메뉴 건너뛰기




Volumn 8, Issue 9, 2016, Pages

Tree species classification in temperate forests using Formosat-2 satellite image time series

Author keywords

[No Author keywords available]

Indexed keywords

BIODIVERSITY; CLIMATE CHANGE; ECOSYSTEMS; IMAGE CLASSIFICATION; MAPPING; NEAREST NEIGHBOR SEARCH; SATELLITE IMAGERY; SATELLITES; TIME SERIES;

EID: 85016832978     PISSN: None     EISSN: 20724292     Source Type: Journal    
DOI: 10.3390/rs8090734     Document Type: Article
Times cited : (70)

References (71)
  • 3
    • 84876972950 scopus 로고    scopus 로고
    • Tree-species range shifts in a changing climate: Detecting, modeling, assisting
    • Iverson, L.R.; McKenzie, D. Tree-species range shifts in a changing climate: Detecting, modeling, assisting. Landsc. Ecol. 2013, 28, 879-889.
    • (2013) Landsc. Ecol. , vol.28 , pp. 879-889
    • Iverson, L.R.1    McKenzie, D.2
  • 7
    • 84941334770 scopus 로고    scopus 로고
    • Mapping dominant tree species over large forested areas using Landsat Best-Available-Pixel image composites
    • Thompson, S.D.; Nelson, T.A.; White, J.C.; Wulder, M.A. Mapping dominant tree species over large forested areas using Landsat Best-Available-Pixel image composites. Can. J. Remote Sens. 2015, 41, 203-218.
    • (2015) Can. J. Remote Sens. , vol.41 , pp. 203-218
    • Thompson, S.D.1    Nelson, T.A.2    White, J.C.3    Wulder, M.A.4
  • 8
    • 0034610199 scopus 로고    scopus 로고
    • Predictive habitat distribution models in ecology
    • Guisan, A.; Zimmermann, N.E. Predictive habitat distribution models in ecology. Ecol. Model. 2000, 135, 147-186.
    • (2000) Ecol. Model. , vol.135 , pp. 147-186
    • Guisan, A.1    Zimmermann, N.E.2
  • 10
    • 38349150302 scopus 로고    scopus 로고
    • Estimating potential habitat for 134 eastern US tree species under six climate scenarios
    • Iverson, L.R.; Prasad, A.M.; Matthews, S.N.; Peters, M. Estimating potential habitat for 134 eastern US tree species under six climate scenarios. For. Ecol. Manag. 2008, 254, 390-406.
    • (2008) For. Ecol. Manag. , vol.254 , pp. 390-406
    • Iverson, L.R.1    Prasad, A.M.2    Matthews, S.N.3    Peters, M.4
  • 12
    • 0031883060 scopus 로고    scopus 로고
    • Satellite remote sensing for forestry planning-A review
    • Holmgren, P.; Thuressopn, T. Satellite remote sensing for forestry planning-A review. Scand. J. For. Res. 1998, 13, 90-110.
    • (1998) Scand. J. For. Res. , vol.13 , pp. 90-110
    • Holmgren, P.1    Thuressopn, T.2
  • 13
    • 15844428374 scopus 로고    scopus 로고
    • Satellite remote sensing of forest resources: Three decades of research development
    • Boyd, D.S.; Danson, F.M. Satellite remote sensing of forest resources: Three decades of research development. Prog. Phys. Geogr. 2005, 29, 1-26.
    • (2005) Prog. Phys. Geogr. , vol.29 , pp. 1-26
    • Boyd, D.S.1    Danson, F.M.2
  • 14
    • 0035882583 scopus 로고    scopus 로고
    • Using remote sensing to assess biodiversity
    • Nagendra, H. Using remote sensing to assess biodiversity. Int. J. Remote Sens. 2001, 22, 2377-2400.
    • (2001) Int. J. Remote Sens. , vol.22 , pp. 2377-2400
    • Nagendra, H.1
  • 15
    • 0029667336 scopus 로고    scopus 로고
    • Semi-automated procedures for tree species identification in high spatial resolution data from digitized colour infrared-aerial photography
    • Meyer, P.; Staenzb, K.; Ittena, K.I. Semi-automated procedures for tree species identification in high spatial resolution data from digitized colour infrared-aerial photography. ISPRS J. Photogramm. Remote Sens. 1996, 51, 5-16.
    • (1996) ISPRS J. Photogramm. Remote Sens. , vol.51 , pp. 5-16
    • Meyer, P.1    Staenzb, K.2    Ittena, K.I.3
  • 17
    • 0037121509 scopus 로고    scopus 로고
    • Individual tree-based species classification in high spatial resolution aerial images of forests using fuzzy sets
    • Brandtberg, T. Individual tree-based species classification in high spatial resolution aerial images of forests using fuzzy sets. Fuzzy Sets Syst. 2002, 132, 371-387.
    • (2002) Fuzzy Sets Syst. , vol.132 , pp. 371-387
    • Brandtberg, T.1
  • 18
    • 2942719059 scopus 로고    scopus 로고
    • Species classification of individually segmented tree crowns in high-resolution aerial images using radiometric and morphologic image measures
    • Erikson, M. Species classification of individually segmented tree crowns in high-resolution aerial images using radiometric and morphologic image measures. Remote Sens. Environ. 2004, 91, 469-477.
    • (2004) Remote Sens. Environ. , vol.91 , pp. 469-477
    • Erikson, M.1
  • 19
    • 0034627794 scopus 로고    scopus 로고
    • Incorporating texture into classification of forest species composition from airborne multispectral images
    • Franklin, S.E.; Hall, R.J.; Moskal, L.M.; Maudie, A.J.; Lavigne, M. Incorporating texture into classification of forest species composition from airborne multispectral images. Int. J. Remote Sens. 2000, 21, 61-79.
    • (2000) Int. J. Remote Sens. , vol.21 , pp. 61-79
    • Franklin, S.E.1    Hall, R.J.2    Moskal, L.M.3    Maudie, A.J.4    Lavigne, M.5
  • 20
    • 4344717974 scopus 로고    scopus 로고
    • Discrimination automatique de peuplements forestiers à partir d'orthophotos numériques couleur: Un cas d'etude en Belgique
    • Kayitakire, F.; Giot, P.; Defourny, P. Discrimination automatique de peuplements forestiers à partir d'orthophotos numériques couleur: Un cas d'etude en Belgique. Can. J. Remote Sens. 2002, 28, 629-640.
    • (2002) Can. J. Remote Sens. , vol.28 , pp. 629-640
    • Kayitakire, F.1    Giot, P.2    Defourny, P.3
  • 21
    • 77958099826 scopus 로고    scopus 로고
    • Semi-automatic classification of tree species in different forest ecosystems by spectral and geometric variables derived from Airborne Digital Sensor (ADS40) and RC30 data
    • Waser, L.T.; Ginzler, C.; Kuechler, M.; Baltsavias, E.; Hurni, L. Semi-automatic classification of tree species in different forest ecosystems by spectral and geometric variables derived from Airborne Digital Sensor (ADS40) and RC30 data. Remote Sens. Environ. 2011, 115, 76-85.
    • (2011) Remote Sens. Environ. , vol.115 , pp. 76-85
    • Waser, L.T.1    Ginzler, C.2    Kuechler, M.3    Baltsavias, E.4    Hurni, L.5
  • 22
    • 0347948379 scopus 로고    scopus 로고
    • Exploitation of very high resolution satellite data for tree species identification
    • Carleer, A.; Wolff, E. Exploitation of very high resolution satellite data for tree species identification. Photogramm. Eng. Remote Sens. 2004, 70, 135-140.
    • (2004) Photogramm. Eng. Remote Sens. , vol.70 , pp. 135-140
    • Carleer, A.1    Wolff, E.2
  • 23
    • 84870760985 scopus 로고    scopus 로고
    • Tree species classification with random forest using very high spatial resolution 8-band WorldView satellite data
    • Immitzer, M.; Atzberger, C.; Koukal, T. Tree species classification with random forest using very high spatial resolution 8-band WorldView satellite data. Remote Sens. 2012, 4, 2661-2693.
    • (2012) Remote Sens. , vol.4 , pp. 2661-2693
    • Immitzer, M.1    Atzberger, C.2    Koukal, T.3
  • 24
    • 84929346514 scopus 로고    scopus 로고
    • Classification of tree species in overstorey canopy of subtropical forest using QuickBird images
    • Lin, C.; Popescu, S.C.; Thomson, G.; Tsogt, K.; Chang, C.I. Classification of tree species in overstorey canopy of subtropical forest using QuickBird images. PLoS ONE 2015, 10, e0125554.
    • (2015) PLoS ONE , vol.10 , pp. e0125554
    • Lin, C.1    Popescu, S.C.2    Thomson, G.3    Tsogt, K.4    Chang, C.I.5
  • 25
    • 0028978288 scopus 로고
    • Improved forest classification in the northern Lake States using multi-temporal Landsat imagery
    • Wolter, P.T.; Mladenoff, D.J.; Host, G.E.; Crow, T.R. Improved forest classification in the northern Lake States using multi-temporal Landsat imagery. Photogramm. Eng. Remote Sens. 1995, 61, 1129-1143.
    • (1995) Photogramm. Eng. Remote Sens. , vol.61 , pp. 1129-1143
    • Wolter, P.T.1    Mladenoff, D.J.2    Host, G.E.3    Crow, T.R.4
  • 26
    • 0030208643 scopus 로고    scopus 로고
    • Classification of tropical forest classes from Landsat TM data
    • Foody, G.M.; Hill, R.A. Classification of tropical forest classes from Landsat TM data. Int. J. Remote Sens. 1996, 17, 2353-2367.
    • (1996) Int. J. Remote Sens. , vol.17 , pp. 2353-2367
    • Foody, G.M.1    Hill, R.A.2
  • 27
    • 0034082989 scopus 로고    scopus 로고
    • Using vegetation reflectance variability for species level classification of hyperspectral data
    • Cochrane, M.A. Using vegetation reflectance variability for species level classification of hyperspectral data. Int. J. Remote Sens. 2000, 21, 2075-2087.
    • (2000) Int. J. Remote Sens. , vol.21 , pp. 2075-2087
    • Cochrane, M.A.1
  • 28
    • 84871745702 scopus 로고    scopus 로고
    • Tree species discrimination in tropical forests using airborne imaging spectroscopy
    • Féret, J.B.; Asner, G.P. Tree species discrimination in tropical forests using airborne imaging spectroscopy. IEEE Trans. Geosci. Remote Sens. 2012, 51, 73-84.
    • (2012) IEEE Trans. Geosci. Remote Sens. , vol.51 , pp. 73-84
    • Féret, J.B.1    Asner, G.P.2
  • 29
    • 84878355763 scopus 로고    scopus 로고
    • Hyperspectral discrimination of tree species with different classifications using single-and multiple-endmember
    • Ghiyamat, A.; Shafri, H.Z.; Mahdiraji, G.A.; Shariff, A.R.M.; Mansor, S. Hyperspectral discrimination of tree species with different classifications using single-and multiple-endmember. Int. J. Appl. Earth Obs. Geoinf. 2013, 23, 177-191.
    • (2013) Int. J. Appl. Earth Obs. Geoinf. , vol.23 , pp. 177-191
    • Ghiyamat, A.1    Shafri, H.Z.2    Mahdiraji, G.A.3    Shariff, A.R.M.4    Mansor, S.5
  • 30
    • 84897452056 scopus 로고    scopus 로고
    • Forest tree species discrimination in western Himalaya using EO-1 Hyperion
    • George, R.; Padaliab, H.; Kushwahab, S.P. Forest tree species discrimination in western Himalaya using EO-1 Hyperion. Int. J. Appl. Earth Obs. Geoinf. 2014, 28, 140-149.
    • (2014) Int. J. Appl. Earth Obs. Geoinf. , vol.28 , pp. 140-149
    • George, R.1    Padaliab, H.2    Kushwahab, S.P.3
  • 31
    • 79952735042 scopus 로고    scopus 로고
    • Exploring full-waveform LiDAR parameters for tree species classification
    • Heinzel, J.; Koch, B. Exploring full-waveform LiDAR parameters for tree species classification. Int. J. Appl. Earth Obs. Geoinf. 2011, 13, 152-160.
    • (2011) Int. J. Appl. Earth Obs. Geoinf. , vol.13 , pp. 152-160
    • Heinzel, J.1    Koch, B.2
  • 32
    • 77949657728 scopus 로고    scopus 로고
    • Synergistic use of QuickBird multispectral imagery and LIDAR data for object-based forest species classification
    • Ke, Y.; Quackenbush, L.J.; Im, J. Synergistic use of QuickBird multispectral imagery and LIDAR data for object-based forest species classification. Remote Sens. Environ. 2010, 114, 1141-1154.
    • (2010) Remote Sens. Environ. , vol.114 , pp. 1141-1154
    • Ke, Y.1    Quackenbush, L.J.2    Im, J.3
  • 33
    • 84859928062 scopus 로고    scopus 로고
    • Tree species classification in the Southern Alps based on the fusion of very high geometrical resolution multispectral/hyperspectral images and LiDAR data
    • Dalponte, M.; Bruzzone, L.; Gianelle, D. Tree species classification in the Southern Alps based on the fusion of very high geometrical resolution multispectral/hyperspectral images and LiDAR data. Remote Sens. Environ. 2012, 123, 258-270.
    • (2012) Remote Sens. Environ. , vol.123 , pp. 258-270
    • Dalponte, M.1    Bruzzone, L.2    Gianelle, D.3
  • 34
    • 84883503642 scopus 로고    scopus 로고
    • Combining ensemble modeling and remote sensing for mapping individual tree species at high spatial resolution
    • Engler, R.; Waser, L.T.; Zimmermann, N.E.; Schaub, M.; Berdos, S.; Ginzler, C.; Psomas, A. Combining ensemble modeling and remote sensing for mapping individual tree species at high spatial resolution. For. Ecol. Manag. 2013, 310, 64-73.
    • (2013) For. Ecol. Manag. , vol.310 , pp. 64-73
    • Engler, R.1    Waser, L.T.2    Zimmermann, N.E.3    Schaub, M.4    Berdos, S.5    Ginzler, C.6    Psomas, A.7
  • 35
    • 84897585667 scopus 로고    scopus 로고
    • A framework for mapping tree species combining hyperspectral and LiDAR data: Role of selected classifiers and sensor across three spatial scales
    • Ghosh, A.; Fassnacht, F.E.; Joshia, P.K.; Koch, B. A framework for mapping tree species combining hyperspectral and LiDAR data: Role of selected classifiers and sensor across three spatial scales. Int. J. Appl. Earth Obs. Geoinf. 2014, 26, 49-63.
    • (2014) Int. J. Appl. Earth Obs. Geoinf. , vol.26 , pp. 49-63
    • Ghosh, A.1    Fassnacht, F.E.2    Joshia, P.K.3    Koch, B.4
  • 36
    • 40049088615 scopus 로고    scopus 로고
    • Species identification of individual trees by combining high resolution LiDAR data with multispectral images
    • Holmgren, J.; Persson, A.; Soderman, U. Species identification of individual trees by combining high resolution LiDAR data with multispectral images. Int. J. Remote Sens. 2008, 29, 1537-1552.
    • (2008) Int. J. Remote Sens. , vol.29 , pp. 1537-1552
    • Holmgren, J.1    Persson, A.2    Soderman, U.3
  • 37
    • 0029256774 scopus 로고
    • Evaluating seasonal variability as an aid to cover-type mapping from Landsat Thematic Mapper data in the Northeast
    • Schriver, J.R.; Congalton, R.G. Evaluating seasonal variability as an aid to cover-type mapping from Landsat Thematic Mapper data in the Northeast. Photogramm. Eng. Remote Sens. 1995, 61, 321-327.
    • (1995) Photogramm. Eng. Remote Sens. , vol.61 , pp. 321-327
    • Schriver, J.R.1    Congalton, R.G.2
  • 38
    • 0031692709 scopus 로고    scopus 로고
    • Delineating forest canopy species in the northeastern United States using multi-temporal TM imagery
    • Mickelson, J.G.; Civco, D.L.; Silander, J.A. Delineating forest canopy species in the northeastern United States using multi-temporal TM imagery. Photogramm. Eng. Remote Sens. 1998, 64, 891-904.
    • (1998) Photogramm. Eng. Remote Sens. , vol.64 , pp. 891-904
    • Mickelson, J.G.1    Civco, D.L.2    Silander, J.A.3
  • 39
    • 0036094212 scopus 로고    scopus 로고
    • Phenological differences in Tasseled Cap indices improve deciduous forest classification
    • Dymond, C.C.; Mladenoff, D.J.; Radeloff, V.C. Phenological differences in Tasseled Cap indices improve deciduous forest classification. Remote Sens. Environ. 2002, 80, 460-472.
    • (2002) Remote Sens. Environ. , vol.80 , pp. 460-472
    • Dymond, C.C.1    Mladenoff, D.J.2    Radeloff, V.C.3
  • 40
    • 84904287664 scopus 로고    scopus 로고
    • Accurate mapping of forest types using dense seasonal Landsat time-series
    • Zhu, X.; Liu, D. Accurate mapping of forest types using dense seasonal Landsat time-series. ISPRS J. Photogramm. Remote Sens. 2014, 96, 1-11.
    • (2014) ISPRS J. Photogramm. Remote Sens. , vol.96 , pp. 1-11
    • Zhu, X.1    Liu, D.2
  • 42
    • 0035124583 scopus 로고    scopus 로고
    • A comparison of multispectral and multitemporal information in high spatial resolution imagery for classification of individual tree species in a temperate hardwood forest
    • Key, T.; Warner, T.A.; McGraw, J.B.; Fajvan, M.A. A comparison of multispectral and multitemporal information in high spatial resolution imagery for classification of individual tree species in a temperate hardwood forest. Remote Sens. Environ. 2001, 75, 100-112.
    • (2001) Remote Sens. Environ. , vol.75 , pp. 100-112
    • Key, T.1    Warner, T.A.2    McGraw, J.B.3    Fajvan, M.A.4
  • 43
    • 77949435300 scopus 로고    scopus 로고
    • Mapping tree species in temperate deciduous woodland using time-series multi-spectral data
    • Hill, R.A.; Wilson, A.K.; George, M.; Hinsley, S.A. Mapping tree species in temperate deciduous woodland using time-series multi-spectral data. Appl. Veg. Sci. 2010, 13, 86-99.
    • (2010) Appl. Veg. Sci. , vol.13 , pp. 86-99
    • Hill, R.A.1    Wilson, A.K.2    George, M.3    Hinsley, S.A.4
  • 44
    • 84878372022 scopus 로고    scopus 로고
    • Urban vegetation classification: Benefits of multitemporal RapidEye satellite data
    • Tigges, J.; Lakes, T.; Hostert, P. Urban vegetation classification: Benefits of multitemporal RapidEye satellite data. Remote Sens. Environ. 2013, 136, 66-75.
    • (2013) Remote Sens. Environ. , vol.136 , pp. 66-75
    • Tigges, J.1    Lakes, T.2    Hostert, P.3
  • 45
    • 61349093180 scopus 로고    scopus 로고
    • VENmS mission: A joint Israel-French Earth Observation scientific mission with High spatial and temporal resolution capabilities
    • Sobrino, J.A., Ed.; Publicacions de la Universitat de València: Valencia, Spain
    • Dedieu, G.; Karnieli, A.; Hagolle, O.; Jeanjean, H.; Cabot, F.; Ferrier, P.; Yaniv, Y. VENmS mission: A joint Israel-French Earth Observation scientific mission with High spatial and temporal resolution capabilities. In Second Recent Advances in Quantitative Remote Sensing; Sobrino, J.A., Ed.; Publicacions de la Universitat de València: Valencia, Spain, 2006; pp. 517-521.
    • (2006) Second Recent Advances in Quantitative Remote Sensing , pp. 517-521
    • Dedieu, G.1    Karnieli, A.2    Hagolle, O.3    Jeanjean, H.4    Cabot, F.5    Ferrier, P.6    Yaniv, Y.7
  • 47
    • 40949131913 scopus 로고    scopus 로고
    • Correction of aerosol effects on multi-temporal images acquired with constant viewing angles: Application to Formosat-2 images
    • Hagolle, O.; Dedieu, G.; Mougenot, B.; Debaecker, V.; Duchemin, B.; Meygret, A. Correction of aerosol effects on multi-temporal images acquired with constant viewing angles: Application to Formosat-2 images. Remote Sens. Environ. 2008, 112, 1689-1701.
    • (2008) Remote Sens. Environ. , vol.112 , pp. 1689-1701
    • Hagolle, O.1    Dedieu, G.2    Mougenot, B.3    Debaecker, V.4    Duchemin, B.5    Meygret, A.6
  • 48
    • 84926382691 scopus 로고    scopus 로고
    • A multi-temporal and multi-spectral method to estimate aerosol optical thickness over land, for the atmospheric correction of Formosat-2, Landsat, VENmS and Sentinel-2 Images
    • Hagolle, O.; Dedieu, G.; Mougenot, B.; Debaecker, V.; Duchemin, B.; Meygret, A. A multi-temporal and multi-spectral method to estimate aerosol optical thickness over land, for the atmospheric correction of Formosat-2, Landsat, VENmS and Sentinel-2 Images. Remote Sens. 2015, 7, 2668-2691.
    • (2015) Remote Sens. , vol.7 , pp. 2668-2691
    • Hagolle, O.1    Dedieu, G.2    Mougenot, B.3    Debaecker, V.4    Duchemin, B.5    Meygret, A.6
  • 49
    • 77955321544 scopus 로고    scopus 로고
    • A multi-temporal method for cloud detection, applied to Formosat-2, VENmS, Landsat and Sentinel-2 Images
    • Hagolle, O.; Huc, M.; Pascual, D.V.; Dedieu, G. A multi-temporal method for cloud detection, applied to Formosat-2, VENmS, Landsat and Sentinel-2 Images. Remote Sens. Environ. 2010, 114, 1747-1755.
    • (2010) Remote Sens. Environ. , vol.114 , pp. 1747-1755
    • Hagolle, O.1    Huc, M.2    Pascual, D.V.3    Dedieu, G.4
  • 50
    • 25044469436 scopus 로고    scopus 로고
    • A perfect smoother
    • Eilers, P. A perfect smoother. Anal. Chem. 2011, 75, 3299-3304.
    • (2011) Anal. Chem. , vol.75 , pp. 3299-3304
    • Eilers, P.1
  • 51
    • 79960068688 scopus 로고    scopus 로고
    • Evaluating the effectiveness of smoothing algorithms in the absence of ground reference measurements
    • Atzberger, C.; Eilers, P. Evaluating the effectiveness of smoothing algorithms in the absence of ground reference measurements. Int. J. Remote Sens. 2011, 32, 3689-3709.
    • (2011) Int. J. Remote Sens. , vol.32 , pp. 3689-3709
    • Atzberger, C.1    Eilers, P.2
  • 53
    • 67650759361 scopus 로고    scopus 로고
    • Kernel functions analysis for support vector machines for land cover classification
    • Kavzoglu, T.; Colkesen, I. Kernel functions analysis for support vector machines for land cover classification. Int. J. Appl. Earth Obs. Geoinf. 2009, 11, 352-359.
    • (2009) Int. J. Appl. Earth Obs. Geoinf. , vol.11 , pp. 352-359
    • Kavzoglu, T.1    Colkesen, I.2
  • 54
    • 84864524799 scopus 로고    scopus 로고
    • Investigating multiple data sources for tree species classification in temperate forest and use for single tree delineation
    • Heinzel, J.; Koch, B. Investigating multiple data sources for tree species classification in temperate forest and use for single tree delineation. Int. J. Appl. Earth Obs. Geoinf. 2012, 18, 101-110.
    • (2012) Int. J. Appl. Earth Obs. Geoinf. , vol.18 , pp. 101-110
    • Heinzel, J.1    Koch, B.2
  • 55
    • 14644402381 scopus 로고    scopus 로고
    • Partially supervised classification of remote sensing images through SVM-based probability density estimation
    • Mantero, P.; Moser, G.; Serpico, S. Partially supervised classification of remote sensing images through SVM-based probability density estimation. IEEE Trans. Geosci. Remote Sens. 2005, 43, 559-570.
    • (2005) IEEE Trans. Geosci. Remote Sens. , vol.43 , pp. 559-570
    • Mantero, P.1    Moser, G.2    Serpico, S.3
  • 58
    • 84952019779 scopus 로고    scopus 로고
    • An evaluation of time-series smoothing algorithms for land-cover classifications using MODIS-NDVI multi-temporal data
    • Shao, Y.; Lunetta, R.; Wheeler, B.; Iiames, J.; Campbell, J. An evaluation of time-series smoothing algorithms for land-cover classifications using MODIS-NDVI multi-temporal data. Remote Sens. Environ. 2016, 174, 258-265.
    • (2016) Remote Sens. Environ. , vol.174 , pp. 258-265
    • Shao, Y.1    Lunetta, R.2    Wheeler, B.3    Iiames, J.4    Campbell, J.5
  • 59
    • 0026278621 scopus 로고
    • A review of assessing the accuracy of classifictions of remotely sensed data
    • Congalton, R.G. A review of assessing the accuracy of classifictions of remotely sensed data. Remote Sens. Environ. 1991, 37, 35-46.
    • (1991) Remote Sens. Environ. , vol.37 , pp. 35-46
    • Congalton, R.G.1
  • 60
    • 70449338714 scopus 로고    scopus 로고
    • Sample size determination for image classification accuracy assessment and comparison
    • Foody, G.M. Sample size determination for image classification accuracy assessment and comparison. Int. J. Remote Sens. 2009, 30, 5273-5291.
    • (2009) Int. J. Remote Sens. , vol.30 , pp. 5273-5291
    • Foody, G.M.1
  • 61
    • 33745756516 scopus 로고    scopus 로고
    • The use of small training sets containing mixed pixels for accurate hard image classification: Training on mixed spectral responses for classification by a SVM
    • Foody, G.M.; Mathur, A. The use of small training sets containing mixed pixels for accurate hard image classification: Training on mixed spectral responses for classification by a SVM. Remote Sens. Environ. 2006, 103, 179-189.
    • (2006) Remote Sens. Environ. , vol.103 , pp. 179-189
    • Foody, G.M.1    Mathur, A.2
  • 62
    • 0013326060 scopus 로고    scopus 로고
    • Feature selection for classification
    • Dash, M.; Liu, H. Feature selection for classification. Intell. Data Anal. 1997, 1, 131-156.
    • (1997) Intell. Data Anal. , vol.1 , pp. 131-156
    • Dash, M.1    Liu, H.2
  • 63
    • 85027943988 scopus 로고    scopus 로고
    • Fast forward feature selection of hyperspectral images for classification with Gaussian mixture models
    • Fauvel, M.; Dechesne, C.; Zullo, A.; Ferraty, F. Fast forward feature selection of hyperspectral images for classification with Gaussian mixture models. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2015, 8, 2824-2831.
    • (2015) IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. , vol.8 , pp. 2824-2831
    • Fauvel, M.1    Dechesne, C.2    Zullo, A.3    Ferraty, F.4
  • 65
    • 84962601050 scopus 로고    scopus 로고
    • Tree species abundance predictions in a tropical agricultural landscape with a supervised classification model and imbalanced data
    • Graves, S.J.; Asner, G.P.; Martin, R.E.; Anderson, C.B.; Colgan, M.S.; Kalantari, L.; Bohlman, S.A. Tree species abundance predictions in a tropical agricultural landscape with a supervised classification model and imbalanced data. Remote Sens. 2016, 8, doi:10.3390/rs8020161.
    • (2016) Remote Sens. , vol.8
    • Graves, S.J.1    Asner, G.P.2    Martin, R.E.3    Anderson, C.B.4    Colgan, M.S.5    Kalantari, L.6    Bohlman, S.A.7
  • 66
    • 39849102390 scopus 로고    scopus 로고
    • Mapping forest alliances and associations using fuzzy systems and nearest neighbor classifiers
    • Triepke, F.; Brewer, C.; Leavell, D.; Novak, S. Mapping forest alliances and associations using fuzzy systems and nearest neighbor classifiers. Remote Sens. Environ. 2008, 112, 1037-1050.
    • (2008) Remote Sens. Environ. , vol.112 , pp. 1037-1050
    • Triepke, F.1    Brewer, C.2    Leavell, D.3    Novak, S.4
  • 67
    • 34347380005 scopus 로고    scopus 로고
    • Estimating species abundance in a northern temperate forest using spectral mixture analysis
    • Plourde, L.; Ollinger, S.; Smith, M.L.; Martin, M. Estimating species abundance in a northern temperate forest using spectral mixture analysis. Photogramm. Eng. Remote Sens. 2007, 73, 829-840.
    • (2007) Photogramm. Eng. Remote Sens. , vol.73 , pp. 829-840
    • Plourde, L.1    Ollinger, S.2    Smith, M.L.3    Martin, M.4
  • 68
    • 0029667619 scopus 로고    scopus 로고
    • Optimistic bias in classification accuracy assessment
    • Hammond, T.; Verbyla, D. Optimistic bias in classification accuracy assessment. Int. J. Remote Sens. 1996, 7, 1261-1266.
    • (1996) Int. J. Remote Sens. , vol.7 , pp. 1261-1266
    • Hammond, T.1    Verbyla, D.2
  • 70
    • 61349091068 scopus 로고    scopus 로고
    • The effect of spatial autocorrelation and class proportion on the accuracy measures from different sampling designs
    • Chen, D.; Wei, H. The effect of spatial autocorrelation and class proportion on the accuracy measures from different sampling designs. ISPRS J. Photogramm. Remote Sens. 2009, 64, 140-150.
    • (2009) ISPRS J. Photogramm. Remote Sens. , vol.64 , pp. 140-150
    • Chen, D.1    Wei, H.2
  • 71
    • 85019718109 scopus 로고    scopus 로고
    • Impact of training and validation sample selection on classification accuracy and accuracy assessment when using reference polygons in object-based classification
    • Zhen, Z.; Quackenbush, L.; Stehman, S.; Zhang, L. Impact of training and validation sample selection on classification accuracy and accuracy assessment when using reference polygons in object-based classification. Int. J. Remote Sens. 2001, 22, 2377-2400.
    • (2001) Int. J. Remote Sens. , vol.22 , pp. 2377-2400
    • Zhen, Z.1    Quackenbush, L.2    Stehman, S.3    Zhang, L.4


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.