A Novel Spatial-Spectral Similarity Measure for Dimensionality Reduction and Classification of Hyperspectral Imagery
A Novel Spatial-Spectral Similarity Measure for Dimensionality Reduction and Classification of Hyperspectral Imagery
复制标题
一种用于高光谱图像降维和分类的新型空间光谱相似性度量
DOI：
10.1109/tgrs.2014.2306687
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发表时间：
2014-11-01
影响因子：
8.2
通讯作者：
Jiang, Geng-Ming
中科院分区：
文献类型：
--
作者：
Pu, Hanye;Chen, Zhao;Jiang, Geng-Ming
近年来，降维和分类已成为高光谱图像分析的重要问题。在本文中，我们提出了一种新的空间-光谱相似性度量，它映射了高光谱图像中两个图像块之间的距离。该方法利用空间邻域来包含空间信息，基于图像中观察到的像素在空间上是相关的这一事实，可以从谱域和空间域中提取有意义的特征。首先，新的相似性度量能够有效地利用数据丰富的光谱和空间结构，从而改进了原有的k近邻分类方法。其次，新的相似性度量可以结合到现有的DR方法中，包括线性或非线性技术。结合所提出的相似性度量的优点，改进的DR方法能够有效地处理光谱特征引起的冗余性以及像素之间的空间关系。通过对不同仪器获取的5个真实高光谱数据集的分类实验，对所提出的相似性度量进行了比较研究和分析。实验结果表明，该方法在高光谱数据集的DR和分类中具有很好的融合光谱和空间信息的效果。
In recent years, dimensionality reduction (DR) and classification have become important issues of hyperspectral image analysis. In this paper, we propose a new spatial-spectral similarity measure, which maps the distances between two image patches in hyperspectral images. Including spatial information by using the spatial neighbors, the proposed similarity measure is based on the fact that the observed pixels in the images are spatially related, and the meaningful features can be extracted from both the spectral and spatial domains. First, the new similarity measure can effectively exploit the rich spectral and spatial structures of data, thus improving the original k-nearest neighbor (kNN) classification methods. Second, the new similarity measure can be incorporated into existing DR methods including linear or nonlinear techniques. With the merits of the proposed similarity measure, the modified DR methods become effective in dealing with the redundancy resulting from spectral signature as well as the spatial relation among pixels. A comparative study and analysis based on classification experiments using five real hyperspectral data sets, which were acquired by different instruments, is conducted to evaluate the proposed similarity measure. The experimental results demonstrate that the proposed measure is promising for combining spectral and spatial information when applied to DR and classification of hyperspectral data sets.