Three-dimensional singular spectrum analysis for precise land cover classification from UAV-borne hyperspectral benchmark datasets

Abstract

The precise classification of land covers with hyperspectral imagery (HSI) is a major research-focused topic in remote sensing, especially using unmanned aerial vehicle (UAV) systems as the abundant data sources have brought severe intra-class spectral variability and high spatial heterogeneity challenges, making precise classification difficult. To this end, a novel three-dimensional singular spectrum analysis (3DSSA) method is proposed for the 3D feature extraction of HSI. It aims to construct a low-rank trajectory tensor containing global and local features and extract both spectral discrimination features and spatial contextual features in conjunction with tensor singular value decomposition (t-SVD). To reduce the risk of tensor operations exceeding memory on large-scale HSI data, the extended regional clustering (RC) 3DSSA framework (RC-3DSSA) is proposed for precise HSI classification. RC-3DSSA uses RC processing to alleviate the scale diversity and further applies 3DSSA to tackle issues of intra-class spectral variability and spatial heterogeneity. In order to effectively evaluate the performance of RC-3DSSA, a new challenging classification dataset namely the Qingdao UAV-borne HSI (QUH) dataset was further built. It consists of three sub-datasets: QUH-Tangdaowan, QUH-Qingyun, and QUH-Pingan, which are freely available as benchmarks for precise land cover classification. The experimental results on QUH and two publicly available datasets show that the RC-3DSSA can accurately distinguish ground objects and reliably map their distribution when benchmarked with ten state-of-the-art methods. Specifically, the overall accuracies achieved are 86.62%, 87.51%, and 87.35% under 10% spatially disjoint training samples for the three UAV-borne HSI datasets, respectively, providing the best performance.