참고문헌
- 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. https://doi.org/10.1080/01431160110115960
- Bork, E.W., N.E. West, and K.P. Price, 1999. Calibration of broad- and narrow-band spectral variables for rangeland cover component quantification, International Journal of Remote Sensing, 20(18): 3641-3662. https://doi.org/10.1080/014311699211255
- Bunting, P. and R. Lucas, 2006. The delineation of tree crowns in Australian mixed species forests using hyperspectral compact airborne spectrographic imager (CASI) data, Remote Sensing of Environment, 101: 230-248. https://doi.org/10.1016/j.rse.2005.12.015
- Cha, S.Y., U.H. Pi, J.H. Yi, and C.H. Park, 2011. Identification of two common types of forest cover, Pinus densiflora(Pd) and Querqus mongolica(Qm), using the 1st harmonics of a discrete fourier transform, Korean Journal of Remote Sensing, 27(3): 329-338. https://doi.org/10.7780/kjrs.2011.27.3.329
- Cho, M.A., P. Debba, R. Mathieu, L. Naidoo, J.V. Aardt, and G.P. Asner, 2010. Improving discrimination of Savanna tree species through a multiple-endmember spectral angle mapper approach: canopy-level analysis, IEEE Transactions on Geoscience and Remote Sensing, 48(11): 4133-4142. https://doi.org/10.1109/TGRS.2010.2058579
- Choi, H.A., W.K. Lee, Y.H. Son, T. Kojima, and H. Muraoka, 2010. Vegetation classification using seasonal variation MODIS data, Korean Journal of Remote Sensing, 26(6): 665-673. https://doi.org/10.7780/kjrs.2010.26.6.665
- Chung, S.Y., J.S. Yim, and M.Y. Shin, 2011. A comparison of pixel-and segment-based classification for tree species classification using QuickBird imagery, Journal of Korean Forest Society, 100(4): 540-547 (in Korean with English abstract).
- Demir, B. and S. Erturk, 2010. Empirical mode decomposition of hyperspectral images for support vector machine classification, IEEE Transactions on Geoscience and Remote Sensing, 48(11): 4071-4084. https://doi.org/10.1109/TGRS.2010.2070510
- Enkhbaatar, L., S. Jayakumar, and J. Heo, 2009. Support vector machine and spectral angle mapper classifications of high resolution hyper spectral aerial image, Korean Journal of Remote Sensing, 25(3): 233-242. https://doi.org/10.7780/kjrs.2009.25.3.233
- Frank, T.D., 1988. Mapping dominant vegetation communities in the Colorado Rocky Mountain Front Range with Landsat Thematic Mapper and digital terrain data. Photogrammetric Engineering and Remote Sensing, 54: 1727-1734.
- Franklin, S.E., R.J. Hall, L.M. Moskal, A.J. Maudie, and M.B. Lavignei, 2000. Incorporating texture into classification of forest species composition from airborne multispectral images, International Journal of Remote Sensing, 21(1): 61-79. https://doi.org/10.1080/014311600210993
- Green, A.A., M. Berman, P. Switzer, and M.D. Craig, 1988. 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. https://doi.org/10.1109/36.3001
- Han, D.Y., Y.W. Cho, Y.I. Kim, and Y.W. Lee, 2003. Feature selection for image classification of Hyperion data, Korean Journal of Remote Sensing, 19(2): 171-179 (in Korean with English abstract). https://doi.org/10.7780/kjrs.2003.19.2.170
- Kim, C., 2008. Use of crown feature analysis to separate the two pine species in QuickBird Imagery, Korean Journal of Remote Sensing, 24(3): 267-272. https://doi.org/10.7780/kjrs.2008.24.3.267
- Kim, S.H., K.S. Lee, J.R. Ma, and M.J. Kook, 2005. Current status of hyperspectral remote sensing: principle, data processing techniques, and applications, Korean Journal of Remote Sensing, 21(4): 341-369 (in Korean with English abstract). https://doi.org/10.7780/kjrs.2005.21.4.341
- Kruse, F.A., K.S. Keirein-Young, and J.W. Boardman, 1990. Mineral Mapping at Cuprite, Nevada with a 63-channel Imaging spectrometer, Photogrammetric Engineering and Remote Sensing, 56: 83-92.
- Leckie, D.G., F.A. Gougeon, S. Tinis, T. Nelson, C.N. Burnett, and D. Paradine, 2005. Automated tree recognition in old growth conifer stands with high resolution digital imagery, Remote Sensing of Environment, 94(3): 311-326. https://doi.org/10.1016/j.rse.2004.10.011
- Lee, K.S., W.B. Cohen, R.E. Kennedy, T.K. Maiersperger, and S.T. Gower, 2004. Hyperspectral versus multispectral data for estimating leaf area index in four different biomes, Remote Sensing of Environment, 91: 508-520. https://doi.org/10.1016/j.rse.2004.04.010
- Lucas, R., P. Bunting, M. Paterson, and L. Chisholm, 2008. Classification of Australian forest communities using aerial photography, CASI and HyMap data, Remote Sensing of Environment, 112(5): 2088-2103. https://doi.org/10.1016/j.rse.2007.10.011
- Martin, M.E., S.D. Newman, J.D. Aber, and R.G. Congalton, 1998. Determining Forest Species Composition Using High Spectral Resolution Remote Sensing Data, Remote Sensing of Enviornment, 65: 249-254. https://doi.org/10.1016/S0034-4257(98)00035-2
- Papes, M., R. Tupayachi, P. Martinez, A.T. Peterson, and G.V.N. Powell, 2010. Using hyperspectral satellite imagery for regional inventories: a test with tropical emergent trees in the Amazon Basin, Journal of Vegetation Science, 21(2): 342-354. https://doi.org/10.1111/j.1654-1103.2009.01147.x
- Park, N.W., H.Y. Yoo, Y.H. Kim, and S.Y. Hong, 2012. Classification of remote sensing data using random selection of training data and multiple classifiers, Korean Journal of Remote Sensing, 28(5): 489-499 (in Korean with English abstract). https://doi.org/10.7780/kjrs.2012.28.5.2
- Thenkabail, P.S., J.G. Lyon, and A. Huete, 2012. Hyperspectral Remote Sensing of Vegetation, CRC Press Taylor & Francis Group, Broken Sound Parkway, NW, USA.
- Thenkabail, P.S., E.A. Enclona, M.S. Ashton, and B. Van Der Meer, 2004. Accuracy assessments of hyperspectral waveband performance for vegetation analysis applications, Remote Sensing of Environment, 91(3-4): 354-376. https://doi.org/10.1016/j.rse.2004.03.013
- Wang, L., W.P. Sousa, P. Gong, and G.S. Biging, 2004. Comparison of IKONOS and QuickBird images for mapping mangrove species on the Caribbean coast of Panama, Remote Sensing of Environment, 91(3-4): 432-440. https://doi.org/10.1016/j.rse.2004.04.005
피인용 문헌
- Study of Comparison of Classification Accuracy of Airborne Hyperspectral Image Land Cover Classification though Resolution Change vol.22, pp.3, 2014, https://doi.org/10.7319/kogsis.2014.22.3.155
- Accuracy Assessment of Supervised Classification using Training Samples Acquired by a Field Spectroradiometer: A Case Study for Kumnam-myun, Sejong City vol.24, pp.1, 2016, https://doi.org/10.7319/kogsis.2016.24.1.121
- Classification of Forest Vertical Structure in South Korea from Aerial Orthophoto and Lidar Data Using an Artificial Neural Network vol.7, pp.10, 2017, https://doi.org/10.3390/app7101046
- Comparing the Potential of Multispectral and Hyperspectral Data for Monitoring Oil Spill Impact vol.18, pp.2, 2018, https://doi.org/10.3390/s18020558
- 초분광 영상의 Morphological Attribute Profiles와 추가 밴드를 이용한 감독분류의 정확도 평가 vol.25, pp.1, 2014, https://doi.org/10.7319/kogsis.2017.25.1.009
- 하천 부유쓰레기에 대한 분광라이브러리 특성 분석 vol.13, pp.3, 2014, https://doi.org/10.13067/jkiecs.2018.13.3.623
- UAV를 활용한 초분광 영상의 하천공간특성 분류 연구 vol.19, pp.10, 2014, https://doi.org/10.5762/kais.2018.19.10.633
- 수위변화에 따른 하상재료의 분광특성정보 분석 vol.6, pp.4, 2014, https://doi.org/10.17820/eri.2019.6.4.243
- Mapping Forest Vertical Structure in Gong-ju, Korea Using Sentinel-2 Satellite Images and Artificial Neural Networks vol.10, pp.5, 2020, https://doi.org/10.3390/app10051666
- Machine Learning for Tree Species Classification Using Sentinel-2 Spectral Information, Crown Texture, and Environmental Variables vol.12, pp.12, 2014, https://doi.org/10.3390/rs12122049
- 초분광 영상정보를 이용한 태화강 수계지역의 토지피복 변화분석 vol.24, pp.1, 2014, https://doi.org/10.11108/kagis.2021.24.1.012
- Characterizing and classifying urban tree species using bi-monthly terrestrial hyperspectral images in Hong Kong vol.177, pp.None, 2014, https://doi.org/10.1016/j.isprsjprs.2021.05.003