Restitution of information under shadow in remote sensing highs pace resolution images: Application to IKONOS data of Sherbrooke City. International Archives of Photogrammetry & Remote Sensing, 35(Part B7): 173-178.
51. Mather P M, 2004. Computer Processing of Remotely-Sensed Images. London: John Wiley & Sons Ltd., 81 and 136.
52. Matsushita B, Yang W, Onda Y et al., 2007. Sensitivity of the enhanced vegetation index (EVI) and normalized difference vegetation index (NDVI) to topographic effect: A case study in high-density cypress forest. Sensors, 7(11): 2636-265. doi: 10.3390/s7112636
53. Miura H, Midorikawa S, 2006. Slopr failures by the 2004 Niigata-Ken Chuetsu, Japan earthquake observed in high-resolution satellite images. 4th International Workshop on Remote Sensing for Post-Disaster Response. Cambridge, UK, 25-26 September.
54. Nagao M, Matsutyama T, Ikeda Y, 1979. Region extraction and shape analysis in aerial photographs. Computer Vision Graphics and Image Processing, 10(3): 195-223.
55. Nakajima T, Tao G, Yasuoka Y, 2002. Simulated recovery of information in shadow areas on IKONOS image by combing ALS data. Proceeding of Asian Conference on Remote Sensing (ACRS). Available at: http://www.a-a-r-s.org/acrs/ proceedings2002.php
56. Nizalapur V, 2008. Land cover classification using multi-source data fusion of ENVISAT-ASAR and IRS p6 LISS-III Satellite data: A case study over tropical most deciduous forested regions of Karnataka, India. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Science, Beijing, China.
57. Nole G, Danese M, Mugante B et al., 2012. Satellite based observations of the time-variation of urban sprawl using autocorrelation techniques. Managing Complexity in Land Use and Environmental Impacts Modelling. 14-15 May, 512-527.
58. Ortega-Huerta M, Komar O, Price K et al., 2012. Mapping coffee plantations with Landsat imagery: An example from El Salvador. International Journal of Remote Sensing, 33(1): 220-242. doi: 10.1080/01431161.2011.591442
59. Ozdemir I, 2008. Estimating stem volume by tree crown area and tree shadow area extracted from pan-sharpened Quickbird imagery in open Crimean juniper forests. International Journal of Remote Sensing, 29(19): 5643-5655. doi: 10.1080/01431160802082155
60. Prati A, Mikic I, Trivedi M, Cucchiara R, 2003. Detecting Moving Shadows: Algorithms and Evaluation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 25(7): 918-923. doi: 10.1109/TPAMI.2003.1206520
61. Pringle M J, Schmidt M, Muir J S, 2009. Geostatistical interpolation of SLC-off Landsat ETM+ images. ISPRS Journal of Photogrammetry and Remote Sensing, 64(6): 654-664. doi: 10.1016/j.isprsjprs.2009.06.001
62. Rau J Y, Chen N Y, Chen L C, 2002. True orthophoto generation of built-up areas using multi-view images. Photogrammetric Engineering and Remote Sensing, 68(6): 581-588.
63. Ren G, Zhu A X, Wang W et al., 2009. A hierarchical approach coupled with coarse DEM information for improving the efficiency and accuracy of forest mapping over very rugged terrains. Forest Ecology and Management, 258(1): 26-34. doi: 10.1016/j.foreco.2009.03.043
64. Riano D, Chuvieco E, Salas J et al., 2003. Assessment of different topographic corrections in Landsat-TM data for mapping vegetation types. IEEE Transactions on Geoscience and Remote Sensing, 41(5): 1056-1061. doi: 10.1109/TGRS.2003.811693
65. Richter R, Kellenberger T, Kaufmann H, 2009. Comparison of topographic correction methods. Remote Sensing, 1(3): 184-196. doi: 10.3390/rs1030184
66. Richter R, Muller A, 2005. De-shadowing of satellite/airborne imagery. International Journal of Remote Sensing, 26(15): 3137-3148. doi: 10.1080/01431160500114664
67. Rosin P L, Ellis T, 1995. Image difference threshold strategies and shadow detection. In: Proceedings of the Sixth British Machine Vision Conference. Birmingham, UK, 347-356.
68. Rossi R E, Dungan J L, Beck L R, 1994. Kriging in the shadows: Geostatistical interpolation for remote sensing. Remote Sensing of Environment, 49(1): 32-40. doi: 10.1016/0034-4257(94)90057-4
69. Roy D P, Ju J, Lewis P et al., 2008. Multi-temporal MODIS-Landsat data fusion for relative radiometric normalization, gap filling, and prediction of Landsat data. Remote Sensing of Environment, 112(6): 3112-3112. doi: 10.1016/j.rse.2008.03.009
70. Saha K A, Arora M K, Csaplovics E et al., 2005. Land covers classification using IRS LISS III image and DEM in a rugged terrain: A case study in Himalayas. Geocarto International, 20(2): 33-40. doi: 10.1080/10106040508542343
71. Salvador E, Cavallaro A, Ebrahimi T, 2001. Shadow identification and classification using invariant colour models. IEEE International Conference on Acoustic, Speech, and Signal Processing, Salt Lake City, Utah, 3, 1545-1548.
72. Sarabandi P, Yamazaki F, Matsuoka M et al., 2004. Shadow detection and radiometric restoration in satellite high resolution images. Proceedings of IEEE International Geoscience and Remote Sensing Symposium (IGARSS). Ancorage Alaska, 20-24 Septmeber, 3744-3747. doi: 10.1109/IGARSS.2004.1369936
73. Shahtahmassebi A R, Wang K, Zhangguan S et al., 2011. Evaluation on the two filling functions for the recovery of forest information in mountainous shadows on Landsat ETM+ Image. Journal of Mountain Science, 8(3): 414-426. doi: 10.1007/s11629-011-2051-5
74. Shettigara V K, Sumerling G M, 1998. Height determination of extended objects using shadows in SPOT images. Photogrammetric Engineering and Remote Sensing, 64(1): 35-44.
75. Shu J S P, Freeman H, 1990. Cloud shadow removal from aerial photographs. Pattern Recognition, 23(6): 647-656. doi: 10.1016/0031-3203(90)90040-R
76. Simpson J J, Sitt J R, 1998. A procedure for the detection and removal of cloud shadow from AVHRR data over land. IEEE Transactions on Geoscience and Remote Sensing, 36(3): 880-897. doi: 10.1109/36.673680
77. Soenen S A, Peddle D R, Coburn C A et al., 2007. Improved topographic correction of forest image data using a 3-D canopy reflectance model in multiple forward mode. International Journal of Remote Sensing, 29(4): 1007-1027. doi: 10.1080/01431160701311291
78. Song M, Civco D L, 2002. A knowledge-based approach for reducing cloud and shadow. Proceedings of the American Society of Photogrammetry and Remote Sensing - American Congress on Surveying and Mapping (ASPRS-ACSM) Annual Convention and International Federation of Surveyors (FIG) XXII Congress. Washington, DC, April, 22-26.
79. Sotomayor A I T, 2002. A spatial analysis of different forest cover types using GIS and Remote sensing techniques. Forest Science Division International Institute for Geo information Science and Earth observation Enschede. the Netherlands, 20.
80. Statella T, Da Silva E A, 2008. Shadows and clouds detection in high resolution images using mathematical morphology. Pecora 17-The Future of Land Imaging. Denver, Colorado, November 18-20.
81. Susuki A, Shio A, Arai H et al., 2000. Dynamic shadow compensation of aerial images based on color and spatial analysis. In: Proceedings of the 15th International Conference on Patten Recognition. Barcelona, Catalonia, Spain, 317-320.
82. Tobler W R, 1970. A computer movie simulating urban growth in the Detroit region. Economic Geography, 46: 234-240.
83. Tokola T, Sticklen J, Linden, M V D, 2001. Use of topographic correction in Landsat TM-based forest interpretation in Nepal. International Journal of Remote Sensing, 22(4): 551-563. doi: 10.1080/01431160050505856
84. Tsai V J D, 2006. A comparative study on shadow compensation of color aerial images in invariant color models. IEEE Transactions on Geoscience and Remote Sensing, 44(6): 1661-1667. doi: 10.1109/TGRS.2006.869980
85. Tseng D C, Tseng H T, Chien C L, 2008. Automatic cloud removal from multi-temporal SPOT images. Applied Mathematics and Computation, 205(2): 584-600. doi: 10.1016/j.amc.2008.05.050
86. Wan C Y, King B A, Li Z, 2012. An assessment of shadow enhanced urban remote sensing imagery of a complex city - Hong Kong. Proceedings of the XXII Congress of the International Society for Photogrammetry and Remote Sensing. Melbourne, Australia, 25 August-01 September, 177-182.
87. Wang B, Ono A, Muramatsu K et al., 1999. Automated detection and removal of clouds and their shadows from Landsat TM images. IEICE Transactions on Information and Systems, E82D(2): 453-460.
88. Wang Q J, Tian Q J, Lin Q Z et al., 2008. An improved algorithm for shadow restoration of high spatial resolution imagery. Proceedings of SPIE7123, Remote Sensing of the Environment: 16th National Symposium on Remote Sensing of China, 7123: 1-7. doi: 10.1117/12.816170
89. Yang X, Skidmore, A K, Melick D et al., 2007. Towards an efficacious method of using Landsat TM imagery to map forest in complex mountain terrain in Northwest Yunnan, China. International Society for Tropical Ecology, 48(2): 227-239.
90. Yesilnacar E, Suzen M L, 2006. A land-cover classification for landslide susceptibility mapping by using feature components. International Journal of Remote Sensing, 27(2): 253-275. doi: 10.1080/0143116050030042
91. Zhan Q M, Shi W Z, Xiao Y H, 2005. Quantitative analysis of shadow effects in high-resolution images of urban areas. 3rd International Symposium Remote Sensing and Data Fusion Over Urban Areas (URBAN) and 5th International Symposium Remote Sensing of Urban Areas (URS), 1682-1777. Available at: http://www.isprs.org/proceedings/XXXVI/8-W27/zhan.pdf
92. Zhang C R, Li W D, Travis D J, 2009. Restoration of clouded pixels in multispectral remotely sensed imagery with cokriging. International Journal of Remote Sensing, 30(9): 2173-2195. doi: 10.1080/01431160802549294
93. Zhang C R, Li W D, Travis D J, 2007. Gap-fills of SLC-off Landsat ETM+ satellite image using a geostatistical approach. International Journal of Remote Sensing, 28(22): 5103-5122. doi: 10.1080/01431160701250416
94. Zhang X Y, Jiang H, Zhou G M et al., 2011. Geostatistical interpolation of missing data and downscaling of spatial resolution for remotely sensed atmospheric methane column concentrations. International Journal of Remote Sensing, 33(1): 1-15. doi: 10.1080/01431161.2011.584078
95. Zhou W L, Huang G L, Troy A et al., 2009. Object-based land cover classification of shaded areas in high spatial resolution of imagery of urban areas: A comparison study. Remote Sensing of Environment, 113(8): 1769-1777. doi: 10.1016/j.rse.2009.04.007
96. Zhu X L, Liu D S, Chen J, 2012. A new geostatistical approach for filling gaps in Landsat ETM+ SLC-off images. Remote Sensing of Environment, 124: 49-60. doi: 10.1016/j.rse.2012.04.019