Abstract
Forest changes caused by fires, clear-cuts, and Land Use/Land Cover (LULC) changes have negatively affected the climate, wildlife, and global ecosystem. By monitoring the forest changes, managers and planners can make appropriate decisions to preserve these natural areas. In this regard, this paper presents a novel deep learning-based forest change detection method including two main steps: (1) producing a new difference image enabling a more efficient distinction of changed and unchanged areas, and (2) generating a reliable forest change map by applying the recurrent residual-based U-Net deep neural network (R2U-Net) on the difference image. The recently introduced forest fused difference image (FFDI) is first improved by modifying its weighted angular operator as well as applying the fast local laplacian filter (FLLF) to generate an enhanced forest fused difference image (EFFDI). R2U-Net is subsequently used to segment the EFFDI into the changed and unchanged regions because it preserves their geometric shapes more effectively than other U-net variants. To assess the efficacy of the presented method, experimental results were conducted on four bi-temporal images acquired by the Sentinel 2 and Landsat 8 satellite sensors. The qualitative and quantitative results demonstrated the effectiveness of the proposed EFFDI in reflecting the true forest changes from the background. Moreover, compared with the other conventional U-Net-based models, including U-Net, ResU-Net, and U-Net ++, forest changes and their geometrical details were better preserved by R2U-Net. Furthermore, the proposed approach outperformed the other state-of-the-art supervised and unsupervised change detection methods in terms of quantitative and qualitative results, demonstrating its high potential for forest change detection applications.
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The training and testing datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.
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All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by E.K. The first draft of the manuscript was written by E.K. All authors read and approved the final manuscript.
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Khankeshizadeh, E., Mohammadzadeh, A., Moghimi, A. et al. FCD-R2U-net: Forest change detection in bi-temporal satellite images using the recurrent residual-based U-net. Earth Sci Inform 15, 2335–2347 (2022). https://doi.org/10.1007/s12145-022-00885-6
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DOI: https://doi.org/10.1007/s12145-022-00885-6