Pixel- and feature-level fusion of hyperspectral and lidar data for urban land-use classification

Abstract

The complexity of urban areas makes it difficult for single-source remotely sensed data to meet all urban application requirements. Airborne light detection and ranging (lidar) can provide precise horizontal and vertical point cloud data, while hyperspectral images can provide hundreds of narrow spectral bands which are sensitive to subtle differences in surface materials. The main objectives of this study are to explore: (1) the performance of fused lidar and hyperspectral data for urban land-use classification, especially the contribution of lidar intensity and height information for land-use classification in shadow areas; and (2) the efficiency of combined pixel- and object-based classifiers for urban land-use classification. Support vector machine (SVM), maximum likelihood classification (MLC), and object-based classifiers were used to classify lidar, hyperspectral data and their derived features, such as the normalized digital surface model (nDSM), normalized difference vegetation index (NDVI), and texture measures, into 15 urban land-use classes. Spatial attributes and rules were used to minimize misclassification of the objects showing similar spectral properties, and accuracy assessments were carried out for the classification results. Compared with hyperspectral data alone, hyperspectral–lidar data fusion improved overall accuracy by 6.8% (from 81.7 to 88.5%) when the SVM classifier was used. Meanwhile, compared with SVM alone, the combined SVM and object-based method improved OA by 7.1% (from 87.6 to 94.7%). The results suggest that hyperspectral–lidar data fusion is effective for urban land-use classification, and the proposed combined pixel- and object-based classifiers are very efficient and flexible for the fusion of hyperspectral and lidar data.

Acknowledgements

The first author would like to thank the China Scholarship Council for their support of two years’ study in the Department of Geography, University of North Texas. The authors would like to thank the Hyperspectral Image Analysis group and the NSF-Funded Center for Airborne Laser Mapping (NCALM) at the University of Houston for providing the data sets used in this study, and the IEEE GRSS Data Fusion Technical Committee for organizing the 2013 Data Fusion Contest. Finally, the authors would like to thank three anonymous reviewers for their helpful comments and suggestions.

Additional information

Funding

This research was funded by the National Natural Science Foundation of China’s ABCC Programme [project 41120114001], the National Key Technology R&D Programme [Projects 2012BAC16B01 and 2012BAH27B05], National Basic Research Programme of China (973 Programme) for Global Change [project 2010CB950701], and Natural Science Foundation of China [project 41201357].