Region-Enhancing Network for Semantic Segmentation of Remote-Sensing Imagery

Semantic segmentation for high-resolution remote-sensing imagery (HRRSI) has become increasingly popular in machine vision in recent years. Most of the state-of-the-art methods for semantic segmentation of HRRSI usually emphasize the strong learning ability of deep convolutional neural network to mo...

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Autores principales:	Bo Zhong, Jiang Du, Minghao Liu, Aixia Yang, Junjun Wu
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	semantic segmentation remote sensing imagery (HRRSI) deep convolutional neural network regional integrity of images Chemical technology TP1-1185
Acceso en línea:	https://doaj.org/article/736ce94b04cd48859056be59ab0de43a
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spelling	oai:doaj.org-article:736ce94b04cd48859056be59ab0de43a2021-11-11T19:15:57ZRegion-Enhancing Network for Semantic Segmentation of Remote-Sensing Imagery10.3390/s212173161424-8220https://doaj.org/article/736ce94b04cd48859056be59ab0de43a2021-11-01T00:00:00Zhttps://www.mdpi.com/1424-8220/21/21/7316https://doaj.org/toc/1424-8220Semantic segmentation for high-resolution remote-sensing imagery (HRRSI) has become increasingly popular in machine vision in recent years. Most of the state-of-the-art methods for semantic segmentation of HRRSI usually emphasize the strong learning ability of deep convolutional neural network to model the contextual relationship in the image, which takes too much consideration on every pixel in images and subsequently causes the problem of overlearning. Annotation errors and easily confused features can also lead to the confusion problem while using the pixel-based methods. Therefore, we propose a new semantic segmentation network—the region-enhancing network (RE-Net)—to emphasize the regional information instead of pixels to solve the above problems. RE-Net introduces the regional information into the base network, to enhance the regional integrity of images and thus reduce misclassification. Specifically, the regional context learning procedure (RCLP) can learn the context relationship from the perspective of regions. The region correcting procedure (RCP) uses the pixel aggregation feature to recalibrate the pixel features in each region. In addition, another simple intra-network multi-scale attention module is introduced to select features at different scales by the size of the region. A large number of comparative experiments on four different public datasets demonstrate that the proposed RE-Net performs better than most of the state-of-the-art ones.Bo ZhongJiang DuMinghao LiuAixia YangJunjun WuMDPI AGarticlesemantic segmentationremote sensing imagery (HRRSI)deep convolutional neural networkregional integrity of imagesChemical technologyTP1-1185ENSensors, Vol 21, Iss 7316, p 7316 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	semantic segmentation remote sensing imagery (HRRSI) deep convolutional neural network regional integrity of images Chemical technology TP1-1185
spellingShingle	semantic segmentation remote sensing imagery (HRRSI) deep convolutional neural network regional integrity of images Chemical technology TP1-1185 Bo Zhong Jiang Du Minghao Liu Aixia Yang Junjun Wu Region-Enhancing Network for Semantic Segmentation of Remote-Sensing Imagery
description	Semantic segmentation for high-resolution remote-sensing imagery (HRRSI) has become increasingly popular in machine vision in recent years. Most of the state-of-the-art methods for semantic segmentation of HRRSI usually emphasize the strong learning ability of deep convolutional neural network to model the contextual relationship in the image, which takes too much consideration on every pixel in images and subsequently causes the problem of overlearning. Annotation errors and easily confused features can also lead to the confusion problem while using the pixel-based methods. Therefore, we propose a new semantic segmentation network—the region-enhancing network (RE-Net)—to emphasize the regional information instead of pixels to solve the above problems. RE-Net introduces the regional information into the base network, to enhance the regional integrity of images and thus reduce misclassification. Specifically, the regional context learning procedure (RCLP) can learn the context relationship from the perspective of regions. The region correcting procedure (RCP) uses the pixel aggregation feature to recalibrate the pixel features in each region. In addition, another simple intra-network multi-scale attention module is introduced to select features at different scales by the size of the region. A large number of comparative experiments on four different public datasets demonstrate that the proposed RE-Net performs better than most of the state-of-the-art ones.
format	article
author	Bo Zhong Jiang Du Minghao Liu Aixia Yang Junjun Wu
author_facet	Bo Zhong Jiang Du Minghao Liu Aixia Yang Junjun Wu
author_sort	Bo Zhong
title	Region-Enhancing Network for Semantic Segmentation of Remote-Sensing Imagery
title_short	Region-Enhancing Network for Semantic Segmentation of Remote-Sensing Imagery
title_full	Region-Enhancing Network for Semantic Segmentation of Remote-Sensing Imagery
title_fullStr	Region-Enhancing Network for Semantic Segmentation of Remote-Sensing Imagery
title_full_unstemmed	Region-Enhancing Network for Semantic Segmentation of Remote-Sensing Imagery
title_sort	region-enhancing network for semantic segmentation of remote-sensing imagery
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/736ce94b04cd48859056be59ab0de43a
work_keys_str_mv	AT bozhong regionenhancingnetworkforsemanticsegmentationofremotesensingimagery AT jiangdu regionenhancingnetworkforsemanticsegmentationofremotesensingimagery AT minghaoliu regionenhancingnetworkforsemanticsegmentationofremotesensingimagery AT aixiayang regionenhancingnetworkforsemanticsegmentationofremotesensingimagery AT junjunwu regionenhancingnetworkforsemanticsegmentationofremotesensingimagery
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Region-Enhancing Network for Semantic Segmentation of Remote-Sensing Imagery

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