Natura 2000生境卫星图像分类的自然数值网络
Natural Numerical Networks for Natura 2000 habitats classification by satellite images
摘要：
基于完全图上前向-后向扩散型非线性偏微分方程的数值解，引入自然数值网络作为一种新的分类算法。建议的自然数值网络应用于开放的重要的环境和自然保护任务，通过使用卫星图像自动识别受保护的栖息地。在自然数值网络中，前向扩散导致特征空间中的点彼此相向移动。相反的效果，使点彼此远离，是由反向扩散引起的。这就产生了所需的分类。自然数值网络包含在该方法的学习阶段中优化的几个参数。在学习参数和优化网络图的拓扑结构后，进行生境识别所需的分类。每个栖息地的相关性地图作为验证分类和寻找新的自然2000栖息地外观的工具。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
