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An automated segmentation of NATURA 2000 habitats from Sentinel-2 optical data

  • * Corresponding author

    * Corresponding author 

This work was supported by projects APVV-16-0431, APVV-15-0522, VEGA 1/0608/15 and ESA Contract No. 4000122575/17/NL/SC

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  • In this paper, we present a mathematical model and numerical method designed for the segmentation of satellite images, namely to obtain in an automated way borders of Natura 2000 habitats from Sentinel-2 optical data. The segmentation model is based on the evolving closed plane curve approach in the Lagrangian formulation including the efficient treatment of topological changes. The model contains the term expanding the curve in its outer normal direction up to the region of habitat boundary edges, the term attracting the curve accurately to the edges and the smoothing term given by the influence of local curvature. For the numerical solution, we use the flowing finite volume method discretizing the arising advection-diffusion intrinsic partial differential equation including the asymptotically uniform tangential redistribution of curve grid points. We present segmentation results for satellite data from a selected area of Western Slovakia (Záhorie) where the so-called riparian forests represent the important European Natura 2000 habitat. The automatic segmentation results are compared with the semi-automatic segmentation performed by the botany expert and with the GPS tracks obtained in the field. The comparisons show the ability of our numerical model to segment the habitat areas with the accuracy comparable to the pixel resolution of the Sentinel-2 optical data.

    Mathematics Subject Classification: Primary: 35R01, 65M08; Secondary: 35R37, 92F05.

    Citation:

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  • Figure 1.  First row: the original image $ I^0 $ and smoothed image $ I^{\sigma_0} $. Second row: the visualization of $ g(\mathbf{x}) $, smoothed edge detector $ g_1(\mathbf{x}) $ and a zoom of the vector field $ -\nabla g_1(\mathbf{x}) $ where we see arrows pointing towards the edges in $ I^0 $. Third row: the functions $ H(\mathbf{x}) $ using (5) and $ g_2(\mathbf{x} ) $ evaluated by using the initial circle plotted in the Fourth row, left. Fourth row: the initial segmentation curve placed in $ I^0 $ and its time evolution until the final segmentation state (bottom right). In the middle image we see that the evolving curve undergoes topological changes which are resolved efficiently

    Figure 2.  Closed planar curve discretization (left) corresponding to the uniform discretization of the unit circle (right)

    Figure 3.  Visualization of the curve discretization: curve grid points (red) and their midpoints. Finite volumes $ \mathbf{p}_{i-1}, \mathbf{p}_i, $ and $ \mathbf{p}_{i+1} $ are highlighted by green, brown and yellow color. Note that $ \mathbf{p}_i $ is not a straight line given by $ \mathbf{x}_{i-\frac{1}2} $ and $ \mathbf{x}_{i+\frac{1}2} $, but a broken line given by $ \mathbf{x}_{i-\frac{1}2} $, $ \mathbf{x}_{i} $ and $ \mathbf{x}_{i+\frac{1}2} $, see also [1]

    Figure 4.  First row: the original image $ I^0 $ and smoothed image $ I^{\sigma_0} $. Second row: the visualization of the function $ g(\mathbf{x}) $ and smoothed edge detector $ g_1(\mathbf{x}) $. Third row: the function $ H(\mathbf{x}) $ and $ g_2(\mathbf{x}) $ evaluated by using the initial circle plotted in the Fourth row, left. Fourth row: the initial segmentation curve placed in $ I^0 $ (bottom left) and its time evolution (bottom middle) until the final segmentation state (bottom right) is reached

    Figure 5.  First row: the original image $ I^0 $ and smoothed image $ I^{\sigma_0} $. Second row: the visualization of the function $ g(\mathbf{x}) $ and smoothed edge detector $ g_1(\mathbf{x}) $. Third row: the function $ H(\mathbf{x}) $ and $ g_2(\mathbf{x}) $ evaluated by using the initial circle plotted in the Fourth row, left. Fourth row: the initial segmentation curve placed in $ I^0 $ (bottom left) and its time evolution (bottom middle) until the final segmentation state (bottom right) is reached

    Figure 6.  Left: the evolution of the segmentation curve from the initial circle to the final state. Right: the final automatic segmentation (red) together with the result of the semi-automatic segmentation (yellow) and the GPS track (light-blue)

    Figure 7.  Left: the evolution of the segmentation curve from the initial circle to the final state. Right: the final automatic segmentation (red) together with the result of the semi-automatic segmentation (yellow) and the GPS track (light-blue)

    Figure 8.  Left: the evolution of the segmentation curve from the initial circle to the final state. Right: the final automatic segmentation (red) together with the result of the semi-automatic segmentation (yellow) and the GPS track (light-blue)

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