Published September 27, 2021 | Version Published
Conference paper Open

Land Use Change Detection Using Deep Siamese Neural Networks and Weakly Supervised Learning

  • 1. Indian Institute of Information Technology Guwahati, Assam 781015, India
  • 2. CYENS Center of Excellence, Nicosia, Cyprus
  • 3. CYENS Center of Excellence, Nicosia, Cyprus and Dept. of Computer Science, University of Twente, Enschede, The Netherlands

Description

A weakly supervised change detection method is proposed for remotely sensed multi-temporal images, by utilizing a Siamese neural
network architecture. The architecture of the Siamese network is a combination of two multi-filter multi-scale deep convolutional neural networks (MFMS DCNN). Initially, the Siamese network is trained by utilizing the image-level semantic labels of the image pairs in the dataset. The features of the image pairs are obtained using the trained network to generate the difference image (DI). Then, a combination of the PCA and the K-means algorithms has been used to produce the change map for the pair of images. Experiments were carried out using two remotely sensed image datasets. The weakly supervised method proposed in this paper offers better results in comparison to both weakly supervised- and unsupervised-based state-of-the-art models and techniques.

Notes

This work has been partly supported by the project that has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 739578 (RISE – Call: H2020-WIDESPREAD-01-2016-2017-TeamingPhase2) and the Republic of Cyprus through the Deputy Ministry of Research, Innovation and Digital Policy.

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Additional details

Funding

European Commission
RISE – Research Center on Interactive Media, Smart System and Emerging Technologies 739578