高光谱异常检测作为一种不依赖目标先验知识的无监督技术,存在诸如异常与背景难以区分、异常目标的检测精度易受背景污染等难题。为了解决这些问题,提出了一种联合Gabor滤波(Gabor filter,GF)的改进低秩稀疏矩阵异常检测算法。将Gabor...高光谱异常检测作为一种不依赖目标先验知识的无监督技术,存在诸如异常与背景难以区分、异常目标的检测精度易受背景污染等难题。为了解决这些问题,提出了一种联合Gabor滤波(Gabor filter,GF)的改进低秩稀疏矩阵异常检测算法。将Gabor滤波器引入低秩和稀疏矩阵分解(Low-rank and sparse matrix decomposition,LRaSMD)中,用来提取高光谱图像数据的空间纹理特征。对LRaSMD进行改进,选取Gabor变换后的初始结果构建子集,并对这个子集进行约束,通过固定子集的n个最小值并减去背景均值向量,最大程度地保留样本的异常值信息。采用马氏距离对构建的背景协方差矩阵进行检测。所提出的算法在5个数据集上的AUC值(Area under the curve)分别为0.9934、0.9967、0.9995、0.9991和0.9964。实验结果证明,所提出的算法极大地减弱了背景对异常像元的污染,并且可以很好地将背景和异常区分开,具有非常好的异常目标检测性能。展开更多
Sparse representation has been highly successful in various tasks related to image processing and computer vision.For ancient mural image inpainting,traditional group sparse representation models usually lead to struc...Sparse representation has been highly successful in various tasks related to image processing and computer vision.For ancient mural image inpainting,traditional group sparse representation models usually lead to structure blur and line discontinuity due to the construction of similarity group solely based on the Euclidean distance and the randomness of dictionary initialization.To address the aforementioned issues,an improved curvature Gabor transform and group sparse representation(CGabor-GSR)model for ancient Dunhuang mural inpainting is proposed.To begin with,mutual information is introduced to weight the Euclidean distance,and then the weighted Euclidean distance acts as a new standard of similarity group.Subsequently,to mitigate the randomness of dictionary initialization,a curvature Gabor wavelet transform is proposed to extract the features and initialize the feature dictionary with dimension reduction based on principal component analysis(PCA).Ultimately,singular value decomposition(SVD)and split Bregman iteration(SBI)can be used to resolve the CGabor-GSR model to reconstruct the mural images.Experimental results on Dunhuang mural inpainting demonstrate tha the proposed CGabor-GSR achieves a better performance than compared algorithms in both objective and visual evaluation.展开更多
文摘高光谱异常检测作为一种不依赖目标先验知识的无监督技术,存在诸如异常与背景难以区分、异常目标的检测精度易受背景污染等难题。为了解决这些问题,提出了一种联合Gabor滤波(Gabor filter,GF)的改进低秩稀疏矩阵异常检测算法。将Gabor滤波器引入低秩和稀疏矩阵分解(Low-rank and sparse matrix decomposition,LRaSMD)中,用来提取高光谱图像数据的空间纹理特征。对LRaSMD进行改进,选取Gabor变换后的初始结果构建子集,并对这个子集进行约束,通过固定子集的n个最小值并减去背景均值向量,最大程度地保留样本的异常值信息。采用马氏距离对构建的背景协方差矩阵进行检测。所提出的算法在5个数据集上的AUC值(Area under the curve)分别为0.9934、0.9967、0.9995、0.9991和0.9964。实验结果证明,所提出的算法极大地减弱了背景对异常像元的污染,并且可以很好地将背景和异常区分开,具有非常好的异常目标检测性能。
基金supported by National Natural Science Foundation of China(No.61963023)Humanities and Social Sciences Youth Foundation of Ministry of Education(No.19YJC760012)Lanzhou Jiaotong University Basic Top-Notch Personnel Project(No.2022JC36).
文摘Sparse representation has been highly successful in various tasks related to image processing and computer vision.For ancient mural image inpainting,traditional group sparse representation models usually lead to structure blur and line discontinuity due to the construction of similarity group solely based on the Euclidean distance and the randomness of dictionary initialization.To address the aforementioned issues,an improved curvature Gabor transform and group sparse representation(CGabor-GSR)model for ancient Dunhuang mural inpainting is proposed.To begin with,mutual information is introduced to weight the Euclidean distance,and then the weighted Euclidean distance acts as a new standard of similarity group.Subsequently,to mitigate the randomness of dictionary initialization,a curvature Gabor wavelet transform is proposed to extract the features and initialize the feature dictionary with dimension reduction based on principal component analysis(PCA).Ultimately,singular value decomposition(SVD)and split Bregman iteration(SBI)can be used to resolve the CGabor-GSR model to reconstruct the mural images.Experimental results on Dunhuang mural inpainting demonstrate tha the proposed CGabor-GSR achieves a better performance than compared algorithms in both objective and visual evaluation.
