Satellite classification and segmentation using non-additive entropy

2nd International Conference on Mathematical Modeling in Physical Sciences 2013

物理学;数学

Assirati, Lucas^1 ; Martinez, Alexandre Souto^2,3 ; Bruno, Odemir Martinez^1

Instituto de Física de São Carlos (IFSC), Universidade de São Paulo, Av. Trabalhador São Carlense, 400, 13560-970 São Carlos, SP, Brazil^1

Faculdade de Filosofia, Ciências e Letras de Ribeirão Preto (FFCLRP), Universidade de São Paulo (USP), Avenida Bandeirantes, 3900, 14040-901 Ribeirão Preto, SP, Brazil^2

Instituto Nacional de Cincias e Tecnologia de Sistemas Complexos, Brazil^3

关键词: Agricultural activities;    Entropic indexes;    Image partition;    Numerical experiments;    Regions of interest;    Satellite images;    Standard entropy;    Tsallis entropies;   

Others  :  https://iopscience.iop.org/article/10.1088/1742-6596/490/1/012086/pdf
DOI  :  10.1088/1742-6596/490/1/012086

来源: IOP

【 摘 要 】

Here we compare the Boltzmann-Gibbs-Shannon (standard) with the Tsallis entropy on the pattern recognition and segmentation of colored images obtained by satellites, via "Google Earth". By segmentation we mean particionate an image to locate regions of interest. Here, we discriminate and define an image partition classes according to a training basis. This training basis consists of three pattern classes: aquatic, urban and vegetation regions. Our numerical experiments demonstrate that the Tsallis entropy, used as a feature vector composed of distinct entropic indexes q outperforms the standard entropy. There are several applications of our proposed methodology, once satellite images can be used to monitor migration form rural to urban regions, agricultural activities, oil spreading on the ocean etc.