This article develops a robust feature fusion approach to enhance the classification performance of very high resolution (VHR) remote sensing images. Specifically, a novel two-stage multiple feature fusion (TsF) approach is proposed, which includes an intragroup and an intergroup feature fusion stages. In the first fusion stage, multiple features are grouped by clustering, where redundant information between different types of features is eliminated within each group. Then, features are pairwisely fused in an intergroup fusion model based on the guided filtering method. Finally, the fused feature set is imported into a classifier to generate the classification map. In this work, the original VHR spectral bands and their attribute profiles are taken as examples as input spectral and spatial features, respectively, in order to test the performance of the proposed TsF approach. Experimental results obtained on two QuickBird datasets covering complex urban scenarios demonstrate the effectiveness of the proposed approach in terms of generation of more discriminative fusion features and enhancing classification performance. More importantly, the fused feature dimensionality is limited at a certain level; thus, the computational cost will not be significantly increased even if multiple features are considered.

中文翻译：

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一种用于 VHR 遥感图像分类的新型特征融合方法

本文开发了一种强大的特征融合方法，以提高超高分辨率 (VHR) 遥感图像的分类性能。具体而言，提出了一种新的两阶段多特征融合（TsF）方法，其中包括组内和组间特征融合阶段。在第一个融合阶段，多个特征通过聚类进行分组，在每个组内消除不同类型特征之间的冗余信息。然后，在基于引导过滤方法的组间融合模型中成对融合特征。最后，融合的特征集被导入到分类器中以生成分类图。在这项工作中，以原始 VHR 光谱带及其属性剖面为例，分别作为输入光谱和空间特征，为了测试所提出的 TsF 方法的性能。在覆盖复杂城市场景的两个 QuickBird 数据集上获得的实验结果证明了所提出的方法在生成更具辨别力的融合特征和提高分类性能方面的有效性。更重要的是，融合的特征维数在一定程度上是有限的；因此，即使考虑多个特征，计算成本也不会显着增加。