Multi-Spectro-Temporal Analysis of Hyperspectral Imagery Based on 3-D Spectral Modeling and Multilinear Algebra
Multi-Spectro-Temporal Analysis of Hyperspectral Imagery Based on 3-D Spectral Modeling and Multilinear Algebra
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DOI：
10.1109/tgrs.2012.2200486
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发表时间：
2013-01-01
影响因子：
8.2
通讯作者：
Solaiman, Basel
中科院分区：
文献类型：
--
作者：
Hemissi, Selim;Farah, Imed Riadh;Solaiman, Basel
遥感业界开发新一代传感器的雄心表明，多时相高光谱图像正获得越来越大的重要性。因此，多时相图像分类和变化检测问题在几个研究课题中都有很大的相关性。在本文中，我们提出了一种新的方法来模拟反射率响应的时间变化作为时间周期和波长的函数，将高光谱像素的光谱特征总结为三维网格。这种方法被用于高光谱时间序列分析，其主要贡献如下：时间光谱特征的高级形式，将每个像素处的反射率定义为空间/光谱/时间维度的集合。然后，通过在适当的上下文数据多维特征空间中构建时间数据集，采用了一种创新的处理方案，利用三维表面重建和匹配的理论背景进行数据解释。最后，提出了一种基于多线性代数方法的多时相端元提取和光谱分解的改进方法。在突尼斯南部的一个地区，对Hyperion图像的多时态子集进行了案例研究。该方法对采样点的预测准确率高达89.86%，优于传统的分类器。在模拟多时相图像和各种真实实验场景上获得的良好性能表明了该方法的有效性和泛化能力。
Multitemporal hyperspectral images are gaining an ever-increasing importance revealed by the ambition of the remote sensing community to develop new generation of sensors. Therefore, multitemporal images classification and change detection issues are greatly relevant in several research topics. In this paper, we propose a novel approach for modeling the temporal variation of the reflectance response as a function of time period and wavelength; summarizing the spectral signature of hyperspectral pixels as a 3-D mesh. This approach is adopted for hyperspectral time series analysis leading to the main following contribution: an advanced form of the temporal spectral signature defining the reflectance at each pixel as a congregation of the spatial/spectral/temporal dimensions. Afterward, by formulating the temporal data set in an adequate multidimensional feature space of contextual data, an innovative processing scheme exploiting the theoretical backgrounds of 3-D surface reconstruction and matching is adopted for data interpretation. Finally, an improved method for multitemporal endmember extraction and spectral unmixing based on multilinear algebra methods is introduced. A case study, in a region located in southern Tunisia, is conducted on a multitemporal subset of Hyperion images. Up to 89.86% of sampling sites have been correctly predicted by the proposed approach, outperforming conventional classifiers. The good performances obtained, on simulated multitemporal images and over various real experimental scenarios, illustrate the effectiveness and the generalization capacities of the proposed approach.