Integration of heterogeneous data for classification in hyperspectral satellite imagery
Integration of heterogeneous data for classification in hyperspectral satellite imagery
复制标题
整合异构数据以进行高光谱卫星图像分类
DOI：
10.1117/12.919236
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发表时间：
2012
期刊：
影响因子：
--
通讯作者：
David Gillis
中科院分区：
文献类型：
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作者：
John J. Benedetto;Wojtek Czaja;Julia A. Dobrosotskaya;Timothy Doster;K. Duke;David Gillis
随着新的遥感模式的出现，为了目标/异常检测和分类的目的，寻找更适合不同数据类型的融合和集成的算法变得越来越重要。解决这一问题的典型技术是基于在选定的模式中分别执行检测/分类/分割，然后将结果整合到更完整的画面中。在本文中，我们对一种新的方法进行了广泛的分析，该方法基于建立多模式数据的融合表示，然后可以通过最先进的分类器或检测器进行分析。在这种情况下，我们将考虑结合空间信息的高光谱图像。我们的方法涉及基于联合数据依赖图及其关联扩散核的分析的机器学习技术。然后，将得到的融合图拉普拉斯算子的重要特征向量构成新的表示，该表示提供了来自不同输入数据的综合特征。我们将这些融合方法与空间和谱图方法的综合输出分析进行了比较。
As new remote sensing modalities emerge, it becomes increasingly important to nd more suitable algorithms for fusion and integration of dierent data types for the purposes of target/anomaly detection and classication. Typical techniques that deal with this problem are based on performing detection/classication/segmentation separately in chosen modalities, and then integrating the resulting outcomes into a more complete picture. In this paper we provide a broad analysis of a new approach, based on creating fused representations of the multi- modal data, which then can be subjected to analysis by means of the state-of-the-art classiers or detectors. In this scenario we shall consider the hyperspectral imagery combined with spatial information. Our approach involves machine learning techniques based on analysis of joint data-dependent graphs and their associated diusion kernels. Then, the signicant eigenvectors of the derived fused graph Laplace operator form the new representation, which provides integrated features from the heterogeneous input data. We compare these fused approaches with analysis of integrated outputs of spatial and spectral graph methods.