2022/03/29 by P Gokul, P, Gokul, Ujjwal Verma +1 · 1 citation
Chemistry · Engineering · Environmental Science · #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Remote Sensing and LiDAR Applications #Remote-Sensing Image Classification #Wood and Agarwood Research
paper · pdf · doi:10.48550/arxiv.2203.15687
openalex publication_date 2022/03/29 · openalex created_date 2022/04/03 · openalex updated_date 2026/07/28
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.