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ISPRS Int. J. Geo-Inf. 2019, 8(1), 28; https://doi.org/10.3390/ijgi8010028

Multisource Hyperspectral and LiDAR Data Fusion for Urban Land-Use Mapping based on a Modified Two-Branch Convolutional Neural Network

1
College of Resources and Environmental Sciences, China Agricultural University, Beijing 100094, China
2
College of Land Science and Technology, China Agricultural University, Beijing 100083, China
*
Authors to whom correspondence should be addressed.
Received: 4 November 2018 / Revised: 4 January 2019 / Accepted: 9 January 2019 / Published: 14 January 2019
(This article belongs to the Special Issue Multi-Source Geoinformation Fusion)
PDF [814 KB, uploaded 14 January 2019]

Abstract

Accurate urban land-use mapping is a challenging task in the remote-sensing field. With the availability of diverse remote sensors, synthetic use and integration of multisource data provides an opportunity for improving urban land-use classification accuracy. Neural networks for Deep Learning have achieved very promising results in computer-vision tasks, such as image classification and object detection. However, the problem of designing an effective deep-learning model for the fusion of multisource remote-sensing data still remains. To tackle this issue, this paper proposes a modified two-branch convolutional neural network for the adaptive fusion of hyperspectral imagery (HSI) and Light Detection and Ranging (LiDAR) data. Specifically, the proposed model consists of a HSI branch and a LiDAR branch, sharing the same network structure to reduce the time cost of network design. A residual block is utilized in each branch to extract hierarchical, parallel, and multiscale features. An adaptive-feature fusion module is proposed to integrate HSI and LiDAR features in a more reasonable and natural way (based on "Squeeze-and-Excitation Networks"). Experiments indicate that the proposed two-branch network shows good performance, with an overall accuracy of almost 92%. Compared with single-source data, the introduction of multisource data improves accuracy by at least 8%. The adaptive fusion model can also increase classification accuracy by more than 3% when compared with the feature-stacking method (simple concatenation). The results demonstrate that the proposed network can effectively extract and fuse features for a better urban land-use mapping accuracy.
Keywords: convolutional neural networks; multisource data; feature fusion; urban land-use mapping convolutional neural networks; multisource data; feature fusion; urban land-use mapping
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Feng, Q.; Zhu, D.; Yang, J.; Li, B. Multisource Hyperspectral and LiDAR Data Fusion for Urban Land-Use Mapping based on a Modified Two-Branch Convolutional Neural Network. ISPRS Int. J. Geo-Inf. 2019, 8, 28.

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