Journal
REMOTE SENSING
Volume 13, Issue 6, Pages -Publisher
MDPI
DOI: 10.3390/rs13061143
Keywords
feature fusion; total variation; feature extraction; classification; remote sensing
Categories
Funding
- National Natural Science Foundation of China [61772397, 12005169]
- National Key RAMP
- D Program of China [2016YFE0200400]
- Open Research Fund of Key Laboratory of Digital Earth Science [2019LDE005]
- science and technology innovation team of Shaanxi Province [2019TD-002]
- Special fund for basic scientific research project in the central scientific research institutes (Institute of Grassland Research of CAAS) [1610332020026]
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A novel feature fusion method, RTVSA, for urban area classification is proposed in this paper, combining features derived from HSI and LiDAR data. The method effectively extracts structural correlation, withstands noise well, and improves land cover classification accuracy, as demonstrated in experiments conducted on two urban Houston University datasets.
The fusion of the hyperspectral image (HSI) and the light detecting and ranging (LiDAR) data has a wide range of applications. This paper proposes a novel feature fusion method for urban area classification, namely the relative total variation structure analysis (RTVSA), to combine various features derived from HSI and LiDAR data. In the feature extraction stage, a variety of high-performance methods including the extended multi-attribute profile, Gabor filter, and local binary pattern are used to extract the features of the input data. The relative total variation is then applied to remove useless texture information of the processed data. Finally, nonparametric weighted feature extraction is adopted to reduce the dimensions. Random forest and convolutional neural networks are utilized to evaluate the fusion images. Experiments conducted on two urban Houston University datasets (including Houston 2012 and the training portion of Houston 2017) demonstrate that the proposed method can extract the structural correlation from heterogeneous data, withstand a noise well, and improve the land cover classification accuracy.
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