Browse > Article
http://dx.doi.org/10.7780/kjrs.2005.21.4.341

Current Status of Hyperspectral Remote Sensing: Principle, Data Processing Techniques, and Applications  

Kim Sun-Hwa (Department of Geoinformatic Engineering, Inha University)
Ma Jung-Rim (Department of Geoinformatic Engineering, Inha University)
Kook Min-Jung (Department of Geoinformatic Engineering, Inha University)
Lee Kyu-Sung (Department of Geoinformatic Engineering, Inha University)
Publication Information
Korean Journal of Remote Sensing / v.21, no.4, 2005 , pp. 341-369 More about this Journal
Abstract
Hyperspectral images have emerged as a new and promising remote sensing data that can overcome the limitations of existing optical image data. This study was designed to provide a comprehensive review on definition, data processing methods, and applications of hyperspectral data. Various types of airborne, spaceborne, and field hyperspectral image sensors were surveyed from the available literatures and internet search. To understand the current status of hyperspectral remote sensing technology and research development, we collected several hundreds research papers from international journals (IEEE Transactions on Geoscience and Remote Sensing, International Journal of Remote Sensing, Remote Sensing of Environment and AVIRIS Workshop Proceedings), and categorized them by sensor types, data processing techniques, and applications. Although several hyperspectral sensors have been developing, AVIRIS has been a primary data source that the most hyperspectral remote sensing researches were relied on. Since hyperspectral data have very large data volume with many spectral bands, several data processing techniques that are particularly oriented to hyperspectral data have been developed. Although atmospheric correction, spectral mixture analysis, and spectral feature extraction are among those processing techniques, they are still in experimental stage and need further refinement until the fully operational adaptation. Geology and mineral exploration were major application in early stage of hyperspectral sensing because of the distinct spectral features of rock and minerals that could be easily observed with hyperspectral data. The applications of hyperspectral sensing have been expanding to vegetation, water resources, and military areas where the multispectral sensing was not very effective to extract necessary information.
Keywords
hyperspectral sensing; imaging spectroscopy; spectral library; spectral mixture analysis; feature extraction; application;
Citations & Related Records
연도 인용수 순위
  • Reference
1 Abousleman, G. P., M. W. Marcellin, and B. R Hoot, 1995. Compression of hyperspectral imagery using the 3-D DCT and Hybrid DPCMj DCT, IEEE Transactions on Geoscience and Remote Sensing, 33(1): 26-34   DOI   ScienceOn
2 Aiazzi, B., L. Alparone, and S. Baronti, 2001. Near Lossless compression of 3-D optical data IEEE Transactions on Geoscience and Remote Sensing, 39(11): 2547-2557   DOI   ScienceOn
3 Asner, G. P., M. M. C. Bustamante, and A. R. Townsend, 2003. Scale dependence of biophysical structrue in deforested areas bordering the Tapaos National Forest, Central Amazon, Remote Sensing of Environment, 87: 507-520   DOI   ScienceOn
4 Ben-Dor, E., N. Levin, and H. Saaroni, 2001. A spectral based recognition of the urban environment using the visible and near infrared spectral region. A case study over Tel Aviv, Israel International Journal of Remote Sensing, 22(11): 2193-2218
5 Brando, V. E. and A. G. Dekker, 2003. Satellite Hyperspectral Remote Sensing of Estimating Esturaine and Coastal Water Quality, IEEE Transactions on Geoscience and Remote Sensing, 41(6): 1378-1387   DOI   ScienceOn
6 Cairns, B., B. E. Carlson, R. Ing., A. A. Lacis, and V. Oinas, 2003. Atmospheric Correction and Its Application to an Analysis of hyperion Data, IEEE Transactions on Geoscience and Remote Sensing, 41(6): 1232-1244   DOI   ScienceOn
7 Crosta, A. P., C. Sabine, and J. V. Taranik, 1998. Hydrothermal Alteration Mapping at bodie, California, Using A VIRIS hyperspectral data, Remote Sensing of Environment, 65: 309-319   DOI   ScienceOn
8 Green, A. A., M. Berman, P. Switzer, and M. D. Graig, 1998. A transformation for ordering multispectral data in terms of image quality with implications for noise removal, IEEE Transactions on Geoscience and Remote Sensing, 26(1): 65-74   DOI   ScienceOn
9 Hapke, B., 1993. Theory of Reflectance and Emittance Spectroscopy, Cambridge, U.K.: Cambridge Univ. Press
10 Herold, M., D. A. Roberts, M. E. Gardner, and P. E. Dennison, 2004. Spectrometry for urban area remote sensing-development and analysis of a spectral library from 350 to 2400nm, Remote Sensing of Environment, 91: 304-319   DOI   ScienceOn
11 Jiang, X., L. Tang, and C. Wang, 2004. Spectral characteristics and feature selection of hyperspectral remote sensing data, International Journal of Renwte Sensing, 25(1): 51-59   DOI   ScienceOn
12 Jong, S. M., E. J. Pebesma, and B. Lacaze, 2003. Above ground biomass assessment of Mediterranean forests using airborne imaging spectrometry: the DAIS Peyne experiment, International Journal of Remote Sensing, 24(7): 1505-1520   DOI   ScienceOn
13 Kallio, K., S. Koponen, and J. Pulliainen, 2003. Feasibility of airborne imaging spectrometry for lake monitoring a case study of spatial chlorophyll a distribution in two, International Journal of Renwte Sensing, 24(19): 3771-3790   DOI   ScienceOn
14 Kim, B. Y. and D. A. Landgrebe, 1991. Hierarchical classifier design in high dimensional, numerous class cases, IEEE Transactions on Geoscience and Remote Sensing, 29(4): 518-528   DOI   ScienceOn
15 Landgrebe D., 2001. Analysis of Multispectral and Hyperspectral Image Data, John Wiley & Sans, 2001
16 Kokaly, R. F., D. G. Despain, R. N, Clark, and K. E. Livo, 2003. Mapping vegetation in yellowstone national park using spectral feature analysis of A VIRIS data, Remote Sensing of Environment, 84: 437-456   DOI   ScienceOn
17 Kruse, F. A, J. W. Boardman, and J. F. Huntington, 2003. Comparison of Airborne hyperspectral Data and EO-1 Hyperion for Mineral Mapping; IEEE Transactions on Geoscience and Remote Sensing, 41(6): 1388-1400   DOI   ScienceOn
18 Kumar,S., J. Ghosh, and M. M. Crawford, 2001. Best bases feature extraction algorithms for classification of hyperspectral data, IEEE Transactions on Geoscience and Remote Sensing, 39(7): 1368-1379   DOI   ScienceOn
19 Okin, G. S. and T. H. Painter, 2004. Effect of grain size on remotely sensed spectral reflectance of sandy desert surfaces, Remote Sensing of Environment, 89: 272-280   DOI   ScienceOn
20 Pu, R., Q. Yu. P. Gong, and G. S. Biging, 2005. EO-1 Hyperion, ALI and Landsat 7 ETM+ data comparison for estimating forest crown closure and leaf area index, International Journal of Remote Sensing, 26(3): 457-474   DOI   ScienceOn
21 Roberts, D. A., P. E. Dennison, M. E. Gardner, Y. Hetzel, S. L. Ustin, and C. T. Lee, 2003. Evaluation of the Potential of Hyperion for Fire Danger Assessment by Comparison to the Airborne Visible/Infrared Imaging Spectrometer, IEEE Transactions on Geoscience and Remote Sensing, 41(6): 1297-1310   DOI   ScienceOn
22 Serpico, S. B. and L. Bruzzone, 2001. Anew search algorithm for feature selection in hyperspectral remote sensing images, IEEE Transactions on Geoscience and Remote Sensing, 39(7): 1360-1367   DOI   ScienceOn
23 Zarco Tejada, P. J., J. C. Pushnik, S. Dobrowskui, and S. L. Ustin, 2003. Steady state chlorophyll a fluorescence detection from canopy derivatice reflectance and double peak red edge effects, Remote Sensing of Environment, 84: 283-294   DOI   ScienceOn
24 Viggh, H. E. M. and D. H. Staelin, 2003. Spatial surface prior infomation reflectance estimation (SPIRE) algorithm, IEEE Transactions on Geoscience and Remote Sensing, 41(11): 2424-2435   DOI   ScienceOn
25 Warner, T. A., K. Steinmaus, and H. Foote, 1999. An evaluation of spatial autocorrelation feature selection, International Journal of Remote Sensing, 20(8): 1601-1616   DOI   ScienceOn
26 Yao, H. and L. Tian, 2003. A genetic algorithm based selective principal component analysis (GA SPCA) method for high dimensional data feature extraction, IEEE Transactions on Geoscience and Remote Sensing, 41(6): 1469-1478   DOI   ScienceOn
27 Garcia, M. and S. L. Us tin, 2001. Detection of Interannual Vegetaion Responses to Climatic Variability using AVIRIS data in a coastal savanna in California, IEEE Transactions on Geoscience and Remote Sensing, 39(7): 14801490   DOI   ScienceOn
28 JPL, 2005. NASA JPL Homepage (http://aviris.jpl. nasa.gov)
29 Lee, Z. P. and K. L. Carder, 2004. Absorption spectrum of phytoplankton pigments derived from hyperspectal remote sensing reflectance, Remote Sensing of Environment, 89: 361-368   DOI   ScienceOn
30 Atkinson, P. M., 1997. On estimating measurement error in remotely sensed images with the variogram, International Journal of Remote Sensing, 18(14): 3075-3084   DOI
31 Goetz, A. F. H, B. C. Kinde , M. Ferri, and Z. Qu, 2003. HATCH: Results from simulated radiances, AVIRIS and Hyperion, IEEE Transactions on Geoscience and Remote Sensing, 41(6): 1215-1222   DOI   ScienceOn
32 Jia, X. and J. A. Richards, 1998. Progressive two class decision classifier for optimization class discriminations, Remote Sensing of Environment, 63: 289-297   DOI   ScienceOn
33 Jia, X. and J. A. Richards, 2002. Cluster Space Representation for Hyperspectral Data Classification, IEEE Transactions on Geoscience and Remote Sensing, 40(3): 593-598   DOI   ScienceOn
34 Jimenez, L. O. and D. A. Landgrebe, 1999. Hyperspectral Data Analysis and supervised feature reduction via projection pursuit, IEEE Transactions on Geoscience and Remote Sensing, 37(6): 2653-2667   DOI   ScienceOn
35 Lu, D., P. Mausel, E. Brondizio, and E. Moran, 2004. Change Detection techniques, International Journal of Remote Sensing, 25(12): 2365-2407   DOI   ScienceOn
36 Goetz A. F. H., 1991. Imaging spectrometry for studying Earth, Air, Fire and Water, EARSeL Advances in Remote Sensing, 1: 3-15
37 Rees, W. G., O. V. Tutubalina, and E.I. Golubeva, 2004. Reflectance spectra of subarctic lichens between 400 and 2400 nm, Remote Sensing of Environment, 90: 281-292   DOI   ScienceOn
38 Blackburn, G. A. and E. J. Milton., 1997. An ecological survey of deciduous woodlands using airborne remote sensing and geographical information system(GIS), International Journal of Remote Sensing, 18(9): 1919-1935   DOI
39 Richter, R. and D. Schlapfer, 2002. Geo atmospheric processing of airborne imaging spectrometry data. Part 2: atmospheric .topographic correction, International Journal of Remote Sensing, 23(13): 2631-2649   DOI   ScienceOn
40 Gao, B. C, M. J. Montes, and C. O. Davis, 2004. Refinement of wavelength calibrations of hyperspectral imaging data using a spectrum matching technique, Remote Sensing of Environment, 90: 424-433   DOI   ScienceOn
41 Hoffman, R. N. and D. W. Johnson, 1994. Application of EOF's to Multispectral Imagery: Data Compression and Noise Detection for AVIRIS, IEEE Transactions on Geoscience and Remote Sensing, 32(1): 25-34   DOI   ScienceOn
42 Pu, R, Gong P., and G. S. Biging, 2003. Simple calibration of AVIRIS data and LAI mapping of forest plantation in southern argentina, International Journal of Remote Sensing, 24(23): 4699-4714   DOI   ScienceOn
43 Riano, D., E. Chuvieco, S. Ustin, R Zomer, P. Dennison, D. Roberts, and J. Salas, 2002. Asessment of vegetation regeneration after fire through multi temporal analysis of AVIRIS images in the Santa Monica Mountains, Remote Sensing of Environment, 79: 60-71   DOI   ScienceOn
44 Asner, G. P. and K. B. Heidebrecht, 2002. Spectral unmixing of vegetation, soil and dry carbon cover in arid regions:comparing multispectral and hyperspectral observations, International Journal of Remote Sensing, 23(19): 3939-3958   DOI   ScienceOn
45 Biggar, S. F., K. J. Thome, and W. Wisniewski, 2003. Vicarious radiometric calibration of EO-1 sensors by reference to High-reflectance ground targets, IEEE Transactions on Geoscience and Remote Sensing, 41(6): 11741179   DOI   ScienceOn
46 Palacios Orueta, A. and S. L. Ustin, 1996. Multicariate statistical classification of soil spectra, Remote Sensing of Environment, 57: 108-118   DOI   ScienceOn
47 Shippert, P., 2004. Why Use Hyperspectral Imagery?, Photogrammetric Engineering & Remote Sensing, 70(4): 377-380
48 Chang, C. I. and H. Ren, 2000. An Experiment-Based Quantitative and Comparative Analysis of Target Detection and Image Classification Algorithm for Hyperspectral Imagery, IEEE Transaction on Geoscience and Remote Sensing, 38(2): 1044-1063   DOI   ScienceOn
49 Van der Meer, F., 2003. Bayesian inversion of imaging spectrometer data using a fuzzy geological outcrop model, International Journal of Remote Sensing, 24(22): 4301-4310   DOI   ScienceOn
50 Wagtendonk, J. W, R. R. Root, and C. H. Key, 2004. Comparison of AVIRIS and Landsat ETM + detection capabilities for burn severity, Remote Sensing of Environment, 92: 397-408   DOI   ScienceOn
51 Van der Meer, F., F. Lihui, and J. Bodechtel, 1997 MAIS imaging spectrometer data analysis for Ni Cu prospecting in ultramafic rocks of the Jinchuan group, China International Journal of Remote Sensing, 18(13): 2743-2761   DOI
52 Van der Meer, F., 2000. Spectral curve shape matching with a continnurn removed CCSM algorithm, International Journal of Remote Sensing, 21(16): 3179-3185   DOI   ScienceOn
53 Gu, Y, J. M. Anderson, and J. G. C. Monk, 1999. An approach to the spectral and radiometric calibration of the VIFIS system, International Joural of Remote Sensing, 20(3): 535-548   DOI   ScienceOn
54 Ifarraguerri, A. and C. I. Chang, 1999. Multispectral and Hyperspectral Image Analysis with Convex Cones, IEEE Transactions on Geoscience and Remote Sensing, 37(2): 756-770   DOI   ScienceOn
55 Smith, M. L., M. E. Martin, L. Plourde, and S. V. Ollinger, 2003. Analysis of Hyperspectral data for estimation of temperate forest canopy nitrogen concentration: comparison Between an Airborne(AVIRIS) and a space borne(Hyperion) Senor, IEEE Transactions on Geoscience and Remote Sensing, 41(6): 13321337   DOI   ScienceOn
56 Gu, D., A. R. Gillespie, A. B. Kahle, and F. D. Palluconi, 2000. Autonomous Atmospheric Compensation(AAC) of High Resolution Hyperspectral thermal infrared remote sensing imagery, IEEE Transactions on Geoscience and Remote Sensing, 38(6): 2557-2570   DOI   ScienceOn
57 Seeker, J., K. Staenz, R. P. Gauthier, and P. Budkewitsch, 2001. Vicarious calibration of airborne hyperspectral sensors in operational environments, Remote Sensing of Environment, 76: 81-92   DOI   ScienceOn
58 Resmini, R F., M. E. Kappus, W. S. Sldrich, J. C. Haranyi, and M. Anderson, 1997. Mineral mapping withhyperspectral Digital Imagery Collection Experiment (HYDICE) sensor data at Cuprite, Nevada, U.S.A. International Journal of Remote Sensing, 18(7): 1553-1570   DOI
59 Dennison, P. E. and D. A. Roberts, 2003. The effects of vegetation phenology on endmember selection and species mapping in southern California chaparral, Remote Sensing of Environment, 87: 295-309   DOI   ScienceOn
60 Gelpi, C. C., 2000. Removing Path Scattered Radiance from Over ocean spectrometer Images for Water vapor estimation, Remote Sensing of Environment, 74: 414-421   DOI   ScienceOn
61 Sanders, L. C. J. R. Schott, and R. Raqueno, 2001. AVNIR/SWIR atmospheric correction algorithm for hyperspectal imagery with adjacency effect, Remote Sensing of Environment, 78: 252-263   DOI   ScienceOn
62 Verhoef, W. and H. Bach, 2003. Simulation of hyperspectral and directional radiance images using coupled biophysical and atmosphere radiative transfer models, Remote Sensing of Environment, 87: 23-41   DOI   ScienceOn
63 Silvestri, S., M. Marani, J. Settle, F. Benvenuto, and A. Marani, 2002. Salt marsh vegetation radiomerty data analysis and scaling, Remote Sensing of Environment, 80: 473-482   DOI   ScienceOn
64 Bachmann, C. M., M. H. Bettenhausen, and R. A. Fusina, 2003. A Credit Assignment Approach to fusing Classifiers of Multiseason Hyperspectral Imagery, IEEE Transactions on Geoscience and Remote Sensing, 41(11): 2488-2499   DOI   ScienceOn
65 Hoogenboom, H. J., A. G. Dekker, and I. A. Althuis, 1998. Simulation of A VIRIS Sensitivity for Detecting Chlorophyll over Coastal and Inland Waters, Remote Sensing of Environment, 65: 333-340   DOI   ScienceOn
66 Launeau, P., J. Girardeau, C. Satin, and J. M. Tubia, 2004. Comparison between field measurements and airborne visible and infrared mapping spectrometry(AVIRIS and HyMap) of the Ronda peridotite massif (south west Spain), International Joural of Remote Sensing, 25(14): 2773-2792   DOI   ScienceOn
67 Tu, T. M., C. H. Chen, and C. I. Chang, 1997. A Posteriori Least Squares Orthogonal Subspace projection approach to Desired signature extraction and detection, IEEE Transactions on Geoscience and Remote Sensing, 35(1): 127-139   DOI   ScienceOn
68 Van der Meer, F. and V. Kato, 2002. Developing a schematic petrogenetic transect for a contact aureole using field spectrometry; a case study in Los Santos, Salamanca Province, central western Spain, International Journal of Remote Sensing, 23(23): 5087-5094   DOI   ScienceOn
69 Warner, T. A. and M. C. Shank, 1997. Spatial Autocorrelation Analysis of Hyperspectral Imagery for Feature Selection, Remote Sensing of Environment, 60: 58-70   DOI   ScienceOn
70 Bateson, C. A., G. P. Asner, and C. A. Wessman, 2000. Endmember Bundles: A New Approach to incorporating endmember variability into spectral mixture analysis, IEEE Transactions on Geoscience and Remote Sensing, 38(2): 1083-1094   DOI   ScienceOn
71 Ustin, S. L. and Q. F. Xiao, 2001. Mapping successional boreal forests in interior central Alaska, International Journal of Remote Sensing, 22(6): 1779-1797   DOI
72 Chabrillat, S., P. C. Pinet, G. Ceulneer, P. E. Johnson, and J. F. Mustard, 2000. Ronda peridotite massif: methodology for its geological mapping and lithological discrimination from airborne hyperspecral data, International Journal of Remote Sensing, 21(12): 2363-2388   DOI   ScienceOn
73 Curran, P. J. and J. L. Dungan, 1989. Estimation of Signal to Noise: Anew procedure applied to A VlRIS data, IEEE Transactions on Geoscience and Remote Sensing, 27(5): 620-628   DOI   ScienceOn
74 Ryan, M. J. and J. F. Arnold, 1997. The lossles compression of AVIRIS images by vector quantization, IEEE Transactions on Geoscience and Remote Sensing, 35(3): 546-550   DOI   ScienceOn
75 Keshava, N. and J. F. Mustard, 2002, Spectral Unmixing, IEEE Signal Processing Magazine, 19(1): 44-57   DOI   ScienceOn
76 Niemann, K. O., D. G. Goodenough, and A. S. Bhogal, 2002. Remote Sensing of relative moisture status in old growth Douglas fir, International Journal of Remote Sensing, 23(2): 395-400   DOI   ScienceOn
77 Gong, P., R. Pu, and R. C. Heald, 2002. Analysis of in situ hyperspectral data for nutrient estimation of giant sequoia, International Journal of Remote Sensing, 23(9): 1827-1850   DOI   ScienceOn
78 Asner, G. P. and K. B. Heidebrecht, 2003. Imaging spectroscopy for desertification studies: comparing A VIRIS and EO-1 Hyperion in Argentina Drylands, IEEE Transactions on Geoscience and Remote Sensing, 41(6): 1283-1296   DOI   ScienceOn
79 Bachmann, C. M., 2003. Improving the performance of classifiers in High dimensional remote sensing applications: An adaptive resampling strategy for error prone example, IEEE Transactions on Geoscience and Remote Sensing, 41(9): 2101-2112   DOI   ScienceOn
80 Bruce, L. M., C. Morgan, and S. Larsen, 2001. Automated Detection of subpixel hyperspectral targets with continuous and discrete wavelet transforms, IEEE Transactions on Geoscience and Remote Sensing, 39(10): 2217-2226   DOI   ScienceOn
81 Hochberg, E. J. and M. J. Atkinson, 2003. Capabilities of remote sensors to classify coral, algae, and sand as pure and mixed spectra, Remote Sensing of Environment, 85: 174-189   DOI   ScienceOn
82 Rand, R S. and D. M. Keenan, 2001. A spectral mixture process conditioned by cibbs based partitioning, IEEE Transactions on Geoscience and Remote Sensing, 39(7): 1421-1434   DOI   ScienceOn
83 Teillet, P. M., G. Fedosejevs, R. P. Gauthier, N. T. O'Neill, K. J. Thome, S. F. Biggar, H. Ripley, and A. Meygret, 2001. A generalized approach to the vicarious calibration of multiple Earth observation sensors using hyperspectral data, Remote Sensing of Environment, 77: 304-327   DOI   ScienceOn
84 Metternicht, G. I. and J. A. Zinck, 2003. Remote Sensing of soil salinity: potentials and constraints, Remote Sensing of Environment, 85: 1-20   DOI   ScienceOn
85 Aiazzi, B., P. Alba, L. Alparone, and S. Baronti, 1999. Lossless Compression of Multi/HyperSpectral Imagery Based on a 3-D Fuzzy Prediction, IEEE Transactions on Geoscience and Remote Sensing, 37(5): 2287-2294   DOI   ScienceOn
86 Chang, C. I., 2002. Target Signature constrained mixed pixel classification for hyperspectral imagery, IEEE Transactions on Geoscience and Remote Sensing, 40(5): 1065-1081   DOI   ScienceOn
87 Galvao, L. S., W. P. Filho, M. M. Abdon, E. M. M. L. Novo, J. S. V. Silva, and F. J. Ponzoni, 2003. Spectral reflectance characterization of shallow lakes from the Brazilian Pantanal wetlands with field and airborne hyperspectral data, International Journal of Remote Sensing, 24(21): 4093-4112   DOI   ScienceOn
88 Hubbard, B. E., J. K.Crowley, and D. R. Zimbelman, 2003. Comparative Alteration Mineral Mapping Using Visible to Shortwave Infrared(0.4-2.5,$\mu$m) Hyperion, ALI, and ASTER Imagery, IEEE Transactions on Geoscience and Remote Sensing, 41(6): 14011410   DOI   ScienceOn
89 Apan, A., A. Held, S. Phinn, and J. Markley, 2004. Detecting sugarcane 'orange rust' disease using EO-1 Hyperion hyperspectral imagery, International Journal of Remote Sensing, 25(2): 489-498   DOI   ScienceOn
90 Chang, C. I. and O. Du, 1999. Interference and Noise Adjusted principal components analysis, IEEE Transactions on Geoscience and Remote Sensing, 37(5): 2387-2396   DOI   ScienceOn
91 Kuo, B. C. and D. A Landgrebe, 2002. A Covariance estimator for small sample size classification problems and its application to feature extraction, IEEE Transactions on Geoscience and Remote Sensing, 40(4): 814-81   DOI   ScienceOn
92 Whiting, M. L., L. Li, and S. L. Ustin, 2004. Predicting water content using Gaussian model on soil spectra, Remote Sensing of Environment, 89: 535-552   DOI   ScienceOn
93 Chang, C. I., S. S. Chiang, J. A Smith, and K. W. Ginsberg, 2002. Linear Spectral Random Mixture Analysis for Hyperspectral Imagery, IEEE Transactions on Geoscience and Remote Sensing, 40(2): 375-392   DOI   ScienceOn
94 Drake, N. A., S. Mackin, and J. J. Settle, 1999. Mapping vegetation, soils, and geology in semiarid shrublands using spectral matching and mixture modeling of SWIR A VIRIS iamgery, Remote Sensing of Environment, 68: 12-25   DOI   ScienceOn
95 Pekkarinen, A., 2002. A method for the segmentation of very high spatial resolution images of forested landscapes, International Journal of Remote Sensing, 23(14): 2817-2836   DOI   ScienceOn
96 Yang, H, F. Van der Meer, W Bakke, and Z. J. Tan, A back propagation neural network for mineralogical mapping from AVIRlS data, 1999. International Journal of Remote Sensing, 20(1): 97-110   DOI   ScienceOn
97 Farrand, W. H. and J. C. Harsanyi, 1997. Mapping the distribution of mine tailings in the Coeur d'Alene River Valley, Idaho through the use of a Constrained Energy Minimization technique, Remote Sensing of the Environment, 59: 64-76   DOI   ScienceOn
98 Ingram, P. M. and A. H. Muse, 2001. Sensitivity of Iterative Spectrally Smooth Temperature/ Emissivity Separation to algorithmic assumptions and measurement noise, IEEE Transactions on Geoscience and Remote Sensing, 39(10): 2158-2167   DOI   ScienceOn
99 Marion, R, R Michel, and C. Faye, 2004. Measuring Trace Gases in Plumes From Hyperspectral Remotely Sensed data, IEEE Transactions on Geoscience and Remote Sensing, 42(4): 854-864   DOI   ScienceOn
100 Schmid, T., M. Koch, J. Gumuzzio, and P. M. Mather, 2004. A spectral library for a semi arid wetland and its application to studies of wetland degradation using hyperspectral and multispectral data, International Journal of Remote Sensing, 25(13): 2485-2496   DOI   ScienceOn
101 Chang C. I., Ren H., and Chiang S. S., 2001. Realtime processing algorithms for target detection and classification in hyperspectral imagery, IEEE Transactions on Geoscience and Remote Sensing, 39(4): 760-768   DOI   ScienceOn
102 Datt, B., T. R. McVicar, T. G. Van Niel, D. L. B. Jupp, and J. S. Pearlman, 2003. Preprocessing EO-l Hyperion Hyperspectral data to Support the Application of Agricultural Indexs, IEEE Transactions on Geoscience and Remote Sensing, 41(6): 1246-1259   DOI   ScienceOn
103 Feind, R. E. and R. M. WELCH, 1995. Cloud fraction and cloud shadow property retrievals from coregistered TIMS and AVIRIS imagery: the use of cloud morphology for registration, IEEE Transactions on Geoscience and Remote Sensing, 33(1): 172-184   DOI   ScienceOn
104 Goodenough, D. G., A. Dyk, K. O. Niemann, J. S. Pearlman, H Chen, T. Han, M. Murdoch, and C. West, 2003. Processing hyperion and ALI for Forest Classification, IEEE Transactions on Geoscience and Remote Sensing, 41(6): 1321-1331   DOI   ScienceOn
105 Zarco Tejada, P. J., J. R. Miller, T. L. Noland, G. H. Mohammed, and P. H. Sampson, 2001. Scaling up and model inversion methods with narrowband optical indices for chlorophyll content estimation in closed forest canopies with hyperspectral data, IEEE Transactions on Geoscience and Remote Sensing, 39(7): 1491-1507   DOI   ScienceOn
106 Ben-Dor, E., B. Kindel, and A. F. H. Goetz, 2004. Quality assessment of several methods to recover surface reflectance using synthetic imaging spectroscopy data, Remote Sensing of Environment, 90: 389-404   DOI   ScienceOn
107 Gao, B. C. and A. F. H. Goetz, 1995. Retrieval of Equivalent Water Thickness and Information Related to Biochemical Components of Vegetation Canopies from AVIRIS Data, Remote Sensing of Environment, 52: 155-162   DOI   ScienceOn
108 Roberts, D. A., M. O. Smith, and J. B. Adams, 1993. Green vegetation, nonphotosynthetic vegetation, and soils in AVIRIS data, Remote Sensing of Environment, 44: 255-269   DOI   ScienceOn
109 Thiemann, S. and H. Kaufmann, 2002. Lake water quality monitoring using hyperspectral airborne data a semiempirical multisensor and multitemporal approach for the Mecklenburg Lake Distric, Germany Remote Sensing of Environment, 81: 228-237   DOI   ScienceOn
110 Tu, T. M., C. H. Chen, J. L. Wu, and C. I. Chang, 1998. A Fast Two stage classification method for high dimensional remote sensing data, IEEE Transactions on Geoscience and Remote Sensing, 36(1): 182-191   DOI   ScienceOn
111 Koponen, S., J. Pulliainen, K. Kallio, and M. Hallikainen, 2002. Lake water Quality classification with airborne hyperspectral spectrometer and simulated MERIS data, Remote Sensing of Environment, 79: 51-59   DOI   ScienceOn
112 Benediktsson, J. I., J. R. Sveinsson, and K. Amason, 1995. Classification and Feature Extraction of A VIRIS Data, IEEE Transactions on Geoscience and Remote Sensing, 33(5): 1194-1205   DOI   ScienceOn
113 Manolakis, D., C. Siracusa, and G. Shaw, 2001. Hyperspectral Subpixel Target Detection Using the Linear Mixing Model, IEEE Transactions on Geoscience and Remote Sensing, 39(7): 1392-1409   DOI   ScienceOn
114 Shaw, G. and D. Manolakis, 2002, Singal Processing for Hyperspectral Image Exploitation, IEEE Signal Processing Magazine, 19(1): 12-16
115 Du, Q. and C. I. Chang, 2004. Linear Mixture Analysis based compression for hyperspectral image Analysis, IEEE Transactions on Geoscience and Remote Sensing, 42(4): 875-89l   DOI   ScienceOn
116 Kirkland, L., K. Herr, E. Keirn, P. Adams, J. Salisbury, J. Hackwell, and A Treiman, 2002. First use of an airborne thermal infrared hyperspectral scanner for compositional mapping, Remote Sensing of Environment, 80: 447-459   DOI   ScienceOn
117 Mutanga, O. and A. K. Skidmore, 2004. Integrating imaging spectroscopy and neural networks to map grass uality in the Kruger National Park, South Afria, Remote Sensing of Environment, 90: 104-115   DOI   ScienceOn
118 Gong, P., J. R. Miller, and M. Spanner, 1994. Forest canopy closure from classification and spectral unmixing of scene components multisensor evaluation of an open canopy, IEEE Transactions on Geoscience and Remote Sensing, 32(5): 1067-1080   DOI   ScienceOn
119 Chabrillat, S., A. F. H. Goetz, L. Krosley, and H. W. Olsen, 2002. Use of hyperspectral images in the identification and mapping of expansive clay soils and the role of spatial resolution, Remote Sensing of Environment, 82: 431-445   DOI   ScienceOn
120 Gao, B. C, P. Yang, W. Han, R. R. Li, and W. J. Wiscombe, 2002. An algorithm using visible and 1.38m channels to retrieve cirrus cloud reflectances from aircraft and satellite data, IEEE Transactions on Geoscience and Remote Sensing, 40(8): 1659-1668   DOI   ScienceOn
121 Jacquemoud, S., F. Baret, B. Andrieu, F. M. Danson, and K. Jaggard, 1995. Extraction of Vegetation Biophysical Parameters by Inversion of the PROSPECT + SAIL models on Sugar beet canopy reflectance data. Application to TM and A VIRIS sensors, Remote Sensing of Environment, 52: 163-172   DOI   ScienceOn
122 Kaewpijit, S., J. Le Moigne, and T. El Ghazawi, 2003. Automatic Reduction of Hyperspectral Imagery Using Wavelet Spectral Analysis, IEEE Transactions on Geoscience and Remote Sensing, 41(4): 863-871   DOI   ScienceOn
123 Williams, A. P. and Jr R. E. Hunt, 2002. Estimation of leafy spurge cover from hyperspectral imagery using mixture tuned matched filtering, Remote Sensing of Environment, 82: 446-456   DOI   ScienceOn
124 Longhi, I., M. Sgavetti, R Chiari, and C. Mazzoli, 2001. Spectral analysis and classification of metamorphic rocks from laboratory reflectance spectra in the 0.4-2.5 interval: a tool for hyperspectral data interpretation, International Journal of Remote Sensing, 22(18): 3763-3782   DOI   ScienceOn
125 Hapke, B., 1981. Bidirectional reflectance spectroscopy 1. Theory, J. Geophys. Res., 86: 3039-3054   DOI