联合中心差异特征和空谱注意力的高光谱图像变化检测方法

周天媛; 刘嘉敏; 郭坦; 付川; 罗甫林

doi:10.11834/jrs.20255012

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联合中心差异特征和空谱注意力的高光谱图像变化检测方法
Joint Central Difference Feature and Spatial Spectral Attention Hyperspectral Image Change Detection Method
2025年页码：1-13
收稿：2025-01-08，

网络出版：2025-10-28，
DOI： 10.11834/jrs.20255012
稿件说明：

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周天媛，刘嘉敏，郭坦，付川，罗甫林.XXXX.联合中心差异特征和空谱注意力的高光谱图像变化检测方法.遥感学报，XX（XX）： 1-13 DOI： 10.11834/jrs.20255012.

Zhou Tian-yuan，Liu Jia-min，Guo Tan，Fu Chuan，Luo Fu-lin. XXXX. Joint Central Difference Feature and Spatial Spectral Attention Hyperspectral Image Change Detection Method. National Remote Sensing Bulletin， XX（XX）：1-13 DOI： 10.11834/jrs.20255012.

摘要

多时相高光谱图像因其丰富的光谱波段和图像细节，在变化检测中具有广泛的应用。基于监督学习的高光谱变化检测算法往往依赖于大量的标记样本，这导致了高昂的标注成本。本文提出了一种联合中心差异特征和空谱注意力的网络（Joint Central Difference Features and Spectral-Spatial Attention Network，JCDS

AN）用于高光谱图像变化检测，该网络可以缓解样本受限下的变化特征波动，利用有限的标记样本学习到具有代表性的变化特征。在JCDS

AN网络中，设计了多尺度空谱注意力块来捕获空间和光谱的多尺度特征，同时提出一种差异特征引导的差分中心像素交换策略，以实现差异特征和两时相特征的高效信息交互。在三个公开数据集上的可视化和定量实验结果表明，所提出的JCDS

AN优于其他先进的

高光谱变化检测方法。

Abstract

Multi-temporal hyperspectral images have a wide range of applications in change detection due to their rich spectral features and image details. Traditional hyperspectral change detection algorithms based on supervised learning often rely on a large number of labeled samples

which requires a large sample annotation cost. In recent years

although research has explored the problem of change detection under limited labeled samples

there are still many aspects that need further exploration. Existing methods often fail to fully tap into the potential of limited labeled samples

and there are also shortcomings in extracting changing features. Therefore

we have developed a new network architecture aimed at more effectively utilizing limited labeled samples and focusing on extracting differential features to enhance information related to changes.In this paper

we propose a joint central difference feature and spatial-spectral attention network (JCDS

AN) for hyperspectral image change detection

which can alleviate the fluctuation of changing features under sample constraints and learn representative changing features using limited labeled samples. In JCDS

a multi-scale spatial-spectral attention block was designed to capture multi-scale spatial and spectral features

and a differential center pixel exchange strategy guided by differential features was proposed to achieve efficient information exchange between differential features and two temporal features.Experimental results on three publicly available hyperspectral image datasets show that the proposed JCDS

AN outperforms the state-of-the-art methods in hyperspectral change detection. When utilizing only 1% of the training samples

the method achieved optimal Kappa and OA of 95.90% and 98.30%

respectively

on the Farmland dataset. Ablation experiments were conducted for each proposed module to demonstrate their effectiveness. This approach is

capable of extracting discriminative deep change semantic information

with both qualitative and quantitative results surpassing those of other advanced networks.

关键词

Keywords

references

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