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Satellite Image Classification using Multi Features Based Descriptors

Journal: International Research Journal of Advanced Engineering and Science (IRJAES) (Vol.3, No. 2)

Publication Date:

Authors : ;

Page : 87-94

Keywords : Local Binary Pattern; Speeded Up Roubst Features; Gray Level Co-occurrence Matrix; Remote Sensing; Support Vector Machine.;

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During the last few decades, Remote Sensing (RS) science develop in many ways in the form of spatial, spectral and temporal resolution, this make the RS used in many application like Agriculture, Urban planning, Military operation, and others. One of the most important of these application is Satellite Image Classification (SIC), SIC still a challenge method due to the variant types of data retrieved from the satellites, also the environment factors and the nature of the earth effect to the any build of the SIC application, because it make the decision of what type of signature that take from the satellite image must be take very carefully. The type of descriptors that extracted from the satellite data play an important role in the case of SIC methodology, thus the type in order to get High level features that best describe the content of the image has to be done by take the low level features and the machine learning techniques.

Last modified: 2018-05-13 23:34:06