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Automatic Change Detection Based on the Independent Component Analysis and Fuzzy C-Means Methods

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Business Intelligence (CBI 2022)

Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 449))

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Abstract

Change analysis, an automated process to measure the change on the Earth surface by jointly analyzing two temporally separated images, becomes a significant research domain to understand the changes in land-cover, it provides important knowledge and data to be used in many other fields such as land-cover analyses, mapping generation, planning traffics, etc. This paper describes and evaluates an unsupervised method for change detection in satellite images by following two major steps: The first step focuses on data reduction using the ICA algorithm to improve the efficiency of the classifier. The second step deals with the Fuzzy C-Means classification method to find specified clusters. Changed and unchanged areas are mapped in a binary image. Three different datasets are used to evaluate the result performance of the proposed system, and experiments results show that the used approach can detect changes in multi-temporal satellite images with good accuracy. To show the effectiveness, the comparisons with some other methods from state-of-the-art are shown on multitemporal images captured by Radarsat1 satellite SAR on the Ottawa area.

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Authors and Affiliations

  1. Laboratory of Innovation in Mathematics and Applications and Information Technologies (LIMATI), Sultan Moulay Slimane University, Beny Mellal, Morocco

    Abdelkrim Maarir, Es-said Azougaghe & Belaid Bouikhalene

Authors
  1. Abdelkrim Maarir
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  2. Es-said Azougaghe
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  3. Belaid Bouikhalene
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Correspondence to Abdelkrim Maarir .

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Editors and Affiliations

  1. Sultan Moulay Slimane University, Beni-Mellal, Morocco

    Mohamed Fakir

  2. Sultan Moulay Slimane University, Beni Mellal, Morocco

    Mohamed Baslam

  3. Sultan Moulay Slimane University, Beni-Mellal, Morocco

    Rachid El Ayachi

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Maarir, A., Azougaghe, Es., Bouikhalene, B. (2022). Automatic Change Detection Based on the Independent Component Analysis and Fuzzy C-Means Methods. In: Fakir, M., Baslam, M., El Ayachi, R. (eds) Business Intelligence. CBI 2022. Lecture Notes in Business Information Processing, vol 449. Springer, Cham. https://doi.org/10.1007/978-3-031-06458-6_14

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  • DOI: https://doi.org/10.1007/978-3-031-06458-6_14

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