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Remote Sens. 2014, 6(12), 11810-11828; doi:10.3390/rs61211810

An Assessment of Polynomial Regression Techniques for the Relative Radiometric Normalization (RRN) of High-Resolution Multi-Temporal Airborne Thermal Infrared (TIR) Imagery

1
The Department of Geography, University of Calgary, 2500 University Dr. NW, Calgary, AB T2N 1N4, Canada
2
Canadian Pacific Railway, 7550 Ogden Dale Road S.E. Calgary, AB T2C 4X9, Canada
3
The Department of Physics & Astronomy, University of Alabama, Box 870324, Tuscaloosa, AL 35487-0324, USA
*
Author to whom correspondence should be addressed.
Received: 10 September 2014 / Revised: 8 November 2014 / Accepted: 18 November 2014 / Published: 27 November 2014
(This article belongs to the Special Issue Recent Advances in Thermal Infrared Remote Sensing)
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Abstract

Thermal Infrared (TIR) remote sensing images of urban environments are increasingly available from airborne and satellite platforms. However, limited access to high-spatial resolution (H-res: ~1 m) TIR satellite images requires the use of TIR airborne sensors for mapping large complex urban surfaces, especially at micro-scales. A critical limitation of such H-res mapping is the need to acquire a large scene composed of multiple flight lines and mosaic them together. This results in the same scene components (e.g., roads, buildings, green space and water) exhibiting different temperatures in different flight lines. To mitigate these effects, linear relative radiometric normalization (RRN) techniques are often applied. However, the Earth’s surface is composed of features whose thermal behaviour is characterized by complexity and non-linearity. Therefore, we hypothesize that non-linear RRN techniques should demonstrate increased radiometric agreement over similar linear techniques. To test this hypothesis, this paper evaluates four (linear and non-linear) RRN techniques, including: (i) histogram matching (HM); (ii) pseudo-invariant feature-based polynomial regression (PIF_Poly); (iii) no-change stratified random sample-based linear regression (NCSRS_Lin); and (iv) no-change stratified random sample-based polynomial regression (NCSRS_Poly); two of which (ii and iv) are newly proposed non-linear techniques. When applied over two adjacent flight lines (~70 km2) of TABI-1800 airborne data, visual and statistical results show that both new non-linear techniques improved radiometric agreement over the previously evaluated linear techniques, with the new fully-automated method, NCSRS-based polynomial regression, providing the highest improvement in radiometric agreement between the master and the slave images, at ~56%. This is ~5% higher than the best previously evaluated linear technique (NCSRS-based linear regression). View Full-Text
Keywords: thermal infrared; relative radiometric normalization; non-linear; TABI-1800; no-change stratified random sample thermal infrared; relative radiometric normalization; non-linear; TABI-1800; no-change stratified random sample
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Mustafizur Rahman, M.; Hay, G.J.; Couloigner, I.; Hemachandran, B.; Bailin, J. An Assessment of Polynomial Regression Techniques for the Relative Radiometric Normalization (RRN) of High-Resolution Multi-Temporal Airborne Thermal Infrared (TIR) Imagery. Remote Sens. 2014, 6, 11810-11828.

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