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Article

SDFCNv2: An Improved FCN Framework for Remote Sensing Images Semantic Segmentation

1
State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China
2
School of Geosciences, Yangtze University, Wuhan 430100, China
*
Author to whom correspondence should be addressed.
Academic Editors: Sidike Paheding, Maitiniyazi Maimaitijiang, Zahangir Alom and Matthew Maimaitiyiming
Remote Sens. 2021, 13(23), 4902; https://doi.org/10.3390/rs13234902
Received: 7 November 2021 / Revised: 25 November 2021 / Accepted: 30 November 2021 / Published: 3 December 2021
(This article belongs to the Special Issue Deep Learning for Remote Sensing Image Classification)
Semantic segmentation is a fundamental task in remote sensing image analysis (RSIA). Fully convolutional networks (FCNs) have achieved state-of-the-art performance in the task of semantic segmentation of natural scene images. However, due to distinctive differences between natural scene images and remotely-sensed (RS) images, FCN-based semantic segmentation methods from the field of computer vision cannot achieve promising performances on RS images without modifications. In previous work, we proposed an RS image semantic segmentation framework SDFCNv1, combined with a majority voting postprocessing method. Nevertheless, it still has some drawbacks, such as small receptive field and large number of parameters. In this paper, we propose an improved semantic segmentation framework SDFCNv2 based on SDFCNv1, to conduct optimal semantic segmentation on RS images. We first construct a novel FCN model with hybrid basic convolutional (HBC) blocks and spatial-channel-fusion squeeze-and-excitation (SCFSE) modules, which occupies a larger receptive field and fewer network model parameters. We also put forward a data augmentation method based on spectral-specific stochastic-gamma-transform-based (SSSGT-based) during the model training process to improve generalizability of our model. Besides, we design a mask-weighted voting decision fusion postprocessing algorithm for image segmentation on overlarge RS images. We conducted several comparative experiments on two public datasets and a real surveying and mapping dataset. Extensive experimental results demonstrate that compared with the SDFCNv1 framework, our SDFCNv2 framework can increase the mIoU metric by up to 5.22% while only using about half of parameters. View Full-Text
Keywords: fully convolutional networks (FCNs); convolutional neural networks (CNNs); deep learning; semantic segmentation; remote sensing; SDFCN fully convolutional networks (FCNs); convolutional neural networks (CNNs); deep learning; semantic segmentation; remote sensing; SDFCN
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MDPI and ACS Style

Chen, G.; Tan, X.; Guo, B.; Zhu, K.; Liao, P.; Wang, T.; Wang, Q.; Zhang, X. SDFCNv2: An Improved FCN Framework for Remote Sensing Images Semantic Segmentation. Remote Sens. 2021, 13, 4902. https://doi.org/10.3390/rs13234902

AMA Style

Chen G, Tan X, Guo B, Zhu K, Liao P, Wang T, Wang Q, Zhang X. SDFCNv2: An Improved FCN Framework for Remote Sensing Images Semantic Segmentation. Remote Sensing. 2021; 13(23):4902. https://doi.org/10.3390/rs13234902

Chicago/Turabian Style

Chen, Guanzhou, Xiaoliang Tan, Beibei Guo, Kun Zhu, Puyun Liao, Tong Wang, Qing Wang, and Xiaodong Zhang. 2021. "SDFCNv2: An Improved FCN Framework for Remote Sensing Images Semantic Segmentation" Remote Sensing 13, no. 23: 4902. https://doi.org/10.3390/rs13234902

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