Target Detection in Hyperspectral Imagery Based on Linear Mixing Model Reconstructed from Measurements

HE Lin; PAN Quan; ZHAO Yong-qiang

您当前的位置：

首页 >

文章列表页 >

Target Detection in Hyperspectral Imagery Based on Linear Mixing Model Reconstructed from Measurements

更新时间：2025-07-16

- Target Detection in Hyperspectral Imagery Based on Linear Mixing Model Reconstructed from Measurements
- Acta Electronica Sinica Vol. 35, Issue 1, Pages: 23-27(2007)
- 作者机构：
  
  西北工业大学自动化学院,陕西,西安,710072
- 作者简介：
- 基金信息：
- DOI：
  CLC： TP391.4
- Published：2007
- 稿件说明：
移动端阅览
HE Lin, PAN Quan, ZHAO Yong-qiang. Target Detection in Hyperspectral Imagery Based on Linear Mixing Model Reconstructed from Measurements[J]. Acta Electronica Sinica, 2007, 35(1): 23-27.
DOI：

HE Lin, PAN Quan, ZHAO Yong-qiang. Target Detection in Hyperspectral Imagery Based on Linear Mixing Model Reconstructed from Measurements[J]. Acta Electronica Sinica, 2007, 35(1): 23-27. DOI：

摘要

针对高光谱图像中无背景和目标先验信息情况下的目标检测问题

给出了一种基于量测重构光谱混合模型的目标检测算法.通过构造投影算子削弱背景干扰

提高自动搜索目标光谱特征的准确性;对光谱空间进行估计后

构造量测重构光谱混合模型;以此量测重构混合光谱模型为基础

使用投影抑制背景并提高信噪比以改善检测效果.同时给出了目标信号与局部杂乱背景之间的均方根误差SLCR及目标信号峰值与局部杂乱背景均值的比例PSLCMR两个检测评价指标的定义.利用可见光/近红外波段高光谱图像进行了实验

实验结果和理论分析表明了算法的有效性.

Abstract

There presents a detection algorithm based on spectral mixing model reconstructed from measurement in this paper in order to detect unknown targets in unknown environment.Firstly

we project the hyperapectral imagery to suppress the background interference in order to search target spectral more accurately.Then

we estimate the spectral subspace and construct a spectral mixing model reconstructed from measurements.And based on the proposed spectral mixing modeling

we project the hyperspectral imagery

which suppress spectral signatures of background and improve the SNR

in order to increase the detection power.Finally

the Signal to Local Clutter RMSE (SLCR) and Peak Signal to Local Clutter Mean Ratio(PSLCMR)

which is proposed

are used to evaluate the detection.Theoretic analysis and the results of experiment on visible/near-infrared hyperspectral imagery verify the effectiveness of the algorithm.

关键词

Keywords

references

Views

1157

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Collaborative Representation-Based Binary Hypothesis Model for Hyperspectral Target Detection

Sparse Representation Based Band Selection for Hyperspectral Imagery Target Detection

Cross-Modal SAR Target Detection via Progressive Knowledge Transfer

Anchor-Free Transformer Algorithm for Aerial Remote Sensing Target Detection

Related Author

HE Lin

PAN Quan

DI Wei

LI Yuan-qing

LING Qiang

HUANG Shu-cai

WEI Dao-zhi

WU Xiao

Related Institution

College of Automation Science and Engineering,South China University of Technology

School of Civil Engineering,Purdue University,West Lafayette

College of Automation Science and EngineeringSouth China University of TechnologyGuangzhouGuangdong 510641China

School of Civil EngineeringPurdue UniversityWest Lafayette 47907USA

College of AutomationNorthwestern Polytechnical UniversityXi’anShaanxi 710072China

⁰