Analysis of Hyperion data with the FLAASH atmospheric correction algorithm

Felde, Gerald W.; Anderson, Gail P.; Cooley, T.; Matthew, Michael W.; Adler‐Golden, Steven M.; Berk, Alexander; Lee, J.

doi:10.1109/igarss.2003.1293688

Cited by 95 publications

(65 citation statements)

References 3 publications

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“…The product Level 1T was used, which refers to orthorectified data. OLI data is converted into radiance and subsequently into atmospherically corrected surface reflectance using the fast line-of-sight atmospheric analysis of spectral hypercubes (FLAASH), a MODTRAN4-based algorithm (Felde et al 2003).…”

Section: Study Area and Data Setsmentioning

confidence: 99%

Semiautomatic approach for land cover classification: a remote sensing study for arid climate in southeastern Tunisia

Bouaziz

Eisold

Guermazi

2017

Euro-Mediterr J Environ Integr

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The land use and land cover (LULC) classification has great potential to contribute to the monitoring of land degradation and climatic disasters. The purpose of this study was to assess the performance of parametric and nonparametric classification methods using remotely sensed Landsat satellite data of arid and semiarid areas, based on the computed producer's accuracy, user's accuracy, overall accuracy, and Cohen's kappa coefficient. Three LULC classes were identified, and supervised classifications were applied to Landsat 8 imagery. The results show that the support vector machines (SVM) classification method produced more accurate results, using two different kernel functions, compared with the maximum likelihood classification (MLC) and the minimum distance classification (MDC). The basis radial function affords the highest overall classification accuracy of 91.20% and a mean kappa coefficient of 0.87. This classification method is very well suited to accurately map LULC in arid and semiarid regions where the main vegetation type is oasis or steppes.

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Section: Study Area and Data Setsmentioning

confidence: 99%

Semiautomatic approach for land cover classification: a remote sensing study for arid climate in southeastern Tunisia

Bouaziz

Eisold

Guermazi

2017

Euro-Mediterr J Environ Integr

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“…It is important to note that FLAASH has an automated wavelength recalibration algorithm, which can also be used to improve the retrieved reflectance specfra. This algorithm uses atmospheric molecular absorption features (e.g., oxygen, carbon dioxide, water vapor) to quantify wavelength error by fitting an absorption band shape in the measured radiance spectra to MODTRAN model calculations (Felde et al, 2003). If the wavelength recalibration option is selected, it is implemented in the FLAASH processing flow immediately after the preliminary water vapor retrieval.…”

Section: Descmption Of Elaashmentioning

confidence: 99%

Water Vapor Retrieval Using the FLAASH Atmospheric Correction Algorithm

Felde¹,

Anderson²,

Gardner³

et al. 2004

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Public reporting burden for tliis collection of infomiation is estimated to average 1 hour per response, including the time for reviewing instrucb'ons, searching existing data sources, gathering and maintaining the data needed, and compleb'ng and reviewing this collection of infomiation. Send comments regarding this burden estimate or any other aspect of this collection of infomiation, including suggestions for reducing this burden to Department of Defense, Washington Headquarters Services, Directorate for Information SPONSORING / MONITORING AGENCY NAME(S) AND ADDRESS(ES) 10. SPONSOR/MONITOR'S ACRONYM(S)AFRL/VSBYH SPONSOIVMONITOR'S REPORT NUMBER(S)AFRL - 14. ABSTRACT FLAASH (Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubes) is a first-principles atmospheric correction algorithm for visible to shortwave infrared (SWIR) hyperspectral data. The first is retrieval of atmospheric parameters, visibility (which is related to the aerosol type and distribution) and column water vapor. The second step is solving the radiation transport ecjuation for the given aerosol and column water and transformation to surface reflectance. The focus of this paper is on the FLAASH water vapor retrieval algorithm. Modeled radiance values in the spectral region of one water vapor absorption feature are calculated from MODTRAN 4 using several different water vapor amounts and are using to generate a Look-up Table (LUT) . The water band typically used is 1030 nm but either the 940 and 820 nm band may also be used. Measured radiation values are compared to the LUT to determine the column water vapor amount for each pixel in the scene. We conpare the results of water retrievals for each of these bands and also the results of their corresponding reflectance retrievals. ABSTRACT FLAASH (Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubes) is a first-principles atmospheric correction algorithm for visible to shortwave infrared (SWIR) hyperspectral data. The algorithm consists of two main steps. The first is retrieval of atmospheric parameters, visibility (which is related to the aerosol ^e and distribution) and column water vapor. The second step is solving the radiation transport equation for the given aerosol and column water and transformation to surface reflectMice. The focus of this paper is Oh the FLAASH wa;ter vapor retrieval algorithm. Modeled radiance values in the spectral region of one water vapor absorption feature are calculated from MODTRAN 4 usmg several different water vapor amounts and are used to generate a Look-Up Table (LUT). The water band typically used is 1130 nm but either the 940 or 820 niri band may also be used. Measured radiance values are compared to the LUT to determine the column water vapor amount for each pixel in the scene. We compare the results of water retrievals for each of these bands and also the rer.ults of their corresponding reflectance retrievals.

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“…In this study, the DN of VNIR and SWIR imagery were converted to TOA reflectance by using the fast line-of-sight atmospheric analysis of spectral hypercubes (FLAASH) model [54] which has been incorporated into the environment for visualizing images (ENVI) software. This allows for the DN of VNIR and SWIR images to be converted to TOA reflectance and that of TIR to be converted to BT.…”

Section: Preprocessingmentioning

confidence: 99%

Disaggregation of Landsat-8 Thermal Data Using Guided SWIR Imagery on the Scene of a Wildfire

Cho

Kim

2018

Remote Sensing

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Thermal data products derived from remotely sensed data play significant roles as key parameters for biophysical phenomena. However, a trade-off between spatial and spectral resolutions has existed in thermal infrared (TIR) remote sensing systems, with the end product being the limited resolution of the TIR sensor. In order to treat this problem, various disaggregation methods of TIR data, based on the indices from visible and near-infrared (VNIR), have been developed to sharpen the coarser spatial resolution of TIR data. Although these methods were reported to exhibit sufficient performance in each study, preservation of thermal variation in the original TIR data is still difficult, especially in fire areas due to the distortion of the VNIR reflectance by the impact of smoke. To solve this issue, this study proposes an efficient and improved disaggregation algorithm of TIR imagery on wildfire areas using guided shortwave infrared (SWIR) band imagery via a guided image filter (GF). Radiometric characteristics of SWIR wavelengths could preserve spatially high frequency temperature components in flaming combustion, and the GF preserved thermal variation of the original TIR data in the disaggregated result. The proposed algorithm was evaluated using Landsat-8 operational land imager (OLI) and thermal infrared sensor (TIRS) images on wildfire areas, and compared with other algorithms based on a vegetation index (VI) originating from VNIR. In quantitative analysis, the proposed disaggregation method yielded the best values of root mean square error (RMSE), mean absolute error (MAE), correlation coefficient (CC), erreur relative globale adimensionelle de synthèse (ERGAS), and universal image quality index (UIQI). Furthermore, unlike in other methods, the disaggregated temperature map in the proposed method reflected the thermal variation of wildfire in visual analysis. The experimental results showed that the proposed algorithm was successfully applied to the TIR data, especially to wildfire areas in terms of quantitative and visual assessments.

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Analysis of Hyperion data with the FLAASH atmospheric correction algorithm

Cited by 95 publications

References 3 publications

Semiautomatic approach for land cover classification: a remote sensing study for arid climate in southeastern Tunisia

Semiautomatic approach for land cover classification: a remote sensing study for arid climate in southeastern Tunisia

Water Vapor Retrieval Using the FLAASH Atmospheric Correction Algorithm

Disaggregation of Landsat-8 Thermal Data Using Guided SWIR Imagery on the Scene of a Wildfire

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