Abstract
This paper presents a method for SIC to classify a PatternNet satellite image dataset taken from Google Earth and Google Map Application newly adopted in 2017, this SIC method use firstly a pre-processing step to verify the way of how represent the gray level of each sample image using multiple features based descriptors to handling the problem of selecting the descriptors for SIC. It suggested to use several type of texture feature extraction method, each of these method tested with the Support Vector Machine (SVM) to verify its ability to extract d a discriminative features. Also the feature selection method used to remove the less informative features of each method to get the more relevant features, finally the decision result of each feature extraction method from the classifier tested with feature combination methods, it is used to improve the final decision of the SIC method by combine multi feature extraction method.
Highlights
Remote Sensing (RS) is the art of science that acquiring information about a specific area, object, or phenomena by analysing the obtaining data from the device that is not contact with the area, object or phenomenon
Different methods and algorithms are used for RS image classification such as maximum likelihood, minimum distance, Artificial Neural Network (ANN), decision tree, linear discriminant analysis, Support Vector Machine (SVM), whilst the search on the optimal one is still continue in terms of suitability for different applications [4], The feature Selection method used to minimize the number of features [5]
The preprocessing step used to convert the input images to gray levels depend on the type of feature extracted method, several feature extraction methods used to describe the information from different classes of satellite imagery, these features extracted from all satellite imagery, these features has been entered in to feature selection method in order to remove as possible the not useful features, the selected features divided in to part used to train the classifier and the other part used to test the performance of these extracted features
Summary
Remote Sensing (RS) is the art of science that acquiring information about a specific area, object, or phenomena by analysing the obtaining data from the device that is not contact with the area, object or phenomenon. The human eye is like a sensor that responds to the reflected light from an object, and pictures the object according to received light intensities [1]. RS in the classical sense is not a scientific discipline; it is rather a big variety of diagnostic methods by using electromagnetic waves. The applications cover a wide range of humanities disciplines, e.g. Botany, archaeology, geology, meteorology and security aspects, etc. There are several methods and techniques that are used in RS images, the choice of classification method depend on many regards such as: information gathered from different sensor, the sample label is known or not, the training sample nature used in RS image classification, the pixel informative nature of the data, the number of the output for each spatial data element, etc. Different methods and algorithms are used for RS image classification such as maximum likelihood, minimum distance, Artificial Neural Network (ANN), decision tree, linear discriminant analysis, Support Vector Machine (SVM), whilst the search on the optimal one is still continue in terms of suitability for different applications [4], The feature Selection method used to minimize the number of features [5]
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