Title: A new neural architecture for feature extraction of remote sensing data

Authors: Mustapha Si Tayeb; Hadria Fizazi

Addresses: Department of Computer Science, Faculty of Mathematics and Computer Science, Université des Sciences et de la Technologie d'Oran – Mohamed Boudiaf (USTO-MB), BP 1505 M'naouer, 31000 Oran, Algeria ' Department of Computer Science, Faculty of Mathematics and Computer Science, Université des Sciences et de la Technologie d'Oran – Mohamed Boudiaf (USTO-MB), BP 1505 M'naouer, 31000 Oran, Algeria

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.

Keywords: classification methods; feature extraction; remote sensing data; extractor multi-layer perceptron; EMLP; support vector machine; SVM; supervised learning.

DOI: 10.1504/IJCSE.2020.105216

International Journal of Computational Science and Engineering, 2020 Vol.21 No.1, pp.95 - 104

Received: 31 Jul 2017
Accepted: 23 Jan 2018

Published online: 11 Feb 2020 *

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