HYPERION GÖRÜNTÜSÜ İLE ATMOSFERİK DÜZELTME YÖNTEMLERİNİN KARŞILAŞTIRILMASI: ORMAN ALANI ÖRNEĞİ

Year 2017, Volume: 22 Issue: 1, 103 - 114, 25.04.2017

Müfit Çetin Nebiye Musaoğlu Osman Hilmi Koçal

https://doi.org/10.17482/uumfd.308630

Cited By: 3

Abstract

Uzaktan algılama amaçlı algılayıcılar tarafından
elde edilen yansıma değerleri atmosferik etkilerden dolayı hatalar
içermektedir. Dolayısıyla, özellikle hiperspektral uydu görüntülerinin
işlenmesinde ve analizinde doğru sonuçlar elde edilmesi için atmosferik
düzeltme önemli bir işlemdir. Bu kapsamda, EO-1 Hyperion hiperspektral uydu
görüntüsü kullanılarak atmosferik ışınımsal transfer modelleri (FLAASH, ATCOR)  ve Doğrusal Ampirik (EL) yöntemi kullanılarak
performans sonuçları sunulmuştur. Elde edilen düzeltilmiş verilerin kalite
analizi ASD spektroradyometre aleti ile yapılan yer ölçmeleri kullanılarak
gerçekleştirilmiştir. Çalışmada elde edilen sonuçlara göre EL ve ATCOR
yöntemlerinin atmosferik etkinin giderilmesinde en iyi sonuçları verdiği, FLAASH
yönteminin ise düzeltilmiş reflektans eğrilerinde güçlü sapmalara neden olduğu
görülmüştür.

Keywords

Uydu görüntüsü, Hiperspektral, Hyperion, Atmosferik düzeltme

A Comparison of Atmospheric Correction Methods on Hyperion Imagery in Forest Areas

Abstract

The reflectance
values recorded by Earth observing satellite sensors can be different from the
surface reflectance values measured on the ground due to interference of gases
and water vapor in the atmosphere. Therefore, atmospheric correction is a
significant procedure to derive the true surface reflectance value during the
processing of remotely sensed imagery especially with hyperspectral data. In
this context, this study attempts to analyze the quality of the surface
reflectance derived from EO-1 Hyperion hyperspectral imagery using the
atmospheric radiative transfer (RT) models (FLAASH and ATCOR) and empirical
line (EL) method. In the study, ground-based reflectance measurements derived
from ASD FieldSpec spectroradiometer are used as reference to evaluate the
quality of the retrieved surface reflectance. The results showed that EL and
ATCOR methods achieved the best results for reducing some of the atmospheric
effects, but FLAASH method resulted in strong anomalies in the corrected
reflectance.

Keywords

Satellite Image, Hyperspectral, Hyperion, Atmospheric Correction

References

• Adler-Golden, S.M., Matthew, M.W., Bernstein, L.S., Levine, R.Y., Berk, A., Richtsmeier, S.C., Acharya, P.K., Anderson, G.P., Felde, G., Gardner, J., Hike, M., Jeong, L.S., Pukall, B., Mello, J., Ratkowski, A. and Burke, H.H. (1999) Atmospheric correction for shortwave spectral imagery based on MODTRAN4, SPIE Proc. Imaging Spectrometry, 61-69. doi:10.1117/12.366315
• Cetin, M. and Musaoglu, N. (2008). Spectral calibration and atmospheric correction of Hyperion images, 2th Remote Sensing and GIS Symposium, Kayseri, Turkey, October, Proc. no:60. (In Turkish)
• Cetin, M. and Musaoglu, N. (2009) Merging hyperspectral and panchromatic image data: qualitative and quantitative analysis, Int. J. Remote Sens., 30(7), 1779–1804. doi:10.1080/01431160802639525
• Christian, B. and Krishnayya, N. S. R. (2007) Spectral signatures of teak (Tectona grandis L.) using hyperspectral (EO-1) data, Current Science, 93(9), 1291-1296.
• Clark, R.N., Swayze, G.A., Livo, K.E., Kokaly, R.F., Sutley, S.J., Dalton, J.B., McDougal, R.R. and Gent, C.A. (2003) Imaging spectroscopy: Earth and planetary remote sensing with the USGS Tetracorder and expert systems, Journal of Geophysical Research, 108(E12), 5.1-5.44. doi:10.1029/2002JE001847
• Cocks, T., Jenssen, R., Stewart, A., Wilson, I. and Shields, T. (1998) The hymap airborne hyperspectral sensor: The system, calibration and performance, Proc. of the First EARSeL Workshop on Imaging Spectroscopy, Zurich, Switzerland, 37–42.
• Dwyer, J.L., Kruse, F.A. and Lefkoff, A.B. (1995) Effects of empirical versus model based reflectance calibration on automated analysis of imaging spectrometer data: A case study from the Drum Mountains, Utah, Photogrammetric Engineering and Remote Sensing, 61(10), 1247–1254.
• Felde, G.W., Anderson, G.P., Cooley, T.W., Matthew, M.W., Adler-Golden, S.M., Berk, A., and Lee, J. (2003) Analysis of Hyperion Data with the FLAASH Atmospheric Correction Algorithm, IEEE Geoscience and Remote Sensing Symposium, 2003, 1, 90–92. doi: 10.1109/IGARSS.2003.1293688
• Farrand, W.H., Singer, R.B. and Merenyi, E. (1994) Retrieval of apparent surface reflectance from AVIRIS data: A comparison of empirical line, radiative transfer, and spectral mixture methods, Remote Sensing of Environment, 47(3), 311–321. doi: 10.1016/0034-4257(94)90099-X
• Gao, B.C., Heidebrecht, K.B. and Goetz, A.F.H. (1993) Derivation of scaled surface reflectances from AVIRIS data, Remote Sensing of Environment, 44(2-3), 165-178. doi:10.1016/0034-4257(93)90014-O
• Goetz, A., Ferri, M., Kindel, B., and Qu, Z. (2002) Atmospheric Correction of Hyperion Data and Techniques for Dynamic Scene Correction, Proc. International Geoscience and Remote Sensing Symposium (IGARSS), 1408–1410. doi: 10.1109/IGARSS.2002.1026132
• Goetz, A.F.H., Kindel, B.C., Ferri M. and Qu, Z. (2003) HATCH: Results from simulated radiances, AVIRIS and HYPERION, IEEE Transactions on Geoscience and Remote Sensing, 41(6/1), 1215–1221. doi: 10.1109/TGRS.2003.812905
• Goodenough, D.G., Dyk, A., Niemann, O., Pearlman, J.S., Chen, H., Han, T., Murdoch, M., and West, C. (2003) Processing HYPERION and ALI for Forest Classification, IEEE Trans. Geosci. Remote Sensing, 41(6/1), 1321-1331. doi: 10.1109/TGRS.2003.813214
• Guanter, L., Richter, R. and Moreno, J. (2006) Spectral calibration of hyperspectral imagery using atmospheric absorption features, Applied Optics, 45(10), 2360–2370. doi: 10.1364/AO.45.002360
• Guanter, L., Estellés, V. and Moreno, J. (2007a) Spectral calibration and atmospheric correction of ultra-fine spectral and spatial resolution remote sensing data. Application to CASI-1500 data, Remote Sensing of Environment, 109(1), 54–65. doi:10.1016/j.rse.2006.12.005
• Guanter, L., Gonzalez-Sanpedro, M., Del, C. and Moreno, J. (2007b) A method for the atmospheric correction of ENVISAT/MERIS data over land targets, International Journal of Remote Sensing, 28(3-4), 709–728. doi: 10.1080/01431160600815525
• Hardin, P. and Hardin, A. (2013) Hyperspectral remote sensing of urban areas, Geography Compass, 7(1), 7-21. doi: 10.1111/gec3.12017
• Karpouzli, E. and Malthus, T. (2003) The empirical line method for the atmospheric correction of IKONOS imagery, International Journal of Remote Sensing, 24(5), 1143–1150.doi: 10.1080/0143116021000026779
• Kruse, F.A., Boardman, J.W. and Huntigton, J.F. (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: 10.1109/TGRS.2003.812908
• Lee, M., Huang, Y., Yao, H., Thomson, S.J. and Bruce, L. (2014) Determining the Effects of Storage on Cotton and Soybean Leaf Samples for Hyperspectral Analysis. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 7(6). doi:10.1109/JSTARS.2014.2330521
• Lu, D., Mausel, P., Brondízio, E., and Moran, E. (2002) Assessment of atmospheric correction methods for Landsat TM data applicable to Amazon basin LBA research, International Journal of Remote Sensing, 23(13), 2651–2671. doi: 10.1080/01431160110109642
• Mahiny, A.S. and Turner, B.J. (2007) A Comparison of Four Common Atmospheric Correction Methods, Photogrammetric Engineering & Remote Sensing, 73(4), 361-368. doi: 10.14358/PERS.73.4.361
• Matthew, M.W., Adler-Golden, S.M., Berk, A., Richtsmeier, S.C., Levine, R.Y., Bernstein, L.S., Acharya, P.K., Anderson, G.P., Felde, G.W., Hoke, M.P., Ratkowski, A., Burke, H.-H., Kaiser, R.D. and Miller, D.P. (2000) Status of Atmospheric Correction Using a MODTRAN4-based Algorithm, Proc. SPIE Algorithms for Multispectral, Hyperspectral and Ultraspectral Imagery VI, 199-207. doi:10.1117/12.410341
• Miller, C.J. (2002) Performance assessment of ACORN atmospheric correction algorithm, Proc. SPIE Algorithms and Technologies for Multispectral, Hyperspectral and Ultraspectral Imagery VIII, 438–449. doi: 10.1117/12.478777
• Norjamäki, I. and Tokola, T. (2007) Comparison of Atmospheric Correction Methods in Mapping Timber Volume with Multitemporal Landsat Images in Kainuu, Finland, Photogrammetric Engineering and Remote Sensing, 73(2), 155-164. doi: 10.14358/PERS.73.2.155
• Pearlman, J.S., Barry, P.S., Segal, C.C., Shepanski, J., Beiso, D. and Carman, S.L. (2003) Hyperion, a Space-Based Imaging Spectrometer, IEEE Transactions on Geoscience and Remote Sensing, 41(6), 1160-1173. doi: 10.1109/TGRS.2003.815018
• Perry, E.M., Warner, T. and Foote, P. (2000) Comparison of atmospheric modeling versus empirical line fitting for mosaicking HYDICE imagery, International Journal of Remote Sensing, 21(4), 799–803.doi:10.1080/014311600210588
• Qu, Z., Kindel, B.C. and Goetz, A.F.H. (2003) The high accuracy atmospheric correction for hyperspectral data (HATCH) model, IEEE Transactions on Geoscience and Remote Sensing, 41(6/1), 1223–1231. doi:10.1109/TGRS.2003.813125
• Richter, R. (1996) Atmospheric correction of satellite data with haze removal including a haze/clear transition region, Computers and Geosciences, 22(6), 675–681. doi:10.1016/0098-3004(96)00010-6
• San, B.T. and Suzen, M.L. (2010) Evaluation of different atmospheric correction algorithms for EO-1 Hyperion Imagery, ISPRS Technical Commision VII Symposium, Kyoto, JAPONYA, 38(8), 392-397.
• Schmid, T., Rodriguez-Rastrero, M., Escribano, P., Palacios-Orueta, A., Ben-Dor, E., Plaza, A., Milewski, R., Huesca, M., Bracken, A., Cicuendez, V., Pelayo, M., Chabrillat, S. (2016) Characterization of soil erosion indicators using hyperspectral data from a Mediterranean rainfed cultivated region, IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens., 9(2), 845-860. doi: 10.1109/JSTARS.2015.2462125
• Shang, S. and Chisholm, L.A. (2014) Classification of Australian native forest species using hyperspectral remote sensing and machine-learning classification algorithms, IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens., 7(6), 2481-2489. doi: 10.1109/JSTARS.2013.2282166
• Smith, G.M. and Milton, E.J. (1999) The use of the empirical line method to calibrate remotely sensed data to reflectance, International Journal of Remote Sensing, 20(13), 2653–2662. doi: 10.1080/014311699211994
• Veraguth, S., Keller, J., Schaepman, M. and Itten, K.I. (1995) Atmospheric correction of AVIRIS imagery in Central Switzerland. Sensitivity analysis regarding correction methods, radiation transfer models and atmospheric profiles, Proc. International Geoscience and Remote Sensing Symposium (IGARSS), 2069-2071. doi:10.1109/IGARSS.1995.524110
• Wald, L. (2000) Quality of high resolution synthesised images: Is there a simple criterion?. Proceedings of the third conference "Fusion of Earth data: merging point measurements, raster maps and remotely sensed images", Sophia Antipolis, France, 99-103.
• Wang, Z., Bovik, A.C., Sheikh, H.R. and Simoncelli, E.P. (2004) Image quality assessment: From error visibility to structural similarity, IEEE Trans. Image Process., 13(4), 600-612. doi: 10.1109/TIP.2003.819861
• Wang, Z., Ziou, D., Armenakis, C., Li, D. and Li, Q. (2005) A comparative analysis of image fusion methods, IEEE Trans. Geosci. Remote Sens., 43(6), 1391-1402. doi: 10.1109/TGRS.2005.846874
• Xu, J.F. and Huang, J.F. (2008) Empirical Line Method Using Spectrally Stable Targets to Calibrate IKONOS Imagery, Pedosphere, 18(1), 124-130. doi:10.1016/S1002-0160(07)60110-6