Journals / Uludağ Üniversitesi Mühendislik Fakültesi Dergisi / 2017 / Cilt: 22 - Sayı: 1

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

HYPERION GÖRÜNTÜSÜ İLE ATMOSFERİK DÜZELTME YÖNTEMLERİNİN KARŞILAŞTIRILMASI: ORMAN ALANI ÖRNEĞİ

Pages
103–114
Publication Date
2017-04-25
DOI
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

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.

📌 3 Key Takeaways

🎯
Objective & Hypothesis: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.
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Key Findings & Data: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.
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Conclusion & Impact: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.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Uydu görüntüsü · Hiperspektral · Hyperion

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.

Özet

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.

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Cite This Article

ÇETİN, M., MUSAOĞLU, N., KOÇAL, O. H. (2017). A Comparison of Atmospheric Correction Methods on Hyperion Imagery in Forest Areas. Uludağ Üniversitesi Mühendislik Fakültesi Dergisi, Cilt: 22 - Sayı: 1, 103–114. https://ajindex.com/articles/708787

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References (4)

  1. 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.
  2. 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.
  3. 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.
  4. 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,