Remote Sens. 2009, 1(3), 278-299; doi:10.3390/rs1030278
Article

A Highly Accurate Classification of TM Data through Correction of Atmospheric Effects

1 Mathematics Department, Hampton University, Hampton VA 23668, USA 2 University of Wisconsin-Madison, Madison, WI, 53706 USA 3 NASA Langley Research Center for Atmospheric Science Hampton, VA, 23681 USA
* Author to whom correspondence should be addressed.
Received: 11 May 2009; in revised form: 29 June 2009 / Accepted: 8 July 2009 / Published: 15 July 2009
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Abstract: Atmospheric correction impacts on the accuracy of satellite image-based land cover classification are a growing concern among scientists. In this study, the principle objective was to enhance classification accuracy by minimizing contamination effects from aerosol scattering in Landsat TM images due to the variation in solar zenith angle corresponding to cloud-free earth targets. We have derived a mathematical model for aerosols to compute and subtract the aerosol scattering noise per pixel of different vegetation classes from TM images of Nicolet in north-eastern Wisconsin. An algorithm in C++ has been developed with iterations to simulate, model, and correct for the solar zenith angle influences on scattering. Results from a supervised classification with corrected TM images showed increased class accuracy for land cover types over uncorrected images. The overall accuracy of the supervised classification was improved substantially (between 13% and 18%). The z-score shows significant difference between the corrected data and the raw data (between 4.0 and 12.0). Therefore, the atmospheric correction was essential for enhancing the image classification.
Keywords: improvement of supervised classification accuracy; remote sensing interpretation; simulation; modeling

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MDPI and ACS Style

Elmahboub, W.; Scarpace, F.; Smith, B. A Highly Accurate Classification of TM Data through Correction of Atmospheric Effects. Remote Sens. 2009, 1, 278-299.

AMA Style

Elmahboub W, Scarpace F, Smith B. A Highly Accurate Classification of TM Data through Correction of Atmospheric Effects. Remote Sensing. 2009; 1(3):278-299.

Chicago/Turabian Style

Elmahboub, Widad; Scarpace, Frank; Smith, Bill. 2009. "A Highly Accurate Classification of TM Data through Correction of Atmospheric Effects." Remote Sens. 1, no. 3: 278-299.

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