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Convolutional Autoencoder for Remote Sensing Change Detection

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Applications of Remote Sensing and GIS Based on an Innovative Vision (ICRSSSA 2022)

Abstract

Change Detection (CD) is crucial for effectively recognizing and analyzing spatial or spectral changes. Binary change detection uses co-registered images of an area obtained at different times to assign changes and no changes per pixel. Image processing, computer vision, and remote sensing desire more accurate binary CD maps. Deep Learning, notably CNNs, detects the environmental change in binary change systems. This work proposes a heuristic-based Siamese Convolutional Autoencoder for CD problem. Three Siamese architectures are shown. We examined how layer order and pooling layer affect CD map accuracy. LEVIR-CD is used to evaluate the proposed architectures. Experimental data reveal that the suggested technique outperforms Siamese by 3%.

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Author information

Authors and Affiliations

  1. Faculty of Computer and Information Sciences, Ain Shams University, Cairo, Egypt

    Menna M. Elkholy, Dina ElSayad, Hala M. Ebeid & Mohamed F. Tolba

  2. Data Reception, Analysis, and Receiving Station Affairs, National Authority for Remote Sensing and Space Science, Cairo, Egypt

    Marwa Mostafa

Authors
  1. Menna M. Elkholy
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  2. Marwa Mostafa
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  3. Dina ElSayad
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  4. Hala M. Ebeid
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  5. Mohamed F. Tolba
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Corresponding author

Correspondence to Menna M. Elkholy .

Editor information

Editors and Affiliations

  1. National Authority for Remote Sensing and Space Sciences, Cairo, Egypt

    Abd Alla Gad

  2. Structure, Thermal Control and Space Environment Department, National Authority for Remote Sensing and Space Sciences, Cairo, Egypt

    Dalia Elfiky

  3. Water and Water Structures Engineering Department, Faculty of Engineering, Zagazig University, Zagazig, Egypt

    Abdelazim Negm

  4. Engineering Applications and Water Division, National Authority for Remote Sensing and Space Sciences, Cairo, Egypt

    Salwa Elbeih

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© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

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Elkholy, M.M., Mostafa, M., ElSayad, D., Ebeid, H.M., Tolba, M.F. (2023). Convolutional Autoencoder for Remote Sensing Change Detection. In: Gad, A.A., Elfiky, D., Negm, A., Elbeih, S. (eds) Applications of Remote Sensing and GIS Based on an Innovative Vision . ICRSSSA 2022. Springer Proceedings in Earth and Environmental Sciences. Springer, Cham. https://doi.org/10.1007/978-3-031-40447-4_26

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