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Open AccessFeature PaperArticle

Hyperspectral and LiDAR Fusion Using Deep Three-Stream Convolutional Neural Networks

1
Signal Processing in Earth Observation (SiPEO), Technical University of Munich (TUM), Arcisstr. 21, 80333 Munich, Germany
2
Institute for Photogrammetry (ifp), University of Stuttgart, 70174 Stuttgart, Germany
3
GIScience Research Group, Institute of Geography, Heidelberg University, 69120 Heidelberg, Germany
4
Helmholtz-Zentrum Dresden-Rossendorf, Helmholtz Institute Freiberg for Resource Technology, Exploration, Chemnitzer Str. 40, D-09599 Freiberg, Germany
5
Remote Sensing Technology Institute (IMF), German Aerospace Center (DLR), Oberpfaffenhofen, 82234 Wessling, Germany
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Remote Sens. 2018, 10(10), 1649; https://doi.org/10.3390/rs10101649
Received: 5 September 2018 / Revised: 8 October 2018 / Accepted: 15 October 2018 / Published: 16 October 2018
Recently, convolutional neural networks (CNN) have been intensively investigated for the classification of remote sensing data by extracting invariant and abstract features suitable for classification. In this paper, a novel framework is proposed for the fusion of hyperspectral images and LiDAR-derived elevation data based on CNN and composite kernels. First, extinction profiles are applied to both data sources in order to extract spatial and elevation features from hyperspectral and LiDAR-derived data, respectively. Second, a three-stream CNN is designed to extract informative spectral, spatial, and elevation features individually from both available sources. The combination of extinction profiles and CNN features enables us to jointly benefit from low-level and high-level features to improve classification performance. To fuse the heterogeneous spectral, spatial, and elevation features extracted by CNN, instead of a simple stacking strategy, a multi-sensor composite kernels (MCK) scheme is designed. This scheme helps us to achieve higher spectral, spatial, and elevation separability of the extracted features and effectively perform multi-sensor data fusion in kernel space. In this context, a support vector machine and extreme learning machine with their composite kernels version are employed to produce the final classification result. The proposed framework is carried out on two widely used data sets with different characteristics: an urban data set captured over Houston, USA, and a rural data set captured over Trento, Italy. The proposed framework yields the highest OA of 92 . 57 % and 97 . 91 % for Houston and Trento data sets. Experimental results confirm that the proposed fusion framework can produce competitive results in both urban and rural areas in terms of classification accuracy, and significantly mitigate the salt and pepper noise in classification maps. View Full-Text
Keywords: data fusion; extinction profiles (EPs); composite kernels; convolutional neural networks (CNN); feature extraction (FE) data fusion; extinction profiles (EPs); composite kernels; convolutional neural networks (CNN); feature extraction (FE)
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MDPI and ACS Style

Li, H.; Ghamisi, P.; Soergel, U.; Zhu, X.X. Hyperspectral and LiDAR Fusion Using Deep Three-Stream Convolutional Neural Networks. Remote Sens. 2018, 10, 1649.

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