Natural Numerical Networks for Natura 2000 habitats classification by satellite images
This addresses an important environmental and nature conservation problem by enabling automated habitat identification, though it appears incremental as it applies a novel method to a specific domain task.
The paper tackles the automated identification of protected Natura 2000 habitats using satellite images by introducing natural numerical networks, a new classification algorithm based on solving nonlinear partial differential equations on graphs, which achieves classification through forward-backward diffusion in feature space.
Natural numerical networks are introduced as a new classification algorithm based on the numerical solution of nonlinear partial differential equations of forward-backward diffusion type on complete graphs. The proposed natural numerical network is applied to open important environmental and nature conservation task, the automated identification of protected habitats by using satellite images. In the natural numerical network, the forward diffusion causes the movement of points in a feature space toward each other. The opposite effect, keeping the points away from each other, is caused by backward diffusion. This yields the desired classification. The natural numerical network contains a few parameters that are optimized in the learning phase of the method. After learning parameters and optimizing the topology of the network graph, classification necessary for habitat identification is performed. A relevancy map for each habitat is introduced as a tool for validating the classification and finding new Natura 2000 habitat appearances.