Semantic segmentation for remote sensing images based on an AD-HRNet model

Semantic segmentation for remote sensing images faces challenges of unbalanced category weight, rich context causing difficulties of recognition, blurred boundaries of multi-scale objects, and so on. To address these problems, we propose a new model by combining HRNet with attention mechanisms and d...
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Autor*in:	Xue Yang [verfasserIn] Xiang Fan [verfasserIn] Mingjun Peng [verfasserIn] Qingfeng Guan [verfasserIn] Luliang Tang [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Schlagwörter:	semantic segmentation convolutional neural networks dilated convolution attention mechanism remote sensing

Übergeordnetes Werk:	In: International Journal of Digital Earth - Taylor & Francis Group, 2022, 15(2022), 1, Seite 2376-2399
Übergeordnetes Werk:	volume:15 ; year:2022 ; number:1 ; pages:2376-2399

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc Journal toc

DOI / URN:	10.1080/17538947.2022.2159080

Katalog-ID:	DOAJ096629584

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source	In International Journal of Digital Earth 15(2022), 1, Seite 2376-2399 volume:15 year:2022 number:1 pages:2376-2399
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authorswithroles_txt_mv	Xue Yang @@aut@@ Xiang Fan @@aut@@ Mingjun Peng @@aut@@ Qingfeng Guan @@aut@@ Luliang Tang @@aut@@
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callnumber-first	G - Geography, Anthropology, Recreation
author	Xue Yang
spellingShingle	Xue Yang misc GA1-1776 misc semantic segmentation misc convolutional neural networks misc dilated convolution misc attention mechanism misc remote sensing misc Mathematical geography. Cartography Semantic segmentation for remote sensing images based on an AD-HRNet model
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topic_title	GA1-1776 Semantic segmentation for remote sensing images based on an AD-HRNet model semantic segmentation convolutional neural networks dilated convolution attention mechanism remote sensing
topic	misc GA1-1776 misc semantic segmentation misc convolutional neural networks misc dilated convolution misc attention mechanism misc remote sensing misc Mathematical geography. Cartography
topic_unstemmed	misc GA1-1776 misc semantic segmentation misc convolutional neural networks misc dilated convolution misc attention mechanism misc remote sensing misc Mathematical geography. Cartography
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title	Semantic segmentation for remote sensing images based on an AD-HRNet model
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title_full	Semantic segmentation for remote sensing images based on an AD-HRNet model
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title_sort	semantic segmentation for remote sensing images based on an ad-hrnet model
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title_auth	Semantic segmentation for remote sensing images based on an AD-HRNet model
abstract	Semantic segmentation for remote sensing images faces challenges of unbalanced category weight, rich context causing difficulties of recognition, blurred boundaries of multi-scale objects, and so on. To address these problems, we propose a new model by combining HRNet with attention mechanisms and dilated convolution, denoted as: AD-HRNet for the semantic segmentation of remote sensing images. In the framework of AD-HRNet, we obtained the weight value of each category based on an improved weighted cross-entropy function by introducing the median frequency balance method to solve the issue of class weight imbalance. The Shuffle-CBAM module with channel attention and spatial attention in AD-HRNet framework was applied to extract more global context information of images through slightly increasing the amount of computation. To address the problem of blurred boundaries caused by multi-scale object segmentation and edge segmentation, we developed an MDC-DUC module in AD-HRNet framework to capture the context information of multi-scale objects and the edge information of many irregular objects. Taking Postdam, Vaihingen, and SAMA-VTOL datasets as materials, we verified the performance of AD-HRNet by comparing with eight typical semantic segmentation models. Experimental results shown that AD-HRNet increases the mIoUs to 75.59% and 71.58% based on the Postdam and Vaihingen datasets, respectively.
abstractGer	Semantic segmentation for remote sensing images faces challenges of unbalanced category weight, rich context causing difficulties of recognition, blurred boundaries of multi-scale objects, and so on. To address these problems, we propose a new model by combining HRNet with attention mechanisms and dilated convolution, denoted as: AD-HRNet for the semantic segmentation of remote sensing images. In the framework of AD-HRNet, we obtained the weight value of each category based on an improved weighted cross-entropy function by introducing the median frequency balance method to solve the issue of class weight imbalance. The Shuffle-CBAM module with channel attention and spatial attention in AD-HRNet framework was applied to extract more global context information of images through slightly increasing the amount of computation. To address the problem of blurred boundaries caused by multi-scale object segmentation and edge segmentation, we developed an MDC-DUC module in AD-HRNet framework to capture the context information of multi-scale objects and the edge information of many irregular objects. Taking Postdam, Vaihingen, and SAMA-VTOL datasets as materials, we verified the performance of AD-HRNet by comparing with eight typical semantic segmentation models. Experimental results shown that AD-HRNet increases the mIoUs to 75.59% and 71.58% based on the Postdam and Vaihingen datasets, respectively.
abstract_unstemmed	Semantic segmentation for remote sensing images faces challenges of unbalanced category weight, rich context causing difficulties of recognition, blurred boundaries of multi-scale objects, and so on. To address these problems, we propose a new model by combining HRNet with attention mechanisms and dilated convolution, denoted as: AD-HRNet for the semantic segmentation of remote sensing images. In the framework of AD-HRNet, we obtained the weight value of each category based on an improved weighted cross-entropy function by introducing the median frequency balance method to solve the issue of class weight imbalance. The Shuffle-CBAM module with channel attention and spatial attention in AD-HRNet framework was applied to extract more global context information of images through slightly increasing the amount of computation. To address the problem of blurred boundaries caused by multi-scale object segmentation and edge segmentation, we developed an MDC-DUC module in AD-HRNet framework to capture the context information of multi-scale objects and the edge information of many irregular objects. Taking Postdam, Vaihingen, and SAMA-VTOL datasets as materials, we verified the performance of AD-HRNet by comparing with eight typical semantic segmentation models. Experimental results shown that AD-HRNet increases the mIoUs to 75.59% and 71.58% based on the Postdam and Vaihingen datasets, respectively.
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title_short	Semantic segmentation for remote sensing images based on an AD-HRNet model
url	https://doi.org/10.1080/17538947.2022.2159080 https://doaj.org/article/f68005344059495dad7b9783d358f102 http://dx.doi.org/10.1080/17538947.2022.2159080 https://doaj.org/toc/1753-8947 https://doaj.org/toc/1753-8955
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up_date	2024-07-03T21:14:25.528Z
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Taking Postdam, Vaihingen, and SAMA-VTOL datasets as materials, we verified the performance of AD-HRNet by comparing with eight typical semantic segmentation models. Experimental results shown that AD-HRNet increases the mIoUs to 75.59% and 71.58% based on the Postdam and Vaihingen datasets, respectively.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">semantic segmentation</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">convolutional neural networks</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">dilated convolution</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">attention mechanism</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">remote sensing</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Mathematical geography. 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Semantic segmentation for remote sensing images based on an AD-HRNet model

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