Fully Convolutional Networks for Semantic Segmentation of Very High Resolution Remotely Sensed Images Combined With DSM

Recently, approaches based on fully convolutional networks (FCN) have achieved state-of-the-art performance in the semantic segmentation of very high resolution (VHR) remotely sensed images. One central issue in this method is the loss of detailed information due to downsampling operations in FCN. T...

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Bibliographic Details
Published in:IEEE geoscience and remote sensing letters 2018-03, Vol.15 (3), p.474-478
Main Authors: Sun, Weiwei, Wang, Ruisheng
Format: Article
Language:English
Subjects:
Artificial neural networks
Benchmark testing
Color
Convolution
Deep layer
deep learning
Digital imaging
Fully convolutional networks (FCN)
High resolution
Image processing
Image resolution
Image segmentation
Remote sensing
Resolution
Semantic segmentation
Semantics
State of the art
very high resolution (VHR)
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Summary:Recently, approaches based on fully convolutional networks (FCN) have achieved state-of-the-art performance in the semantic segmentation of very high resolution (VHR) remotely sensed images. One central issue in this method is the loss of detailed information due to downsampling operations in FCN. To solve this problem, we introduce the maximum fusion strategy that effectively combines semantic information from deep layers and detailed information from shallow layers. Furthermore, this letter develops a powerful backend to enhance the result of FCN by leveraging the digital surface model, which provides height information for VHR images. The proposed semantic segmentation scheme has achieved an overall accuracy of 90.6% on the ISPRS Vaihingen benchmark.
ISSN:1545-598X
1558-0571
DOI:10.1109/LGRS.2018.2795531