结合区域引导和双注意力机制的高光谱目标检测判别式学习网络

钟佳平; 李云松; 谢卫莹; 雷杰; Paolo Gamba

doi:10.12263/DZXB.20221126

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结合区域引导和双注意力机制的高光谱目标检测判别式学习网络

学术论文 | 更新时间：2025-12-11

- 结合区域引导和双注意力机制的高光谱目标检测判别式学习网络
- Region-Guided and Dual Attention Discriminative Learning Network for Hyperspectral Target Detection
- 电子学报 2024年52卷第5期页码：1716-1729
- 作者机构：
  
  1.西安电子科技大学综合业务网全国重点实验室，陕西西安 710071
  2.帕维亚大学，意大利帕维亚 27100
- 作者简介：
  
  [ "钟佳平女，1996年生，陕西咸阳人.现为西安电子科技大学博士研究生.主要研究方向为高光谱图像处理及深度学习. E-mail: jpzhong@stu.xidian.edu.cn" ]
  [ "李云松男，1974年生，辽宁葫芦岛人.现为西安电子科技大学教授、博士生导师.主要研究方向为图像和视频处理和高性能计算. E-mail: ysli@mail.xidian.edu.cn" ]
  [ "谢卫莹女，1988年生，甘肃白银人.现为西安电子科技大学副教授、博士生导师.主要研究方向为分布式智能解译以及轻量化.中国电子学会会员编号：E190022673M.E-mail: wyxie@xidian.edu.cn" ]
  [ "雷杰男，1981年生，陕西渭南人.现为西安电子科技大学教授、博士生导师.主要研究方向为遥感图像编码、高光谱图像实时处理、嵌入式视觉处理、FPGA高级综合.E-mail: jielei@mail.xidian.edu.cn" ]
- 基金信息：
  
  国家自然科学基金(62121001;U22B2014);中国科协青年人才托举工程(2020QNRC001)
- DOI：10.12263/DZXB.20221126
  中图分类号： TP183;TP751.1
- 收稿：2022-09-14，
  
  修回：2023-04-16，
  
  纸质出版：2024-05-25
- 稿件说明：
移动端阅览
钟佳平, 李云松, 谢卫莹, 等. 结合区域引导和双注意力机制的高光谱目标检测判别式学习网络[J]. 电子学报, 2024, 52(05): 1716-1729.

ZHONG Jia-ping, LI Yun-song, XIE Wei-ying, et al. Region-Guided and Dual Attention Discriminative Learning Network for Hyperspectral Target Detection[J]. Acta Electronica Sinica, 2024, 52(05): 1716-1729.
钟佳平, 李云松, 谢卫莹, 等. 结合区域引导和双注意力机制的高光谱目标检测判别式学习网络[J]. 电子学报, 2024, 52(05): 1716-1729. DOI：10.12263/DZXB.20221126

ZHONG Jia-ping, LI Yun-song, XIE Wei-ying, et al. Region-Guided and Dual Attention Discriminative Learning Network for Hyperspectral Target Detection[J]. Acta Electronica Sinica, 2024, 52(05): 1716-1729. DOI：10.12263/DZXB.20221126

摘要

高光谱图像（HyperSpectral Images，HSIs）具有高光谱分辨率和丰富的光谱信息，其具有的大量窄波段电磁波有利于获取感兴趣目标的理化信息，并根据对应的光谱特征对不同物质进行有效区分，从而完成目标检测任务.然而有限样本、少量先验信息、高维相似背景及不同类别差异小所导致的目标和背景混淆问题使得高光谱目标检测（Hyperspectral Target Detection，HTD）面临挑战.为此，本文提出结合区域引导和双注意力机制的高光谱目标检测判别式学习网络（Region-guided and dual-Attention Discriminative learning Network，RADN），以缓解标记样本少的条件下不同类别相似度高和相同类别差异性大导致的背景和目标不易区分的问题，减少高维冗余特征带来的计算复杂度，同时提升检测精度.本文使用经验性区域引导网络训练，采用光谱约束的无监督聚类方法确定网络输入，选择性地关注高光谱图像中的显著性特征和感兴趣区域.此外，本文在网络中添加双通道注意力机制来辅助复杂背景分布的估计，并在网络中引入不同类别光谱先验损失函数，进一步减少高维复杂背景以及光谱变化对于目标的干扰.实验结果和分析表明，RADN在不同数据集上的性能优于现有先进的算法.

Abstract

Hyperspectral images (HSIs) have high spectral resolution and rich spectral information

which can obtain the physical and chemical information of the target of interest by using a large number of narrow-band waves. HSIs can effectively distinguish different substances by corresponding spectral features

and complete the task of target detection. However

the problem of target and background confusion caused by limited samples

a small amount of prior information

high dimensional similar background

and differences between different classes make hyperspectral target detection (HTD) still face challenges. To this end

we propose a region-guided and dual-attention discriminative learning network (RADN) for HTD to solve the problem of intra-class differences and inter-class similarities under a few samples. It can reduce the computational complexity caused by high-dimensional redundant features and improve detection accuracy. In this paper

we introduce the empirical region-guided network for training. We employ the spectrally constrained unsupervised clustering network to determine the network input. To selectively focus on salient features and regions of interest

we add a dual-channel attention mechanism in the generator and discriminator to assist in the estimation of complex background distributions; We introduce an inter-class spectral prior loss function in the network and further reduce the interference of high-dimensional complex background and spectral changes to the target. Experimental results and analysis show that RADN outperforms existing state-of-the-art algorithms on different datasets.

关键词

Keywords

references

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