Journal
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
Volume 15, Issue 3, Pages 474-478Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/LGRS.2018.2795531
Keywords
Fully convolutional networks (FCN); deep learning; semantic segmentation; remote sensing; very high resolution (VHR)
Categories
Funding
- Natural Sciences and Engineering Research Council of Canada
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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.
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