Next Article in Journal
Social Media Big Data Mining and Spatio-Temporal Analysis on Public Emotions for Disaster Mitigation
Next Article in Special Issue
Dynamic Monitoring of Forest Land in Fuling District Based on Multi-Source Time Series Remote Sensing Images
Previous Article in Journal
Exploiting the Potential of Integrated Public Building Data: Energy Performance Assessment of the Building Stock in a Case Study in Northern Italy
Previous Article in Special Issue
Fusion of SAR and Multispectral Images Using Random Forest Regression for Change Detection
Article

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
*
Author to whom correspondence should be addressed.
ISPRS Int. J. Geo-Inf. 2019, 8(1), 28; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi8010028
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)
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. View Full-Text
Keywords: convolutional neural networks; multisource data; feature fusion; urban land-use mapping convolutional neural networks; multisource data; feature fusion; urban land-use mapping
Show Figures

Figure 1

MDPI and ACS Style

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. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi8010028

AMA Style

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 International Journal of Geo-Information. 2019; 8(1):28. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi8010028

Chicago/Turabian Style

Feng, Quanlong, Dehai Zhu, Jianyu Yang, and Baoguo Li. 2019. "Multisource Hyperspectral and LiDAR Data Fusion for Urban Land-Use Mapping based on a Modified Two-Branch Convolutional Neural Network" ISPRS International Journal of Geo-Information 8, no. 1: 28. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi8010028

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop