A new spectral matching algorithm is proposed by us- ing nonsubsampled contourlet transform and scale-invariant fea- ture transform. The nonsubsampled contourlet transform is used to decompose an image into a low freq...A new spectral matching algorithm is proposed by us- ing nonsubsampled contourlet transform and scale-invariant fea- ture transform. The nonsubsampled contourlet transform is used to decompose an image into a low frequency image and several high frequency images, and the scale-invariant feature transform is employed to extract feature points from the low frequency im- age. A proximity matrix is constructed for the feature points of two related images. By singular value decomposition of the proximity matrix, a matching matrix （or matching result） reflecting the match- ing degree among feature points is obtained. Experimental results indicate that the proposed algorithm can reduce time complexity and possess a higher accuracy.展开更多
针对高光谱图像分类任务中小样本引起分类精度不高的问题,提出了一种基于动态图-谱特征提取的高光谱分类方法,提高全局建模和局部信息提取能力,实现跨域空间特征和光谱相似性特征的互补融合。首先,提出动态轴滑动建图策略,建立高效、有...针对高光谱图像分类任务中小样本引起分类精度不高的问题,提出了一种基于动态图-谱特征提取的高光谱分类方法,提高全局建模和局部信息提取能力,实现跨域空间特征和光谱相似性特征的互补融合。首先,提出动态轴滑动建图策略,建立高效、有代表性的图结构。其次,基于动态图结构设计动态图特征提取网络,采用特征卷积层、动态空间卷积模块和动态图卷积模块以捕捉局部特征并整合不同尺度的跨域空间特征。然后,区域-全局光谱特征网络通过多层光谱特征卷积模块,融合局部信息并跨层融合编码器,深入挖掘局部和全局光谱特征的序列属性。最后,交叉注意力建立动态关联以融合空间和光谱信息,完成分类。实验结果表明,该方法在Indian Pines、University of Pavia和Salinas三个高光谱数据集上取得了优于现有方法的分类性能,为处理高光谱图像复杂空间和光谱信息提供了一种有效的深度学习框架。展开更多
基金supported by the National Natural Science Foundation of China (6117212711071002)+1 种基金the Specialized Research Fund for the Doctoral Program of Higher Education (20113401110006)the Innovative Research Team of 211 Project in Anhui University (KJTD007A)
文摘A new spectral matching algorithm is proposed by us- ing nonsubsampled contourlet transform and scale-invariant fea- ture transform. The nonsubsampled contourlet transform is used to decompose an image into a low frequency image and several high frequency images, and the scale-invariant feature transform is employed to extract feature points from the low frequency im- age. A proximity matrix is constructed for the feature points of two related images. By singular value decomposition of the proximity matrix, a matching matrix （or matching result） reflecting the match- ing degree among feature points is obtained. Experimental results indicate that the proposed algorithm can reduce time complexity and possess a higher accuracy.
文摘针对高光谱图像分类任务中小样本引起分类精度不高的问题,提出了一种基于动态图-谱特征提取的高光谱分类方法,提高全局建模和局部信息提取能力,实现跨域空间特征和光谱相似性特征的互补融合。首先,提出动态轴滑动建图策略,建立高效、有代表性的图结构。其次,基于动态图结构设计动态图特征提取网络,采用特征卷积层、动态空间卷积模块和动态图卷积模块以捕捉局部特征并整合不同尺度的跨域空间特征。然后,区域-全局光谱特征网络通过多层光谱特征卷积模块,融合局部信息并跨层融合编码器,深入挖掘局部和全局光谱特征的序列属性。最后,交叉注意力建立动态关联以融合空间和光谱信息,完成分类。实验结果表明,该方法在Indian Pines、University of Pavia和Salinas三个高光谱数据集上取得了优于现有方法的分类性能,为处理高光谱图像复杂空间和光谱信息提供了一种有效的深度学习框架。
