Hyperspectral Remote Sensing of Urban Areas

Geography Compass

Volumn 7, Issue 1, 2013, Pages 7-21

  Hardin, Perry ^a   Hardin, Andrew ^a  

^a BRIGHAM YOUNG UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

AIRBORNE SENSING; ALGORITHM; IMAGE ANALYSIS; IMAGE CLASSIFICATION; REMOTE SENSING; SPECTRAL ANALYSIS; SPECTRAL RESOLUTION; URBAN AREA;

EID: 84872520280     PISSN: None     EISSN: 17498198     Source Type: Journal    
DOI: 10.1111/gec3.12017     Document Type: Article

References (101)

1. Akçay, H. G. and Aksoy, S. (2008). Automatic detection of geospatial objects using multiple hierarchical segmentations. IEEE Transactions on Geoscience and Remote Sensing 46, pp. 2097-2111.
2. Ball, J. E. and Bruce, L. M. (2007). Level set hyperspectral image classification using best band analysis. IEEE Transactions on Geoscience and Remote Sensing 45, pp. 3022-3027.
3. Barducci, A., et al. (2006). Hyperspectral remote sensing for light pollution monitoring. Annals of Geophysics 49, pp. 305-310.
4. Bassani, C., et al. (2007). Deterioration status of asbestos-cement roofing sheets assessed by analyzing hyperspectral data. Remote Sensing of Environment 109, pp. 361-378.
5. Ben-Dor, E., et al. (2001). Quantitative approach for monitoring the urban heat island effects, using hyperspectral remote sensing. In: Proceedings of the 2001 IEEE International Geoscience and Remote Sensing Symposium IGARSS'01, July 9-11, Vol. 6, Sydney, Australia, pp. 73-76.
6. Benediktsson, J. A., Chanussot, J. and Fauvel, M. (2007). Multiple classifier systems in remote sensing: from basics to recent developments. In: Haindl, M., Kittler, J. and Rolli, F. (eds) Lecture Notes in Computer Science. Vol. 4472. Berlin, Germany: Springer-Verlag, pp. 501-512.
7. Benediktsson, J. A. and Kanellopoulos, I. (1999). Classification of multisource and hyperspectral data based on decision fusion. IEEE Transactions on Geoscience and Remote Sensing 37, pp. 1367-1377.
8. Benediktsson, J. A., Palmason, J. A. and Sveinsson, J. R. (2005). Classification of hyperspectral data from urban areas based on extended morphological profiles. IEEE Transactions on Geoscience and Remote Sensing 43, pp. 480-491.
9. Benediktsson, J. A., Pesaresi, M. and Arnason, K. (2003). Classification and feature extraction for remote sensing images from urban areas based on morphological transformations. IEEE Transactions on Geoscience and Remote Sensing 41, pp. 1940-1949.
10. Berge, A., Jensen, A. C. and Shistad Solberg, A. H. (2007).Sparse inverse covariance estimates for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 45, pp.1399-1407.
11. Blaschke, T. (2010). Object based image analysis for remote sensing. ISPRS Journal of Photogrammetry and Remote Sensing 65, pp. 2-16.
12. Borengasser, M., Hungate, W. S. and Watkins, R. (2008). Hyperspectral Remote Sensing: Principles and Applications. Boca Raton FL: CRC Press.
13. Brook, A. E., Ben-Dor, E. and Richter, R. (2010). Fusion of hyperspectral and lidar data for civil engineering structure monitoring. In: Proceedings from the Hyperspectral 2010 Workshop, March 17-19, Frascati, Italy.
14. Bue, B. D., Merényi, E. and Csathó, B. (2010). Automated labeling of materials in hyperspectral imagery. IEEE Transactions in Geoscience and Remote Sensing 48, pp. 4059-4070.
15. Cavalli, R. M., et al. (2008). Hyperspectral sensor data capability for retrieving complex urban land cover in comparison with multispectral data: Venice case study. Sensors 8, pp. 3299-3320.
16. Chang, C.-I. and Wang, S. (2006). Constrained band selection for hyperspectral imagery. IEEE Transactions on Geoscience and Remote Sensing 44, pp. 1575-1585.
17. Chen, C.-M., Hepner, G. F. and Forster, R. R. (2003). Fusion of hyperspectral and radar data using HIS transformation to enhance urban surface features. ISPRS Journal of Photogrammetry and Remote Sensing 58, pp. 19-30.
18. Dell'Acqua, F., et al. (2004). Exploiting spectral and spatial information in hyperspectral urban data with high resolution. IEEE Geoscience and Remote Sensing Letters 1, pp. 322-326.
19. Dougherty, E. R. (1992). An Introduction to Morphological Image Processing. Bellingham, WA: SPIE Optical Engineering Press. 161pp.
20. Esch, T., et al. (2010). Urban remote sensing: how can earth observation support the sustainable development of urban environments? In: Proceedings from the 46th ISOCARP Congress, September 19-23, Nairobi, Kenya.
21. Falcone, J. A. and Gomez, R. (2005). Mapping impervious surface type and sub-pixel abundance using Hyperion hyperspectral imagery. Geocarto International 20, pp. 3-10.
22. Franke, J., Roberts, D. A., Halligan, K. and Menz, G. (2009). Hierarchical multiple endmember spectral mixture analysis (MESMA) of hyperspectral imagery for urban environments. Remote Sensing of Environment 113, pp. 1712-1723.
23. Gamba, P. and Houshmand, B. (2000). Urban remote sensing through multispectral and radar data. In: Proceedings of the ERS/ENVISAT Symposium, October 16-20, Gothenburg, Sweden.
24. Ganesh Babu, B. (2009). Classification of Features Using Airborne Hyperspectral Data, Terre Haute, IN: Department of Geography, Indiana State University.
25. Golubiewski, N. E. and Wessman, C. A. (2010). Discriminating urban vegetation from a metropolitan matrix through partial unmixing with hyperspectral AVIRIS data. Canadian Journal of Remote Sensing 36, pp. 261-275.
26. Gomez, R. (2002). Hyperspectral imaging: a useful technology for transportation analysis. Optical Engineering 41, pp. 2137-2143.
27. Green, A. A., Berman, M., Switzer, P. and Craig, M. D. (1988). A transformation for ordering multispectral data in terms of image quality with implications for noise removal. IEEE Transactions on Geosciences and Remote Sensing 26, pp. 65-74.
28. Griffen, M. K. and Burke, H. K. (2003). Compensation of hyperspectral data for atmospheric effects. Lincoln Laboratory Journal 14, pp. 29-54.
29. Griffiths, P., Hostert, P., Gruebner, O. and van der Linden, S. (2010). Mapping megacity growth with multi-sensor data. Remote Sensing of Environment 114, pp. 426-439.
30. Heiden, U., Segl, K., Roessner, S. and Kaufmann, H. (2003). Ecological evaluation of urban biotope types using airborne hyperspectral HyMap data. In: 2nd GRSS/ISPRS Joint Workshop on Remote Sensing and Data Fusion over Urban Areas, May 22-23, Berlin, Germany, pp. 18-22.
31. Heldens, W., et al. (2011). Can the future EnMap contribute to urban applications? Remote Sensing 3, pp. 1817-1846.
32. Hepner, G. F., Houshmand, B., Kulikov, I. and Bryant, N. (1998). Investigation of the potential for the integration of AVIRIS and IFSAR for urban analysis. Photogrammetric Engineering and Remote Sensing 64, pp. 813-820.
33. Herold, M., Gardner, M. E., Noronha, V. and Roberts, D. A. (2003a). Spectrometry and hyperspectral remote sensing of urban infrastructure. Online Journal of Space Communications 3. Retrieved on 8 February 2012 from: http://satjournal.tcom.ohiou.edu/issue3/abst_herold.html).
34. Herold, M., Gardner, M. E. and Roberts, D. A. (2003b). Spectral resolution requirements for mapping urban areas. IEEE Transactions on Geoscience and Remote Sensing 41, pp. 1907-1919.
35. Herold, M. and Roberts, D. (2005). Spectral characteristics of asphalt road aging and deterioration: implications for remote-sensing applications. Applied Optics 44, pp. 4327-4334.
36. Herold, M. and Roberts, D. A. (2006). Multispectral satellites - imaging spectrometry - LIDAR: spatial-spectral tradeoffs in urban mapping. International Journal in Geoinformatics 2, pp. 1-14.
37. Herold, M., Roberts, D. A., Gardner, M. E. and Dennison, P. E. (2004). Spectrometry for urban area remote sensing - Development and analysis of a spectral library from 350 to 2400nm. Remote Sensing of Environment 91, pp. 304-319.
38. Herold, M., Schiefer, S., Hostert, P. and Roberts, D. A. (2006). Applying imaging spectrometry in urban areas. In: Quattrochi, D. A. and Weng, Q. (eds) Urban Remote Sensing. Boca Raton, FL: CRC Press, pp. 137-162.
39. Homayouni, S. and Roux, M. (2004). Hyperspectral image analysis for material mapping using spectral matching. In: Proceedings from the XX ISPRS Congress, July 12-23, Istanbul, Turkey.
40. Hughes, G. F. (1968). On the mean accuracy of statistical pattern recognizers. IEEE Transactions on Information Theory IT-14, pp. 55-63.
41. Jensen, J. R. (2007). Remote Sensing of the Environment: An Earth Resource Perspective. Upper Saddle River, NJ: Prentice Hall.
42. Jensen, R. R., Hardin, A. J., Hardin, P. J. and Jensen, J. R. (2011). A new method to correct push-broom hyperspectral data using linear features and ground control points. GIScience and Remote Sensing 48, pp. 416-431.
43. Jensen, R. R., Hardin, P. J. and Hardin, A. J. (2012a). Estimating urban leaf area index of individual trees with hyperspectral data. Photogrammetric Engineering and Remote Sensing 78, pp. 495-504.
44. Jensen, R. R., Hardin, P. J. and Hardin, A. J. (2012b) Classification of urban tree species using hyperspectral data. Geocarto International 27, pp. 443-458.
45. Jensen, R. R., et al. (2009). Modeling urban leaf area with AISA+ hyperspectral data. Applied Geography 29, pp. 320-332.
46. Jimenez, L. O., Morales-Morell, A. and Creus, A. (1999). Classification of hyperdimensional data based on feature and decision fusion approaches using projection pursuit, majority voting, and neural networks. IEEE Transactions on Geoscience and Remote Sensing 37, pp. 1360-1366.
47. Jung, A., Kardeván, P. and To{double acute}kei, L. (2005). Detection of urban effect on vegetation in a less built-up Hungarian city by hyperspectral remote sensing. Physics and Chemistry of the Earth 30, pp. 255-259.
48. Jung, A., To{double acute}kei, L. and Kardeván, P. (2007). Application of airborne hyperspectral and thermal images to analyse urban microclimate. Applied Ecology and Environmental Research 7, pp. 165-175.
49. Keshava, N. (2003). A survey of spectral unmixing algorithms. Lincoln Laboratory Journal 14, pp. 55-78.
50. Kuo, B.-C. and Landgrebe, D. (2001). Improved Statistics Estimation and Feature Extraction for Hyperspectral Data Classification. TR-ECE 01-6. School of Electrical and Computer Engineering, Purdue University.
51. Lacherade, S., Miesch, C., Briottet, X. and Le Men, H. (2005). Spectral variability and bidirectional reflectance behavior of urban materials at a 20cm spatial resolution in the visible and near-infrared wavelengths. A case study over Toulouse (France). International Journal of Remote Sensing 26, pp. 3859-3866.
52. Lemp, D. and Weidner, U. (2005). Segment-based characterization of roof surfaces using hyperspectral and laser scanning data. In: Proceedings of the 2005 IEEE International Geoscience and Remote Sensing Symposium IGARSS'05, July 25-29, Seoul, Korea, pp. 4942-4945.
53. Li, J., Bioucas-Dias, J. M. and Plaza, A. (2011) Hyperspectral image segmentation using a new Bayesian approach with active learning. IEEE Transactions on Geoscience and Remote Sensing 49, pp. 3947-3960.
54. Licciardi, G., et al. (2009). Decision fusion for the classification of hyperspectral data; outcome of the 2008 GRS-S Data Fusion Contest. IEEE Transactions on Geoscience and Remote Sensing 47, pp. 3857-3865.
55. van der Linden, S. and Hostert, P. (2009). The influence of urban structures on impervious surface maps from airborne hyperspectral data. Remote Sensing of Environment 113, pp. 2298-2305.
56. van der Linden, S., et al. (2010). Classifying segmented hyperspectral data from heterogeneous urban environments using support vector machines. Journal of Applied Remote Sensing 1, article 013543.
57. Marimont, R. B. and Shapiro, M. B. (1979). Nearest neighbor searches and the curse of dimensionality. IMA Journal of Applied Math 24, pp. 59-70.
58. Melgani, F. and Bruzzone, L. (2004). Classification of hyperspectral remote sensing images with support vector machines. IEEE Transactions on Geoscience and Remote Sensing 42, pp. 1778-1790.
59. Mura, M. D., et al. (2010). Classification of hyperspectral images by using extended morphological attribute profiles and independent component analysis. IEEE Geoscience and Remote Sensing Letters 8, pp. 541-545.
60. Nichol, J., et al. (2007). Earth observation for urban planning and management. State of the art and recommendations for application of earth observation in urban planning. Photogrammetric Engineering and Remote Sensing 73, pp. 973-979.
61. Noble, W. S. (2006). What is a support vector machine? Nature Biotechnology 24, pp. 1565-1568.
62. Oommen, T., Misra, D., Twarakavi, N. K. C., Prakash, A., Sahoo, B. and Bandopadhyay, S. (2008). An objective analysis of support vector machine classification for remote sensing. Mathematical Geosciences 4, pp. 409-424.
63. Pal, M. and Mather, P. M. (2004). Assessment of the effectiveness of support vector machines for hyperspectral data. Future Generation Computer Systems 20, pp. 1215-1225.
64. Platt, R. V. and Goetz, A. F. H. (2004). A comparison of AVIRIS and Landsat for land use classification at the urban fringe. Photogrammetric Engineering and Remote Sensing 70, pp. 813-819.
65. Plaza, A. J. (2007). Recent developments and future directions in hyperspectral data classification. Proceedings of the SPIE, Image and Signal Processing for Remote Sensing XIII 6748, pp. A-1A-15.
66. Plaza, A. J.(2008). Parallel processing of remotely sensed hyperspectral imagery: full-pixel versus mixed-pixel classification. Concurrency and Computation: Practice and Experience 20, pp. 1539-1572.
67. Plaza, A., Martínez, P., Pérez, R. and Plaza, J. (2004). A quantitative and comparative analysis of endmember extraction algorithms from hyperspectral data. IEEE Transactions on Geoscience and Remote Sensing 42, pp. 650-663.
68. Plaza, A., Martínez, P., Plaza, J. and Pérez, R. (2005). Dimensionality reduction and classification of hyperspectral image data using sequences of extended morphological transformations. IEEE Transactions on Geoscience and Remote Sensing 43, pp. 466-479.
69. Plaza, A., et al. (2006). Advanced processing of hyperspectral images. In: Proceedings of the 2006 IEEE International Geoscience and Remote Sensing Symposium IGARSS'06, July 31-August 4, Denver, CO Vol. 4, pp. 1974-1979.
70. Press, W. H., Teukolsky, S. A., Vetterling, W. T. and Flannery, B. P. (2007). Numerical recipes. 3rd ed. New York, NY: Cambridge University Press.
71. Pu, R. and Liu, D. (2011). Segmented canonical discriminant analysis of in situ hyperspectral data for identifying 13 urban tree species. International Journal of Remote Sensing 32, pp. 2207-2226.
72. Rashed, T., et al. (2003). Measuring the physical composition of urban morphology using multiple endmember spectral mixture models. Photogrammetric Engineering and Remote Sensing 69, pp. 1011-1020.
73. Ridd, M. K. (1995). Exploring a V-I-S (vegetation-impervious surface-soil) model for urban ecosystem analysis through remote sensing: comparative anatomy for cities. International Journal of remote Sensing 16, pp. 2165-2185.
74. Roberts, D. A., et al. (1999). Mapping wildfire fuels using imaging spectrometry along the wildland urban interface. In: Proceedings of the 1999 Joint Fire Science Conference and Workshop, June 17-19, Boise, ID, Vol. 1, pp. 212-223.
75. Rodarmel, C. and Shan, J. (2002). Principal component analysis for hyperspectral image classification. Surveying and Land Information Systems 62, pp.115-122.
76. Roessner, S., Segl, K., Heiden, U. and Kaufmann, H. (2001).Automated differentiation of urban surfaces based on airborne hyperspectral imagery. IEEE Transactions on Geoscience and Remote Sensing 39, pp.1525-1532.
77. Schiefer, S., Hostert, P. and Damm, A. (2006). Correcting brightness gradients in hyperspectral data from urban areas. Remote Sensing of Environment 101, pp. 25-37.
78. Segl, K., Roessner, S., Heiden, U. and Kaufmann, H. (2003). Fusion of spectral and shape features for identification of urban surface cover types using reflective and thermal hyperspectral data. ISPRS Journal of Photogrammetry and Remote Sensing 58, pp. 99-112.
79. Serpico, S. B. and Moser, G. (2007). Extraction of spectral channels from hyperspectral images for classification purposes. IEEE Transactions on Geoscience and Remote Sensing 45, pp.484-495.
80. Shaw, G. A. and Burke, H. K. (2003). Spectral imaging for remote sensing. Lincoln Laboratory Journal 14, pp 3-28.
81. Shimoni, M. (2009). Advanced Thermal Hyperspectral Sensors and Processing Presented at: 2nd Dual-Use European Security Infra-Red Element (DESIRE) Workshop, October 8, Frascati Rome, Italy.
82. Small, C. (2001). Multiresolution analysis of urban reflectance. IEEE/ISPRS Joint Workshop on Remote Sensing and Data Fusion over Urban Areas, November 8-9, Rome, Italy, paper 23.
83. Small, C. (2005). A global analysis of urban reflectance. International Journal of Remote Sensing 26, pp. 661-681.
84. Somers, B., et al. (2010). An automated waveband selection technique for optimized hyperspectral mixture analysis. International Journal of Remote Sensing 31, pp. 5549-5568.
85. Song, C. (2005). Spectral mixture analysis for subpixel vegetation fractions in the urban environment: how to incorporate endmember variability?. Remote Sensing of Environment 95, pp. 248-263.
86. Stefanov, W. L., et al. (2004). Investigation of human modifications of landscape and climate in the Phoenix, Arizona metropolitan area using MASTER data. The International Archives of the Photogrammetry, Remote Sensing, and Spatial Information Sciences 35, pp. 1339-1347.
87. Tarabalka, Y., Benediktsson, J. A. and Chanussot, J. (2009a). Spectral-spatial classification of hyperspectral imagery based on partitional clustering techniques. IEEE Transactions in Geoscience and Remote Sensing 47, pp. 2973-2987.
88. Tarabalka, Y., Fauvel, M., Chanussot, J. and Benediktsson, J. A. (2009b). SVM- and MRF- based methods for accurate classification of hyperspectral images. IEEE Geosciences and Remote Sensing Letters 7, pp. 736-740.
89. Thomas, C., Doz, S., Briottet, X. and Lachérade, S. (2011). AMARTIS V2: radiative transfer code in the [0.4, 2.5μm] spectral domain dedicated to urban areas. Remote Sensing 3, pp. 1914-1942.
90. Tulla, D. and Camps-Valls, G. (2011). Urban image classification with semisupervised multiscale cluster kernels. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 4, pp. 65-74.
91. Voss, M. and Sugumaran, R. (2008). Seasonal effect on tree species classification in an urban environment using hyperspectral data, LiDAR, and an object oriented approach. Sensors 8, pp. 3020-3036.
92. Walker, J. S. and Blaschke, T. (2008). Object-based land-cover classification for the Phoenix metropolitan area: optimization vs. transportability. International Journal of Remote Sensing 29, pp. 2021-2040.
93. Walker, J. S. and Briggs, J. M. (2007). An object-oriented approach to urban forest mapping in Phoenix. Photogrammetric Engineering and Remote Sensing 73, pp. 577-583.
94. Weng, Q. (2012). Remote sensing of impervious surfaces in the urban areas. Requirements, methods and trends. Remote Sensing of Environment 117, pp. 34-49.
95. Weng, Q., Hu, X. and Lu, D. (2008). Extracting impervious surfaces from medium spatial resolution multispectral and hyperspectral imagery: a comparison. International Journal of Remote Sensing 29, pp. 3209-3232.
96. Xiao, Q., Ustin, S. L. and McPherson, E. G.(2004). Using AVIRIS data and multiple-masking techniques to map urban forest tree species. International Journal of Remote Sensing 20, pp. 5637-5654.
97. Xu, B. and Gong, P. (2007). Land-use/land-cover classification with multispectral and hyperspectral EO-1 data. Photogrammetric Engineering and Remote Sensing 73, pp. 955-965.
98. Xu, W., Wooster, M. J. and Grimmond, C. S. B. (2008). Modelling of urban sensible heat flux at multiple spatial scales: a demonstration using airborne hyperspectral imagery of Shanghai and a temperature-emissivity separation approach. Remote Sensing of Environment 112, pp. 3493-3510.
99. Yang, H., Ma, B., Du, Q. and Yang, C. (2010a). Improving urban land use and land cover classification from high spatial resolution imagery using contextual information. Journal of Applied Remote Sensing 4, article 041890.
100. Yang, H., Ma, B., Du, Q. and Yang, C. (2010b). Decision fusion on supervised and unsupervised classifiers for hyperspectral imagery. IEEE Geoscience and Remote Sensing Letters 7, pp. 875-879.
101. Zhao, Y.-Q., Zhang, L. and Kong, S. G. (2011). Band-subset-based clustering and fusion for hyperspectral imagery classification. IEEE Transactions in Geoscience and Remote Sensing 49, pp. 747-756.