基于特征融合图像分割算法的生态廊道提取

为提取最短路径生态廊道且兼顾多种生物的迁徙可行性，提出基于特征融合图像分割算法的生态廊道提取方法。融合颜色特征与纹理特征得到生态廊道的感兴趣区域，构建卷积神经网络模型，将研究区景观分为林地、耕地、草地、水域等类型，据此构建路径栅格图；利用二维信息素更新策略、动态启发因子信息素因子策略改进传统蚁群算法，以栅格图为对象使用改进蚁群算法规划最短的生态廊道路径。结果表明，该方法图像分割F值在0.954～0.984之间，波动性小；提取的生态廊道路径相对较短、拐点较少，起始点与终点之间更容易实现物质流动。

Extraction of Ecological Corridor Based on Feature Fusion Image Segmentation Algorithm

In order to extract the shortest path ecological corridor and take into account the migration feasibility of various organisms, an ecological corridor extraction method based on feature fusion image segmentation algorithm was proposed. Color features and texture features were integrated to obtain the area of interest of the ecological corridor, the convolutional neural network model was built to divide the landscape of the study area into forest land, cultivated land, grassland, water and other types, and a path grid map accordingly was constructed; the two-dimensional pheromone update strategy and the dynamic heuristic factor pheromone factor strategy were used to improve the traditional ant colony algorithm, the improved ant colony algorithm was used to plan the shortest ecological corridor path based on a grid graph. The experimental results showed that the image segmentation F value of the modified method was between 0.954 and 0.984, and the fluctuation was small; the extracted ecological corridor path was relatively short, with fewer inflection points, making it easier to realize material flow between the starting point and the end point.