Natural Numerical Networks for Natura 2000 habitats classification by satellite images

TL;DR

This paper introduces a novel classification algorithm called natural numerical networks, which uses PDE-based diffusion processes to identify Natura 2000 habitats from satellite images, aiding environmental conservation efforts.

Contribution

The paper presents a new PDE-based classification method that effectively identifies protected habitats in satellite imagery, with optimized parameters and topology for improved accuracy.

Findings

Effective habitat classification from satellite images

Relevancy maps for habitat validation

Optimized parameters improve classification accuracy

Abstract

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.