Enhancing Remote Sensing Semantic Segmentation Accuracy and Efficiency Through Transformer and Knowledge Distillation

Enhancing Remote Sensing Semantic Segmentation Accuracy and Efficiency Through Transformer and Knowledge Distillation

In semantic segmentation tasks, the transition from convolutional neural networks (CNNs) to transformers is driven by the latter's superior ability to capture global semantic information in remote sensing images. However, most transformer methods face challenges such as slow inference spe...

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Bibliographic Details
Main Authors: Kang Zheng, Yu Chen, Jingrong Wang, Zhifei Liu, Shuai Bao, Jiao Zhan, Nan Shen
Format: Article
Language:English
Published: IEEE 2025-01-01
Series:IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Subjects:
Convolutional neural network (CNN)
remote sensing
semantic segmentation
transformer
Online Access:https://ieeexplore.ieee.org/document/10839278/
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