Multisource remote sensing data contain complementary information on land covers, but fusing them is a challenging problem due to the heterogeneous nature of the data. This article aims to extract and integrate information from hyperspectral image (HSI) and light detection and ranging (LiDAR) data for land cover classification. As there is a scarcity of a large number of training samples for remotely sensed hyperspectral and LiDAR data, in this article, we propose a model, which is able to perform impressively using a limited number of training samples by extracting effective features representing different characteristics of objects of interest from these two complementary data sources (HSI and LiDAR). A novel feature extraction method named inverse coefficient of variation (ICV) is introduced for HSI, which considers the Gaussian probability of neighborhood between every pair of bands. We, then, propose a two-stream feature fusion approach to integrate the ICV feature with several features extracted from HSI and LiDAR data. We incorporate a fusion unit named canonical correlation analysis as a basic unit for fusing two different sets of features within each stream. We also incorporate the concept of ensemble classification where the features produced by two-stream fusion are distributed into subsets and transformed to improve the feature quality. We compare our method with the existing state-of-the-art methods, which are based on deep learning or handcrafted feature extraction or using both of them. Experimental results show that our proposed approach performs better than other existing methods with a limited number of training samples.

中文翻译：

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高光谱和激光雷达数据分类的反变异系数特征和多级融合技术


多源遥感数据包含有关土地覆盖的补充信息，但由于数据的异构性，融合它们是一个具有挑战性的问题。本文旨在从高光谱图像 (HSI) 和光探测和测距 (LiDAR) 数据中提取和集成信息，以进行土地覆盖分类。由于遥感高光谱和激光雷达数据缺乏大量训练样本，在本文中，我们提出了一种模型，该模型能够通过提取代表不同特征的有效特征，使用有限数量的训练样本来表现出色。来自这两个互补数据源（HSI 和 LiDAR）的感兴趣对象。 HSI 引入了一种新的特征提取方法，称为逆变异系数（ICV），该方法考虑了每对频带之间邻域的高斯概率。然后，我们提出了一种双流特征融合方法，将 ICV 特征与从 HSI 和 LiDAR 数据中提取的多个特征相集成。我们将一个名为典型相关分析的融合单元作为融合每个流中两组不同特征的基本单元。我们还结合了集成分类的概念，其中将两流融合产生的特征分布到子集中并进行转换以提高特征质量。我们将我们的方法与现有的最先进的方法进行比较，这些方法基于深度学习或手工特征提取或同时使用两者。实验结果表明，我们提出的方法在训练样本数量有限的情况下比其他现有方法表现更好。