SAM2Former: Segment Anything Model 2 Assisting UNet-Like Transformer for Remote Sensing Image Semantic Segmentation

SAM2Former: Segment Anything Model 2 Assisting UNet-Like Transformer for Remote Sensing Image Semantic Segmentation

Remote sensing semantic segmentation plays a crucial role in the fields of land cover classification, disaster monitoring, and urban planning. However, due to the high complexity and category imbalance of remote sensing data sets, traditional image segmentation network tend to be slow to train and d...

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Bibliographic Details
Main Authors: Xuewen Li, Xiaomin Tian, Zihong Wang, Feng Zhang, Yanting Zhang, Na Yang, Chuanzhao Tian
Format: Article
Language:English
Published: IEEE 2025-01-01
Series:IEEE Access
Subjects:
Feature capture
Segment Anything Model 2
semantic segmentation
remote sensing images
transformer
Online Access:https://ieeexplore.ieee.org/document/11052296/
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https://ieeexplore.ieee.org/document/11052296/

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