Classification of Hyperspectral and LiDAR Data Using Coupled CNNs

• URL: http://arxiv.org/abs/2002.01144v1
• Date: Tue, 4 Feb 2020 06:23:36 GMT
• Title: Classification of Hyperspectral and LiDAR Data Using Coupled CNNs
• Authors: Renlong Hang, Zhu Li, Pedram Ghamisi, Danfeng Hong, Guiyu Xia, and Qingshan Liu
• Abstract summary: We propose an efficient framework to fuse hyperspectral and Light Detection And Ranging (LiDAR) data using two coupled convolutional neural networks (CNNs) One CNN is designed to learn spectral-spatial features from hyperspectral data, the other is used to capture the elevation information from LiDAR data. In the fusion phase, feature-level and decision-level fusion methods are simultaneously used to integrate these heterogeneous features.
• Score: 39.55503477017984
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper, we propose an efficient and effective framework to fuse hyperspectral and Light Detection And Ranging (LiDAR) data using two coupled convolutional neural networks (CNNs). One CNN is designed to learn spectral-spatial features from hyperspectral data, and the other one is used to capture the elevation information from LiDAR data. Both of them consist of three convolutional layers, and the last two convolutional layers are coupled together via a parameter sharing strategy. In the fusion phase, feature-level and decision-level fusion methods are simultaneously used to integrate these heterogeneous features sufficiently. For the feature-level fusion, three different fusion strategies are evaluated, including the concatenation strategy, the maximization strategy, and the summation strategy. For the decision-level fusion, a weighted summation strategy is adopted, where the weights are determined by the classification accuracy of each output. The proposed model is evaluated on an urban data set acquired over Houston, USA, and a rural one captured over Trento, Italy. On the Houston data, our model can achieve a new record overall accuracy of 96.03%. On the Trento data, it achieves an overall accuracy of 99.12%. These results sufficiently certify the effectiveness of our proposed model.

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