Hyperspectral satellite data (Hyperion) preprocessing—a case study on banded magnetite quartzite in Godumalai Hill, Salem, Tamil Nadu, India

Ganesh, B. Poovalinga; Aravindan, S.; Raja, S.; Thirunavukkarasu, Arunachalam

doi:10.1007/s12517-012-0584-8

Cited by 11 publications

(2 citation statements)

References 7 publications

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“…However, some bands produce a considerable amount of noise because of the malfunction in the Hyperion sensor. We manually select 154 bands (10 th -57 th , 80 th -120 th , 134 th -164 th , 187 th -220 th bands) with good quality to compose the test HS image [33,34]. The ALI data covers the wavelength range from 0.433 μm to 2.35 μm by 9 bands.…”

Section: Datasetsmentioning

confidence: 99%

A Novel Hyperspectral Image Simulation Method Based on Nonnegative Matrix Factorization

Huang

Chen

et al. 2019

Remote Sensing

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Hyperspectral (HS) images can provide abundant and fine spectral information on land surface. However, their applications may be limited by their narrow bandwidth and small coverage area. In this paper, we propose an HS image simulation method based on nonnegative matrix factorization (NMF), which aims at generating HS images using existing multispectral (MS) data. Our main novelty is proposing a spectral transformation matrix and new simulation method. First, we develop a spectral transformation matrix that transforms HS endmembers into MS endmembers. Second, we utilize an iteration scheme to optimize the HS and MS endmembers. The test MS image is then factorized by the MS endmembers to obtain the abundance matrix. The result image is constructed by multiplying the abundance matrix by the HS endmembers. Experiments prove that our method provides high spectral quality by combining prior spectral endmembers. The iteration schemes reduce the simulation error and improve the accuracy of the results. In comparative trials, the spectral angle, RMSE, and correlation coefficient of our method are 5.986, 284.6, and 0.905, respectively. Thus, our method outperforms other simulation methods.

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Section: Datasetsmentioning

confidence: 99%

A Novel Hyperspectral Image Simulation Method Based on Nonnegative Matrix Factorization

Huang

Chen

et al. 2019

Remote Sensing

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“…More recently, Pal et al [ 13 ] presented a new method based on local statistics to reduce striping in Hyperion images. To estimate surface reflectance, most AC methods used with Hyperion images have relied on processors using radiative transfer models (RTM), such as ATCOR [ 14 ], FLAASH [ 15 ], or other specific algorithms [ 16 , 17 , 18 , 19 , 20 , 21 , 22 ]. However, empirical or analytical methods can also be used: Prieto-Amparan et al [ 23 ] demonstrated that the empirical line [ 24 ] could be sufficient for biomass estimation of grasslands, and Katkovsky et al [ 25 ] proposed an algorithm based on the solutions of radiative transfer equations, and demonstrated that its results were in agreement with the FLAASH outputs when used over Hyperion images.…”

Section: Introductionmentioning

confidence: 99%

SUREHYP: An Open Source Python Package for Preprocessing Hyperion Radiance Data and Retrieving Surface Reflectance

Miraglio

Coops

2022

Sensors

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Surface reflectance is an essential product from remote sensing Earth observations critical for a wide variety of applications, including consistent land cover mapping and change, and estimation of vegetation attributes. From 2000 to 2017 the Earth Observing-1 Hyperion instrument acquired the first satellite based hyperspectral image archive from space resulting in over 83,138 publicly available images. Hyperion imagery however requires significant preprocessing to derive surface reflectance. SUREHYP is a Python package designed to process batches of Hyperion images, bringing together a number of published algorithms and methods to correct at sensor radiance and derive surface reflectance. In this paper, we present the SUREHYP workflow and demonstrate its application on Hyperion imagery. Results indicate SUREHYP produces flat terrain surface reflectance results comparable to commercially available software, with reflectance values for the whole spectral range almost entirely within 10% of the software’s over a reference target, yet it is publicly available and open source, allowing the exploitation of this valuable hyperspectral archive on a global scale.

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Identifying the spatial distribution of Prosopis Juliflora using Hyperion imagery

2021

Earth Sci Inform

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Hyperspectral satellite data (Hyperion) preprocessing—a case study on banded magnetite quartzite in Godumalai Hill, Salem, Tamil Nadu, India

Cited by 11 publications

References 7 publications

A Novel Hyperspectral Image Simulation Method Based on Nonnegative Matrix Factorization

A Novel Hyperspectral Image Simulation Method Based on Nonnegative Matrix Factorization

SUREHYP: An Open Source Python Package for Preprocessing Hyperion Radiance Data and Retrieving Surface Reflectance

Identifying the spatial distribution of Prosopis Juliflora using Hyperion imagery

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