FSC-Mask2Former： A Forest Panoptic Segmentation Method Based on UAV Imagery

YANG Zhen; YAO Zongqi; ZHANG Xiaoli

doi:10.11834/jrs.20266167

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FSC-Mask2Former： A Forest Panoptic Segmentation Method Based on UAV Imagery
“2026年6月12日北京林业大学林木资源高效生产全国重点实验室的杨振、张晓丽团队在《遥感学报》发文，介绍了其在无人机森林遥感领域的研究进展，团队建立了FSC-Mask2Former全景分割模型，通过频域纹理感知与对比学习策略，解决了高郁闭度森林场景下树种混淆与像素归属冲突难题，为实现复杂生境下的森林全要素精细化制图提供了低成本、高效率的技术方案。”
- role：First author第一作者
- Affiliation：
  State Key Laboratory of Efficient Production of Forest Resources, College of Forestry, Beijing Forestry University, Beijing 100083, China
  Beijing Key Laboratory of Precision Forestry, College of Forestry, Beijing Forestry University, Beijing 100083, China
  Key Laboratory of Forest Cultivation and Protection, Ministry of Education, Beijing Forestry University, Beijing 100083, China
- Email：yangzhen2023@bjfu.edu.cn
- Introduction：杨振，研究方向为森林树种识别。E-mail： yangzhen2023@bjfu.edu.cn
YANG Zhen
123 * ,
- Affiliation：
  State Key Laboratory of Efficient Production of Forest Resources, College of Forestry, Beijing Forestry University, Beijing 100083, China
  Beijing Key Laboratory of Precision Forestry, College of Forestry, Beijing Forestry University, Beijing 100083, China
  Key Laboratory of Forest Cultivation and Protection, Ministry of Education, Beijing Forestry University, Beijing 100083, China
YAO Zongqi
123 * ,
- role：Corresponding author通信作者
- Affiliation：
  State Key Laboratory of Efficient Production of Forest Resources, College of Forestry, Beijing Forestry University, Beijing 100083, China
  Beijing Key Laboratory of Precision Forestry, College of Forestry, Beijing Forestry University, Beijing 100083, China
  Key Laboratory of Forest Cultivation and Protection, Ministry of Education, Beijing Forestry University, Beijing 100083, China
- Email：zhangxl@bjfu.edu.cn
- Introduction：张晓丽，研究方向为资源环境遥感。E-mail： zhangxl@bjfu.edu.cn
ZHANG Xiaoli
123 *
Pages: 1-20(2026)
DOI：10.11834/jrs.20266167
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FSC-Mask2Former： A Forest Panoptic Segmentation Method Based on UAV Imagery

YANG Zhen

YAO Zongqi

ZHANG Xiaoli

DOI：10.11834/jrs.20266167

AI Introduction

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Figs( 11 ) Tabs( 9 )

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