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	融合自适应稀疏表示和相关系数的高光谱伪装分类方法
其他题名	Classification of Camouflages Using Hyperspectral Images Combined With Fusing Adaptive Sparse Representation and Correlation Coefficient
	周冰; 李秉璇; 贺宣; 刘贺雄; 王法臻
来源期刊	光谱学与光谱分析
ISSN	1000-0593
出版年	2021
卷号	41 期号:12 页码:3851-3856
中文摘要	目前比较成熟的高光谱成像手段有卫星遥感和航空成像技术,这两种成像方式侦察时间大致相同,入射光方向基本一致,因而地物的光谱曲线比较固定;在陆基条件下,地物的光谱曲线受成像环境的影响凸显,因此应该对适用于陆基条件下的高光谱图像分类方法进行研究。在陆基高光谱图像中,对每个地物进行类型以及种类的判别有利于后续对目标的识别和处理,不同于传统遥感图像分类,陆基条件下的高光谱图像目标分类训练样本不仅较难获得,并且在陆基条件下的高光谱图像中,训练样本之间的相关性随着目标类型、探测器参数以及成像环境等因素时刻发生变化。基于稀疏性表示的分类方法已经被广泛应用于处理图像问题以及各种机器视觉问题。对于陆基高光谱图像来说,基于固定范数约束的稀疏编码策略无法适应陆基条件下高光谱成像多变的环境,而自适应稀疏表示可以根据样本相关性自适应的调节范数约束,相关系数可以提高图像中的破坏因素(阴影、噪声点等)的识别精度。通过引入正则化参数,融合了自适应稀疏表示和相关系数,提出了一种新的高光谱图像分类方法。为了验证所提方法的有效性,分别在绿色植被背景和荒漠背景中设置伪装物,通过不同的分类方法对图像进行分类,实验结果表明,不管是分类精度还是分类一致性,该方法都有明显的优势,可以应用于陆基条件下的高光谱图像分类,为目标分类提供了理论基础。
英文摘要	In recent years,with the rapid development of military reconnaissance and identification technology,military equipment used for reconnaissance and detection has gradually achieved high-precision levels.The troops with high-tech reconnaissance methods can often perform precise strikes on targets,significantly reducing the cost of victory in war.The more mature hyperspectral imaging methods include satellite remote sensing and high-altitude aerial imaging technologies.The two imaging methods have roughly the same reconnaissance time and the same direction of incident light.Therefore,the spectral curve of the ground object is relatively fixed.However,under land-based conditions,the spectral curve of the ground feature is prominently affected by the imaging environment,so the method of hyperspectral image classification is suitable for land-based conditions should be studied.In land-based hyperspectral images,the identification and classification of each feature are beneficial to the subsequent identification and processing of camouflage targets.Different from traditional remote sensing spectral image classification,the classification of hyperspectral camouflage targets under land-based conditions is not only difficult to obtain training samples,and in hyperspectral images under land-based conditions,the correlation between training samples under land-based conditions,the correlation between training samples varies with the target type.The parameters of the detector and the imaging environment are constantly changing.Classification methods based on sparse representation have been widely used to deal with image problems and various machine vision problems,including hyperspectral image classification.For land-based hyperspectral images,sparse coding strategies based on fixed norm constraints cannot be adapted under land-based conditions, hyperspectral imaging.For land-based hyperspectral images,sparse coding strategies based on fixed norm constraints cannot be adapted.Under land-based conditions,hyperspectral imaging is a changeable environment,and adaptive sparse representation can adaptively adjust norm constraints based on sample correlation.Correlation coefficients can improve the image's recognition accuracy of destructive factors(shadows,noise points,etc.).This paper proposes a new hyperspectral image classification method by introducing regularization parameters,fusing adaptive sparse representation and correlation coefficients.In order to verify the effectiveness of the proposed method,camouflage objects were set in the green vegetation background and the desert background,and different classification methods classified the images.The experimental results show that the method in this paper is obvious,whether it is classification accuracy or classification consistency.The advantages of this can be applied to the classification of hyperspectral images under land-based conditions,providing a theoretical basis for camouflage reconnaissance and identification.
中文关键词	分类 ; 陆基高光谱图像 ; 伪装 ; 自适应稀疏表示 ; 相关系数
英文关键词	Classification Land-based hyperspectral image Camouflage Adaptive sparse representation Correlation coefficient
类型	Article
语种	中文
收录类别	CSCD
WOS类目	Remote Sensing
CSCD记录号	CSCD:7103026
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/377720
作者单位	周冰, 陆军工程大学石家庄校区, 石家庄, 河北 050000, 中国.; 李秉璇, 陆军工程大学石家庄校区, 石家庄, 河北 050000, 中国.; 贺宣, 陆军工程大学石家庄校区, 石家庄, 河北 050000, 中国.; 刘贺雄, 陆军工程大学石家庄校区, 石家庄, 河北 050000, 中国.; 王法臻, 陆军工程大学石家庄校区, 石家庄, 河北 050000, 中国.
推荐引用方式 GB/T 7714	周冰,李秉璇,贺宣,等. 融合自适应稀疏表示和相关系数的高光谱伪装分类方法[J],2021,41(12):3851-3856.
APA	周冰,李秉璇,贺宣,刘贺雄,&王法臻.(2021).融合自适应稀疏表示和相关系数的高光谱伪装分类方法.光谱学与光谱分析,41(12),3851-3856.
MLA	周冰,et al."融合自适应稀疏表示和相关系数的高光谱伪装分类方法".光谱学与光谱分析 41.12(2021):3851-3856.