XXII ISPRS Congress 2012: Technical Commission VII

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Multivolume work

Persistent identifier:: 1663813779

Title:: XXII ISPRS Congress 2012

Sub title:: Melbourne, Australia, 25 August-1 September 2012

Year of publication:: 2013

Place of publication:: Red Hook, NY

Publisher of the original:: Curran Associates, Inc.

Identifier (digital):: 1663813779

Language:: English

Additional Notes:: Kongress-Thema: Imaging a sustainable future

Corporations:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Adapter:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Founder of work:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Other corporate:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Document type:: Multivolume work

Volume

Persistent identifier:: 1663821976

Title:: Technical Commission VII

Scope:: 546 Seiten

Year of publication:: 2013

Place of publication:: Red Hook, NY

Publisher of the original:: Curran Associates, Inc.

Identifier (digital):: 1663821976

Illustration:: Illustrationen, Diagramme

Signature of the source:: ZS 312(39,B7)

Language:: English

Additional Notes:: Erscheinungsdatum des Originals ist ermittelt.; Literaturangaben

Usage licence:: Attribution 4.0 International (CC BY 4.0)

Corporations:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Adapter:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Founder of work:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Other corporate:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Publisher of the digital copy:: Technische Informationsbibliothek Hannover

Place of publication of the digital copy:: Hannover

Year of publication of the original:: 2019

Document type:: Volume

Collection:: Earth sciences

Chapter

Title:: [VII/1: PHYSICAL MODELLING AND SIGNATURES IN REMOTE SENSING]

Document type:: Multivolume work

Structure type:: Chapter

Chapter

Title:: ATMOSPHERIC CORRECTION COMPARISON OF SPOT-5 IMAGE BASED ON MODEL FLAASH AND MODEL QUAC Yunkai GUO, Fan ZENG

Document type:: Multivolume work

Structure type:: Chapter

ite ) al fic ed an on International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XXXIX-B7, 2012 XXII ISPRS Congress, 25 August — 01 September 2012, Melbourne, Australia first-principle method. Its model principle process is shown in Figure l(c: ITT Visual Information Solutions (ITT VIS), *ENVI User's Guide, Version 4.8"). HIS or —> Bright MSI data spectral filter Y : Y Determina tion of Average of baseline end member (Dark channel/ Y offset) Determin Average Y ation of |g of gain reference Baseline end deduction member Y A ; Recovery of reflection Vegetation data by using gain and filter offset Determination of end member Figure 1. Flow chart of QUAC model QUAC also needs the elevation angle of the sun and center wavelength. If the sensor has no correct radiation or wavelength calibration, or the sun light intensity (when there is cloud deck) is unknown, correction can still be made with this method within the allowed accuracy scope. 3.3 Analysis of effect of atmospheric correction In order to evaluate and verify the effects of atmospheric correction which Model FLAASH and Model QUAC have on SPOT-5 remote sensing image, a contrastive analysis shall be made respectively for the images of both models after and before the correction in the aspect of sight and spectral curve of surface features reflectance. The images (geometric correction has been finished) before and after the atmospheric correction need geographical link first to ensure that the images of various scenes correspond to the same pixel in the same area when making the contrastive analysis. 3.3.1 Analysis of visual contrast before and after correction: In Figure 2, a, b, c is respectively the image in the same region after twice magnification before the atmospheric correction, after the FLAASH atmospheric correction and the QUAC atmospheric correction, and band RGB combination is 4, 3, 2. We can see that there are obvious changes in visual effects of the image before and after the correction, darker for a on the whole, because the presence of the atmosphere will reduce the difference between light and dark of surface features to reduce contrast ratio of the images; visual effect of image after the correction has been improved significantly, brighter and clearer, contrast ratio also increased and image quality improved, indicating that atmospheric correction has effectively eliminated the effect of atmospheric aerosols, water vapor and other atmospheric factors. In contrast, image quality of b (through FLAASH atmospheric correction) is slightly better than c (through QUAC atmospheric correction) due to more abundant information. a: b e Figure 2. Visual analysis of atmospheric correction 3.3.2 Contrastive analysis of reflectance spectral curve: Characteristics of reflectance spectrum curve are an important means for the recognition of remote sensing image surface features. From the visual angle, only a rough evaluation can be made on the effects of the two atmospheric correction models, and making a contrast analysis of reflectance spectral curve of its corresponding typical surface features can reflect the effect of atmospheric correction better. The study area of this trial is typically hilly area in southern China; data acquisition time is in November; the image presented mainly small ponds, the harvested farmlands and several scattered hills; and most of the surface is soil. Therefore, soil, vegetation, water body (pond water) and asphalt road are selected respectively as the surface type of pixel. As shown in Figure 3, a, b, c, d are respectively the figures of soil, vegetation, water bodies and asphalt road, four typical surface features, after FLAASH atmospheric correction, after QUAC atmospheric correction and of actual measurement reflectance curve. 0.32 T —+— QUAC : 03¢ FLA ASH ES ey t tt n - Measured : 0.281 r$ Reflectance SE nase 5 Bo ee ; ae ; RANE ei ] $ 024| AL AE AE p^ us En Bees Gt À en ES : mae Lo] 05 1 15 2 Wavelength/um a. Soil

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