A new neural architecture for feature extraction of remote sensing data Online publication date: Sat, 22-Feb-2020
by Mustapha Si Tayeb; Hadria Fizazi
International Journal of Computational Science and Engineering (IJCSE), Vol. 21, No. 1, 2020
Abstract: The paper presents a novel method for the classification of remote sensing data. The proposed approach comprises two main steps: 1) extractor multi-layer perceptron (EMLP) is used for feature extraction of the remote sensing data; 2) the data resulted from the EMLP are classified using support vector machine (SVM) algorithm. The contribution of this work is mainly in the creation of the EMLP method based on the multi-layer perceptron (MLP) method, which has the role of creating a dataset more representative of the classes from the original dataset. To better situate and evaluate our proposed approach, we applied our proposed technique to three datasets, namely, Statlog Landsat satellite, urban land cover and Landsat TM Oran. Several measures were used, for example, classification rate, classification error, precision, recall and F-measure. The experimental results show that the proposed approach (EMLP-SVM) is more efficient and powerful than the basic methods (MLP and SVM) and the existing state-of-the-art classification methods.
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