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Convolutional Neural Network for Satellite Image Classification

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Intelligent Information and Database Systems: Recent Developments (ACIIDS 2019)

Part of the book series: Studies in Computational Intelligence ((SCI,volume 830))

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Abstract

Multimedia applications and processing is an exciting topic, and it is a key of many applications of artificial intelligent like video summarization, image retrieval or image classification. A convolutional neural networks have been successfully applied on multimedia approaches and used to create a system able to handle the classification without any human’s interactions. In this paper, we produce effective methods for satellite image classification that are based on deep learning and using the convolutional neural network for features extraction by using AlexNet, VGG19, GoogLeNet and Resnet50 pretraining models. The Resnet50 model achieves a promising result than other models on three different dataset SAT4, SAT6 and UC Merced Land. The accuracy of classification of this model for UC Merced Land dataset is 98%, for SAT4 is 95.8%, and the result for SAT6 is 94.1%.

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Authors and Affiliations

  1. College of Computer Science and IT, Computer Sciences Department, University of Al-Qadisiyah, Diwaniyah, Iraq

    Mohammed Abbas Kadhim & Mohammed Hamzah Abed

Authors
  1. Mohammed Abbas Kadhim
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  2. Mohammed Hamzah Abed
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Correspondence to Mohammed Abbas Kadhim .

Editor information

Editors and Affiliations

  1. Department of Information Systems, Wrocław University of Science and Technology, Wrocław, Poland

    Maciej Huk

  2. Department of Information Systems, Wrocław University of Science and Technology, Wrocław, Poland

    Marcin Maleszka

  3. Department of Management, Gdańsk University of Technology, Gdańsk, Poland

    Edward Szczerbicki

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Kadhim, M.A., Abed, M.H. (2020). Convolutional Neural Network for Satellite Image Classification. In: Huk, M., Maleszka, M., Szczerbicki, E. (eds) Intelligent Information and Database Systems: Recent Developments. ACIIDS 2019. Studies in Computational Intelligence, vol 830. Springer, Cham. https://doi.org/10.1007/978-3-030-14132-5_13

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