Article

Chavez PS. An improved dark-object subtraction technique for atmospheric scattering correction of multispectral data. Remote Sensing of Environment

U.S. Geological Survey, 2255 N. Gemini Drive, Flagstaff, Arizona 86001 USA
Remote Sensing of Environment (Impact Factor: 6.39). 04/1988; 24(3):459-479. DOI: 10.1016/0034-4257(88)90019-3

ABSTRACT

Digital analysis of remotely sensed data has become an important component of many earth-science studies. These data are often processed through a set of preprocessing or “clean-up” routines that includes a correction for atmospheric scattering, often called haze. Various methods to correct or remove the additive haze component have been developed, including the widely used dark-object subtraction technique. A problem with most of these methods is that the haze values for each spectral band are selected independently. This can create problems because atmospheric scattering is highly wavelength-dependent in the visible part of the electromagnetic spectrum and the scattering values are correlated with each other. Therefore, multispectral data such as from the Landsat Thematic Mapper and Multispectral Scanner must be corrected with haze values that are spectral band dependent. An improved dark-object subtraction technique is demonstrated that allows the user to select a relative atmospheric scattering model to predict the haze values for all the spectral bands from a selected starting band haze value. The improved method normalizes the predicted haze values for the different gain and offset parameters used by the imaging system. Examples of haze value differences between the old and improved methods for Thematic Mapper Bands 1, 2, 3, 4, 5, and 7 are 40.0, 13.0, 12.0, 8.0, 5.0, and 2.0 vs. 40.0, 13.2, 8.9, 4.9, 16.7, and 3.3, respectively, using a relative scattering model of a clear atmosphere. In one Landsat multispectral scanner image the haze value differences for Bands 4, 5, 6, and 7 were 30.0, 50.0, 50.0, and 40.0 for the old method vs. 30.0, 34.4, 43.6, and 6.4 for the new method using a relative scattering model of a hazy atmosphere.

    • "Radiometric corrections consisted in changing 8-bit digital values into radiance and reflectance values (Markham and Barker 1986). Atmospheric correction was done based on Chavez's improved dark object image subtraction approach (Chavez 1988) due to lack of historical data on the atmosphere. Geometric corrections were applied using 20 control points clearly identified in the field, determined by GPS and identifiable on the satellite image. "
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    ABSTRACT: The ability of synthetic aperture radar (SAR) C-band microwave energy to penetrate within forest vegetation makes it possible to extract information on crown components, which in turn gives a better approximation of relative canopy density than optical data-derived canopy density. Many studies have been reported to estimate forest biomass from SAR data, but the scope of C-band SAR in characterizing forest canopy density has not been adequately understood with polarimetric techniques. Polarimetric classification is one of the most significant applications of polarimetric SAR in remote sensing. The objective of the present study was to evaluate the feasibility of different polarimetric SAR data decomposition methods in forest canopy density classification using C-band SAR data. Landsat (Land Satellite) 5 TM (Thematic Mapper) data of the same area has been used as optical data to compare the classification result. RADARSAT (Radar Satellite)-2 image with fine quad-pol obtained on 27 October 2011 over tropical dry forests of Madhav National Park, India, was used for the analysis of full polarimetric data. Six decomposition methods were selected based on incoherent decomposition for generating input images for classification, i.e. Huynen, Freeman and Durden, Yamaguchi, Cloude, Van zyl, and H/A/α. The performance of each decomposition output in relation to each land cover unit present in the study area was assessed using a support vector machine (SVM) classifier. Results show that Yamaguchi 4-component decomposition (overall accuracy 87.66% and kappa coefficient (κ) 0.86) gives better classification results, followed by Van Zyl decomposition (overall accuracy 87.20% and κ 0.85) and Freeman and Durden (overall accuracy 86.79% and κ 0.85) in forest canopy density classification. Both model-based decompositions (Freeman and Durden and Yamaguchi4) registered good classification accuracy. In eigenvector or eigenvalue decompositions, Van zyl registered the second highest accuracy among different decompositions. The experimental results obtained with polarimetric C-band SAR data over a tropical dry deciduous forest area imply that SAR data have significant potential for estimating canopy density in operational forestry. A better forest density classification result can be achieved within the forest mask (without other land cover classes). The limitations associated with optical data such as non-availability of cloud-free data and misclassification because of gregarious occurrence of bushy vegetation such as Lantana can be overcome by using C-band SAR data.
    No preview · Article · Jan 2016 · International Journal of Remote Sensing
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    • "Preprocessing of the image enhances the quality of the data and removes inherent noise that can have negative impacts on the classification and the scene-to-scene comparisons over time, such as change detection[55,56]. We normalized the image by converting the measured digital number (DN) values to top of atmosphere (TOA) reflectance. "
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    ABSTRACT: We tested the Moment Distance Index (MDI) in combination with texture features for the summer vegetation mapping in the eastern Pamir Mountains, Tajikistan using the 2014 Landsat OLI (Operational Land Imager) image. The five major classes identified were sparse vegetation, medium-dense vegetation, dense vegetation, barren land, and water bodies. By utilizing object features in a random forest (RF) classifier, the overall classification accuracy of the land cover maps were 92% using a set of variables including texture features and MDI, and 84% using a set of variables including texture but without MDI. A decrease of the Kappa statistics, from 0.89 to 0.79, was observed when MDI was removed from the set of predictor variables. McNemar’s test showed that the increase in the classification accuracy due to the addition of MDI was statistically significant (p < 0.05). The proposed method provides an effective way of discriminating sparse vegetation from barren land in an arid environment, such as the Pamir Mountains.
    Full-text · Article · Jan 2016 · Remote Sensing
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    • "Scene acquisition dates were 25 August 2013 and 28 August 2014 (earthexplorer.usgs.gov/). Atmospheric correction was performed using the dark object subtraction method (Chavez Jr 1988). NDVI values were calculated for each date using SPRING 5.2.7 (Câmara et al. 1996). "
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    ABSTRACT: Reports of triatomine infestation in urban areas have increased. We analysed the spatial distribution of infestation by triatomines in the urban area of Diamantina, in the state of Minas Gerais, Brazil. Triatomines were obtained by community-based entomological surveillance. Spatial patterns of infestation were analysed by Ripley’s K function and Kernel density estimator. Normalised difference vegetation index (NDVI) and land cover derived from satellite imagery were compared between infested and uninfested areas. A total of 140 adults of four species were captured (100 Triatoma vitticeps, 25 Panstrongylus geniculatus, 8 Panstrongylus megistus, and 7 Triatoma arthurneivai specimens). In total, 87.9% were captured within domiciles. Infection by trypanosomes was observed in 19.6% of 107 examined insects. The spatial distributions of T. vitticeps, P. geniculatus, T. arthurneivai, and trypanosome-positive triatomines were clustered, occurring mainly in peripheral areas. NDVI values were statistically higher in areas infested by T. vitticeps and P. geniculatus. Buildings infested by these species were located closer to open fields, whereas infestations of P. megistus and T. arthurneivai were closer to bare soil. Human occupation and modification of natural areas may be involved in triatomine invasion, exposing the population to these vectors.
    Full-text · Article · Jan 2016 · Memórias do Instituto Oswaldo Cruz
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Questions & Answers about this publication

  • Pat S. Chavez, Jr. added an answer in ENVI:
    Which is best: Landsat 7 and 8 Atmospheric Correction DOS (Dark Object Subtraction) Vs. FLAASH in ENVI?

    Dear All, 

    I am calculating NDVI from Landsat 7 and 8. Before calculating the NDVI using selected band i need to correct the error occur in reflectance value due to atmospheric gas, particle etc. I have used the Dark Object Subtraction method which is inbuilt function in ENVI. But i unable to detect either the Atmospheric error has been remove or not.

    ENVI provides one more method called FLAASH (First Line of Sight) Atmospheric correction which is based on MODTRAN.

    So my question is which method would be the  best for me. My region of interest is near to Coast region. Please share your opinion with me. I will be vary glad.

    Thank You

    Shouvik Jha   

    Pat S. Chavez, Jr.

    I am the person who developed the 'Improved DOS' and 'COST' atmospheric corrections models (the COST model is in the paper titled 'Image Based Atmospheric Corrections --- revisited and improved' published in 1996). Keep in mind that either the DOS or COST corrections can be done with a simple stretch function which all software package contain somewhere within their tool boxes of processing procedures.
    A very critical aspect of image based correction models, such as these two, is that a 'VALID DARK OBJECT' that can be used to compute the haze values exist within the image. At times the minimum DN value of the image is used blindly as the dark object for atmospheric haze corrections which can lead to over correcting for haze and the surface reflectance values will be under estimated. Perhaps the links below might be useful to you:

    Link to slides about Radiometric Calibration and Atmospheric corrections using DOS / Improved DOS / COST models:
    https://www.researchgate.net/publication/275042873_Presentation_-_Radiometric_Calibration_and_Atmospheric_Corrections
    Link to COST paper:
    https://www.researchgate.net/publication/236769129_Image-Based_Atmospheric_Corrections_-_Revisited_and_Improved
    Link to the Improved DOS paper:
    https://www.researchgate.net/publication/223795843_Chavez_PS._An_improved_dark-object_subtraction_technique_for_atmospheric_scattering_correction_of_multispectral_data._Remote_Sensing_of_Environment

    + 2 more attachments

  • Pat S. Chavez, Jr. added an answer in Atmospheric Correction:
    How do I perform 6S radiative transfer model in ENVI for Radiometric and Atmospheric correction of Landsat8?

    Dear All, 

    I am working on atmospheric correction of Landsat 8 image using 6S radiative transfer model using ENVI. I am getting problem to perform 6S radiative transfer model to correct the image. Plz suggest me how to do this task using ENVI or any software. 

    Thank You 

    Regards 

    Shouvik Jha 

    Pat S. Chavez, Jr.

    Shouvik,

    The 2 papers and 1 set of slides linked to below deal with atmospheric corrections but are not ENVI related, however, I thought you you might be interested in seeing them.

    Image-Based Atmospheric Corrections - Revisited and Improved

    https://www.researchgate.net/publication/236769129_Image-Based_Atmospheric_Corrections_-_Revisited_and_Improved

    An improved dark-object subtraction technique for atmospheric scattering correction of multispectral data

    https://www.researchgate.net/publication/223795843_Chavez_PS._An_improved_dark-object_subtraction_technique_for_atmospheric_scattering_correction_of_multispectral_data._Remote_Sensing_of_Environment

    Presentation - Radiometric Calibration and Atmospheric Corrections

    https://www.researchgate.net/publication/275042873_Presentation_-_Radiometric_Calibration_and_Atmospheric_Corrections?ev=prf_pub

    Pat

    + 2 more attachments

  • Pat S. Chavez, Jr. added an answer in Landsat:
    Which is the best software to transform ND data to reflectance values in Landsat 8?

    Dear colleagues, i have tried to transform it using the equation provided by the "Using the USGS Landsat 8 Product" web page: (http://landsat.usgs.gov/Landsat8_Using_Product.php), but this formula

    (ρλ = ρλ' / cos(θSZ)  does not consider the distance from the Earth to the Sun. 

    Any suggestion for a more exact method?

    Thank you, /.GZ

    Pat S. Chavez, Jr.

    Just want to add to the already good suggestions that have been made.  The type and level of radiometric calibration and atmospheric corrections that are needed is dependent on the application that is of interest.  Some applications do not require such corrections while for some applications it is critical.  Of course there are various options and models that have been developed over the last 4 decades and which one to use depends on the available data (or lack of data, such as field and atmospheric measurements during the satellite over flight).  I have developed some image based correction methods (as have others) and you might be interested in taking a look at those approaches.  Below are links to a set of slides and 2 papers within research gate that might be of interest to you.  Keep in mind that TOA radiance and reflectance conversions typically still have atmospheric, and in some cases sun elevation angle, components left in the data and do not represent surface values.

    Link to slides about Radiometric Calibration and Atmospheric corrections using DOS / Improved DOS / COST models:

    https://www.researchgate.net/publication/275042873_Presentation_-_Radiometric_Calibration_and_Atmospheric_Corrections

    Link to COST paper:

    https://www.researchgate.net/publication/236769129_Image-Based_Atmospheric_Corrections_-_Revisited_and_Improved

    Link to the Improved DOS paper:

    https://www.researchgate.net/publication/223795843_Chavez_PS._An_improved_dark-object_subtraction_technique_for_atmospheric_scattering_correction_of_multispectral_data._Remote_Sensing_of_Environment

     Good luck with your work.

    Pat

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  • Pat S. Chavez, Jr. added an answer in Remote Sensing Applications:
    How does sun irradiance affect satellite imagery, and how does it vary among different satellites?

    Why do we need to account for top-atmosphere reflectance?

    This is in relation to the methods by Stapleton, et. al (2014) from Polar Bears from Space: Assessing Satellite Imagery as a Tool to Track Artic Wildlife.

    Pat S. Chavez, Jr.

    Rochelle,

    Depending on the application radiometric calibration (including corrections for Earth-Sun distance and atmospheric conditions, plus sun elevation angle) may or may not be needed.  For most, not all, applications that use multi-temporal or multi-sensor data sets radiometric calibration are needed and/or help with the analysis.

    Of course, there are various methods that have been developed over the last few decades for radiometric calibration and the links below are to 2 papers that discuss  the DOS and COST methods that I developed.  You may want to take a look at them and see if perhaps they may apply for your data sets and applications.

    Good Luck,

    Pat

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      [Show abstract] [Hide abstract]
      ABSTRACT: Digital analysis of remotely sensed data has become an important component of many earth-science studies. These data are often processed through a set of preprocessing or “clean-up” routines that includes a correction for atmospheric scattering, often called haze. Various methods to correct or remove the additive haze component have been developed, including the widely used dark-object subtraction technique. A problem with most of these methods is that the haze values for each spectral band are selected independently. This can create problems because atmospheric scattering is highly wavelength-dependent in the visible part of the electromagnetic spectrum and the scattering values are correlated with each other. Therefore, multispectral data such as from the Landsat Thematic Mapper and Multispectral Scanner must be corrected with haze values that are spectral band dependent. An improved dark-object subtraction technique is demonstrated that allows the user to select a relative atmospheric scattering model to predict the haze values for all the spectral bands from a selected starting band haze value. The improved method normalizes the predicted haze values for the different gain and offset parameters used by the imaging system. Examples of haze value differences between the old and improved methods for Thematic Mapper Bands 1, 2, 3, 4, 5, and 7 are 40.0, 13.0, 12.0, 8.0, 5.0, and 2.0 vs. 40.0, 13.2, 8.9, 4.9, 16.7, and 3.3, respectively, using a relative scattering model of a clear atmosphere. In one Landsat multispectral scanner image the haze value differences for Bands 4, 5, 6, and 7 were 30.0, 50.0, 50.0, and 40.0 for the old method vs. 30.0, 34.4, 43.6, and 6.4 for the new method using a relative scattering model of a hazy atmosphere.
      Full-text · Article · Apr 1988 · Remote Sensing of Environment

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