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Title
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Fusion of hyperspectral and lidar images using non-subsampled shearlet transform
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Author
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Abstract
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| In this paper, a new fusion method for merging the spectral and spatial contents of hyperspectral images (HSI) with the height information of light detection and ranging (LiDAR) for increasing the classification accuracy of HSI is introduced. First, 2D non-subsampled shearlet transform (NSST) is applied to each band of hyperspectral and LiDAR data separately in order to extract the spatial features. Second, principal component analysis (PCA) is applied to all shearlet sub bands of HSI in order to reduce their dimension. Third, the spectral information of HSI and obtained spatial features are integrated and classified using subspace multinomial logistic regression (MLRsub). We evaluate the performance of the proposed method over University of Houston, USA and a rural one captured over Trento, Italy. The obtained results show that the proposed method can efficiently classify the joint hyperspectral and LiDAR images. |
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Language
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English
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Source (journal)
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IEEE International Geoscience and Remote Sensing Symposium proceedings. - [New York]
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IEEE International Geoscience and Remote Sensing Symposium
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Source (book)
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38th IEEE International Geoscience and Remote Sensing Symposium (IGARSS), JUL 22-27, 2018, Valencia, SPAIN
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Publication
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New york
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Ieee
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2018
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ISBN
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978-1-5386-7150-4
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978-1-5386-7150-4
978-1-5386-7149-8
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DOI
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10.1109/IGARSS.2018.8519547
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Volume/pages
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(2018)
, p. 8873-8876
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ISI
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000451039808113
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Full text (Publisher's DOI)
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Full text (open access)
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