A Stable Gaussian Fitting Procedure for the Parameterization of Remote Sensed Thermal Images

Algorithms

Roberta Anniballe¹  Stefania Bonafoni² 

[1] Department of Information Engineering, Electronics and Telecommunications, Sapienza University of Rome, via Eudossiana 18, 00184 Roma, Italy; E-Mail:;Department of Engineering, University of Perugia, via G. Duranti 93, 06125 Perugia, Italy; E-Mail:

关键词: Gaussian fit;    regression;    least-square;    satellite image parameterization;    urban heat island;   

DOI  :  10.3390/a8020082

来源: mdpi

【 摘 要 】

An image analysis procedure based on a two dimensional Gaussian fitting is presented and applied to satellite maps describing the surface urban heat island (SUHI). The application of this fitting technique allows us to parameterize the SUHI pattern in order to better understand its intensity trend and also to perform quantitative comparisons among different images in time and space. The proposed procedure is computationally rapid and stable, executing an initial guess parameter estimation by a multiple regression before the iterative nonlinear fitting. The Gaussian fit was applied to both low and high resolution images (1 km and 30 m pixel size) and the results of the SUHI parameterization shown. As expected, a reduction of the correlation coefficient between the map values and the Gaussian surface was observed for the image with the higher spatial resolution due to the greater variability of the SUHI values. Since the fitting procedure provides a smoothed Gaussian surface, it has better performance when applied to low resolution images, even if the reliability of the SUHI pattern representation can be preserved also for high resolution images.

【 授权许可】

CC BY   
© 2015 by the authors; licensee MDPI, Basel, Switzerland.