Local Representation Few-Shot Learning for Hyperspectral Image Cross-Scene Classification

ZHANG Yu-xiang; LI Wei; ZHANG Meng-meng; TAO Ran

doi:10.12263/DZXB.20230937

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Local Representation Few-Shot Learning for Hyperspectral Image Cross-Scene Classification

PAPERS | 更新时间：2025-12-08

- Local Representation Few-Shot Learning for Hyperspectral Image Cross-Scene Classification
- ACTA ELECTRONICA SINICA Vol. 53, Issue 1, Pages: 248-258(2025)
- 作者机构：
  
  北京理工大学信息与电子学院，北京 100081
- 作者简介：
  
  rantao@bit.edu.cn
- 基金信息：
  
  National Key Laboratory of Multispectral Information Intelligent Processing Technology under Grant(6142113220205);BIT Research and Innovation Promoting Project(2023YCXZ008)
- DOI：10.12263/DZXB.20230937
  CLC： TP75;TP79
- Received：07 October 2023，
  
  Revised：2024-01-19，
  
  Published：25 January 2025
- 稿件说明：
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张宇翔, 李伟, 张蒙蒙, 等. 基于局部表征少样本学习的高光谱图像跨场景分类[J]. 电子学报, 2025, 53(01): 248-258.

ZHANG Yu-xiang, LI Wei, ZHANG Meng-meng, et al. Local Representation Few-Shot Learning for Hyperspectral Image Cross-Scene Classification[J]. Acta Electronica Sinica, 2025, 53(01): 248-258.
张宇翔, 李伟, 张蒙蒙, 等. 基于局部表征少样本学习的高光谱图像跨场景分类[J]. 电子学报, 2025, 53(01): 248-258. DOI：10.12263/DZXB.20230937

ZHANG Yu-xiang, LI Wei, ZHANG Meng-meng, et al. Local Representation Few-Shot Learning for Hyperspectral Image Cross-Scene Classification[J]. Acta Electronica Sinica, 2025, 53(01): 248-258. DOI：10.12263/DZXB.20230937

摘要

在跨场景分类任务中，大多数领域自适应方法（Domain Adaptation，DA）关注于源域数据和目标域数据由相同传感器获得且具有相同地物类别的迁移任务，然而当目标数据中存在新类别时自适应性能会显著下降.此外，大多数高光谱图像分类方法采用全局表征机制，即针对固定大小窗口的样本进行表征学习，其地物类别表征能力有限.本文提出了一种基于局部表征的少样本学习框架（Local representation Few Shot Learning，LrFSL），尝试在少样本学习中构建局部表征机制突破全局表征能力上限.在提出框架中，对所有具有标签的源域数据和少量具有标签的目标域数据构建元任务，依照元学习策略同步进行情景训练，与此同时设计了域内局部表征模块（Intra-domain Local Representation block，ILR-block）用于挖掘样本中多个局部表征的语义信息，设计了域间局部对齐模块（Inter-domain Local Alignment block，ILA-block）进行跨域逐类别分布对齐以缓解领域偏移对少样本学习的影响.在三个公开高光谱图像数据集上的实验结果证明了该方法显著优于目前最先进的方法.

Abstract

In cross-scene classification tasks

most domain adaptation (DA) methods typically focus on transfer tasks where the source domain data and the target domain data are obtained using the same sensor and share the same land cover class. However

the adaptive performance is significantly reduced when new classes are present in the target data. Moreover

many hyperspectral image (HSI) classification methods rely on a global representation mechanism

where representation learning is performed on samples with fixed-size windows

limiting their ability to effectively represent ground object classes. A framework called local representation few-shot learning (LrFSL) is proposed

which aims to overcome the limitations of global representation ability by constructing a local representation mechanism in few-shot learning. In this proposed framework

meta-tasks are created for all labeled source domain data and a few labeled target domain data

and scenario training is performed simultaneously using a meta-learning strategy. Additionally

an Intra-domain local representation block (ILR-block) is designed to extract semantic information from multiple local representations within each sample. Furthermore

the inter-domain local alignment block (ILA-block) is designed to align cross-domain class-wise distribution

thereby mitigating the impact of domain shift on few-shot learning. Experimental results on three publicly available HSI datasets demonstrate that the proposed method outperforms state-of-the-art methods by a significant margin.

关键词

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references

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