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Open AccessArticle

Semantic Labeling of High Resolution Aerial Imagery and LiDAR Data with Fine Segmentation Network

1
Tianjin Key Laboratory of Electronic Materials and Devices, School of Electronics and Information Engineering, Hebei University of Technology, Tianjin 300401, China
2
Key Laboratory of Digital Earth Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China
3
Department of Electrical, Computer and Biomedical Engineering, University of Pavia, 27100 Pavia, Italy
4
College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2018, 10(5), 743; https://doi.org/10.3390/rs10050743
Received: 8 April 2018 / Revised: 2 May 2018 / Accepted: 9 May 2018 / Published: 11 May 2018
(This article belongs to the Special Issue Multisensor Data Fusion in Remote Sensing)
In this paper, a novel convolutional neural network (CNN)-based architecture, named fine segmentation network (FSN), is proposed for semantic segmentation of high resolution aerial images and light detection and ranging (LiDAR) data. The proposed architecture follows the encoder–decoder paradigm and the multi-sensor fusion is accomplished in the feature-level using multi-layer perceptron (MLP). The encoder consists of two parts: the main encoder based on the convolutional layers of Vgg-16 network for color-infrared images and a lightweight branch for LiDAR data. In the decoder stage, to adaptively upscale the coarse outputs from encoder, the Sub-Pixel convolution layers replace the transposed convolutional layers or other common up-sampling layers. Based on this design, the features from different stages and sensors are integrated for a MLP-based high-level learning. In the training phase, transfer learning is employed to infer the features learned from generic dataset to remote sensing data. The proposed FSN is evaluated by using the International Society for Photogrammetry and Remote Sensing (ISPRS) Potsdam and Vaihingen 2D Semantic Labeling datasets. Experimental results demonstrate that the proposed framework can bring considerable improvement to other related networks. View Full-Text
Keywords: high resolution aerial imagery; LiDAR; spectral image; semantic segmentation; deep learning; convolutional neural network (CNN) high resolution aerial imagery; LiDAR; spectral image; semantic segmentation; deep learning; convolutional neural network (CNN)
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MDPI and ACS Style

Pan, X.; Gao, L.; Marinoni, A.; Zhang, B.; Yang, F.; Gamba, P. Semantic Labeling of High Resolution Aerial Imagery and LiDAR Data with Fine Segmentation Network. Remote Sens. 2018, 10, 743. https://doi.org/10.3390/rs10050743

AMA Style

Pan X, Gao L, Marinoni A, Zhang B, Yang F, Gamba P. Semantic Labeling of High Resolution Aerial Imagery and LiDAR Data with Fine Segmentation Network. Remote Sensing. 2018; 10(5):743. https://doi.org/10.3390/rs10050743

Chicago/Turabian Style

Pan, Xuran; Gao, Lianru; Marinoni, Andrea; Zhang, Bing; Yang, Fan; Gamba, Paolo. 2018. "Semantic Labeling of High Resolution Aerial Imagery and LiDAR Data with Fine Segmentation Network" Remote Sens. 10, no. 5: 743. https://doi.org/10.3390/rs10050743

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