论文标题
基于纹理的原型网络，用于几个森林覆盖的语义分割：对不同地理区域的概括
Texture based Prototypical Network for Few-Shot Semantic Segmentation of Forest Cover: Generalizing for Different Geographical Regions
论文作者
论文摘要
森林在减少温室气体排放和减轻气候变化方面起着至关重要的作用，除了维持世界的生物多样性。现有的基于卫星的森林监测系统采用了仅限于特定区域的监督学习方法，并依靠手动注释的数据来识别森林。这项工作将森林识别视为几个射击语义分割任务，以实现在不同地理区域的概括。提出的几片分段方法包含了原型网络中的纹理注意模块，以突出森林的质地特征。的确，森林表现出与其他阶级不同的特征性质地，例如道路，水等。在这项工作中，培训了拟议的方法来识别南亚的热带森林，并在借助少数（1张图像）手动注释的温度森林图像来确定中欧的温带森林。使用所提出的方法获得了森林类别的0.62的IOU（1向1-shot），该方法比现有的少数弹性语义分割方法高得多（锅底0.46）。该结果表明，所提出的方法可以概括在地理区域以供森林识别，从而创造了开发全球森林覆盖识别工具的机会。
Forest plays a vital role in reducing greenhouse gas emissions and mitigating climate change besides maintaining the world's biodiversity. The existing satellite-based forest monitoring system utilizes supervised learning approaches that are limited to a particular region and depend on manually annotated data to identify forest. This work envisages forest identification as a few-shot semantic segmentation task to achieve generalization across different geographical regions. The proposed few-shot segmentation approach incorporates a texture attention module in the prototypical network to highlight the texture features of the forest. Indeed, the forest exhibits a characteristic texture different from other classes, such as road, water, etc. In this work, the proposed approach is trained for identifying tropical forests of South Asia and adapted to determine the temperate forest of Central Europe with the help of a few (one image for 1-shot) manually annotated support images of the temperate forest. An IoU of 0.62 for forest class (1-way 1-shot) was obtained using the proposed method, which is significantly higher (0.46 for PANet) than the existing few-shot semantic segmentation approach. This result demonstrates that the proposed approach can generalize across geographical regions for forest identification, creating an opportunity to develop a global forest cover identification tool.
