A manifold learning approach to urban land cover classification with optical and radar data

Urban land covers (ULC) are essential data for numerous studies of urban landscape ecology performed on various scales. Nevertheless, it remains difficult to obtain accurate and timely ULC information. This study presents a methodological framework for fusing optical and synthetic aperture radar (SAR) data at the pixel level with manifolds to improve ULC classification. Three typical manifold learning models, namely, ISOMAP, Local Linear Embedding (LLE) and principle component analysis (PCA), were employed, and their results were compared. SPOT-5 data were used as optical data to be fused with three different SAR datasets. Experimental results showed that 1) the most useful information of the optical and SAR data were included in the manifolds with intrinsic dimensionality, while various ULC classes were distributed differently throughout the feature spaces of manifolds derived from different learning methods; 2) in certain cases, ISOMAP performed comparably to PCA, but PCA generally performed the best out of all the study cases, yielding the best producer's and user's accuracy of all ULC classes and requiring the least amount of time to build the machine learning models; and 3) the LLE-derived manifolds yielded the lowest accuracy, primarily by confusing bare soils with dark impervious surfaces and vegetation. These results indicate the effectiveness of the new manifold technology to fuse optical and SAR data at the pixel level for improving ULC classification, which can be applied in practice to support the accurate analysis of urban landscape.