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.
    https://doi.org/10.3390/rs61211810
    AMA Style
    
                                Mustafizur Rahman M,                                 Hay GJ,                                 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 Sensing. 2014; 6(12):11810-11828.
        https://doi.org/10.3390/rs61211810
    
    Chicago/Turabian Style
    
                                Mustafizur Rahman, Mir,                                 Geoffrey J. Hay,                                 Isabelle Couloigner,                                 Bharanidharan Hemachandran,                                 and Jeremy Bailin.        
                2014. "An Assessment of Polynomial Regression Techniques for the Relative Radiometric Normalization (RRN) of High-Resolution Multi-Temporal Airborne Thermal Infrared (TIR) Imagery" Remote Sensing 6, no. 12: 11810-11828.
        https://doi.org/10.3390/rs61211810
    
    APA Style
    
                                Mustafizur Rahman, M.,                                 Hay, G. J.,                                 Couloigner, I.,                                 Hemachandran, B.,                                 & Bailin, J.        
        
        (2014). An Assessment of Polynomial Regression Techniques for the Relative Radiometric Normalization (RRN) of High-Resolution Multi-Temporal Airborne Thermal Infrared (TIR) Imagery. Remote Sensing, 6(12), 11810-11828.
        https://doi.org/10.3390/rs61211810