Black and Odorous Water Detection of Remote Sensing Images Based on Improved Deep Learning

Black and odorous water seriously affects the ecological balance of rivers and the health of people living nearby. Satellite remote sensing technology with its advantages of a large range, long-time series, low cost, and high efficiency, has provided a new area for water quality detection. Much arch...

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Main Authors: Jianjun Huang, Jindong Xu, Qianpeng Chong, Ziyi Li
Format: Article
Language:English
Published: Taylor & Francis Group 2023-01-01
Series:Canadian Journal of Remote Sensing
Online Access:http://dx.doi.org/10.1080/07038992.2023.2237591
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author Jianjun Huang
Jindong Xu
Qianpeng Chong
Ziyi Li
author_facet Jianjun Huang
Jindong Xu
Qianpeng Chong
Ziyi Li
author_sort Jianjun Huang
collection DOAJ
description Black and odorous water seriously affects the ecological balance of rivers and the health of people living nearby. Satellite remote sensing technology with its advantages of a large range, long-time series, low cost, and high efficiency, has provided a new area for water quality detection. Much archived remote sensing satellite data can be further processed and used as a data source for black and odorous water detection. In this paper, Gaofen-2 remote sensing data with a spatial resolution of 1 m is leveraged as the data source. To enrich the data source in the northern coastal zone of China, we have built a high-quality remote sensing dataset, called the remote sensing images for black and odorous water detection (RSBD) dataset, which is collected from the Gaofen-2 satellite in Yantai, China. In addition, we propose a network with an encoder-decoder discriminant structure for black and odorous water detection. In the network, an augmented attention module is designed to capture a more comprehensive semantic feature representation. Further, the median balancing loss function is adopted to solve the imbalance issues. Experimental results demonstrate that the network is superior to other state-of-the-art semantic segmentation methods on our dataset.
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spelling doaj.art-3b9b6bfd3c4a478bb31d87fbedf724102024-01-04T15:59:06ZengTaylor & Francis GroupCanadian Journal of Remote Sensing1712-79712023-01-0149110.1080/07038992.2023.22375912237591Black and Odorous Water Detection of Remote Sensing Images Based on Improved Deep LearningJianjun Huang0Jindong Xu1Qianpeng Chong2Ziyi Li3School of Computer and Control Engineering, YanTai UniversitySchool of Computer and Control Engineering, YanTai UniversitySchool of Computer and Control Engineering, YanTai UniversitySchool of Computer and Control Engineering, YanTai UniversityBlack and odorous water seriously affects the ecological balance of rivers and the health of people living nearby. Satellite remote sensing technology with its advantages of a large range, long-time series, low cost, and high efficiency, has provided a new area for water quality detection. Much archived remote sensing satellite data can be further processed and used as a data source for black and odorous water detection. In this paper, Gaofen-2 remote sensing data with a spatial resolution of 1 m is leveraged as the data source. To enrich the data source in the northern coastal zone of China, we have built a high-quality remote sensing dataset, called the remote sensing images for black and odorous water detection (RSBD) dataset, which is collected from the Gaofen-2 satellite in Yantai, China. In addition, we propose a network with an encoder-decoder discriminant structure for black and odorous water detection. In the network, an augmented attention module is designed to capture a more comprehensive semantic feature representation. Further, the median balancing loss function is adopted to solve the imbalance issues. Experimental results demonstrate that the network is superior to other state-of-the-art semantic segmentation methods on our dataset.http://dx.doi.org/10.1080/07038992.2023.2237591
spellingShingle Jianjun Huang
Jindong Xu
Qianpeng Chong
Ziyi Li
Black and Odorous Water Detection of Remote Sensing Images Based on Improved Deep Learning
Canadian Journal of Remote Sensing
title Black and Odorous Water Detection of Remote Sensing Images Based on Improved Deep Learning
title_full Black and Odorous Water Detection of Remote Sensing Images Based on Improved Deep Learning
title_fullStr Black and Odorous Water Detection of Remote Sensing Images Based on Improved Deep Learning
title_full_unstemmed Black and Odorous Water Detection of Remote Sensing Images Based on Improved Deep Learning
title_short Black and Odorous Water Detection of Remote Sensing Images Based on Improved Deep Learning
title_sort black and odorous water detection of remote sensing images based on improved deep learning
url http://dx.doi.org/10.1080/07038992.2023.2237591
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AT qianpengchong blackandodorouswaterdetectionofremotesensingimagesbasedonimproveddeeplearning
AT ziyili blackandodorouswaterdetectionofremotesensingimagesbasedonimproveddeeplearning