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

Quanlong Feng,Baoguo Li,Jianyu Yang,Dehai Zhu

doi:10.3390/ijgi8010028

Quanlong Feng, Baoguo Li + Show 2 more

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https://doi.org/10.3390/ijgi8010028

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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.

Highlights

Urban land-use mapping is of great significance for various urban applications, such as urban planning and designing, urban-environment monitoring, and urban-land surveys [1,2]
In order to evaluate the performance of the proposed two-branch neural network for urban land-use mapping, a series of classification maps are depicted in Figure 7 including the following cases: (a) hyperspectral imagery (HSI) branch only, i.e., using only HSI data and the HSI branch for classification; (b) Light Detection and Ranging (LiDAR) branch only, i.e., using only LiDAR data and the LiDAR branch for classification; (c) the proposed two-branch convolutional neural network (CNN)
This paper proposed a modified two-branch convolutional neural network for urban land-use mapping using multisource hyperspectral and LiDAR data

Summary

Introduction

Urban land-use mapping is of great significance for various urban applications, such as urban planning and designing, urban-environment monitoring, and urban-land surveys [1,2]. Traditional methods for urban land-use mapping are based on the visual interpretation of high-resolution optical remote-sensing imagery and field surveys, which can be quite time-consuming and laborious. With the development of remote-sensing technology, some researchers started to use multispectral optical imagery and machine-learning methods to automatically extract urban and-cover and land-use information [3,4,5,6]. Lu et al [3] combined textural and spectral images with the traditional supervised classification method for urban land-cover classification based on multispectral QuickBird remote-sensing data. Tong et al [7] discussed which features of airborne hyperspectral data to use for urban land-cover classification and showed that the synthetic use of shape, texture and spectral information can improve the classification accuracy

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