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Sustainability
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Remote Sensing and Image Processing in Environmental Field
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Remote Sensing and Image Processing in Environmental Field

A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Environmental Sustainability and Applications".

Deadline for manuscript submissions: closed (29 February 2024) | Viewed by 2433

Special Issue Editors

Dr. Ying-Nong Chen

E-Mail Website
Guest Editor
Center for Space and Remote Sensing Research, National Central University, Taoyuan 32001, Taiwan
Interests: hyperspectral; multispectral signal processing; machine learning; deep learning; image processing
Special Issues, Collections and Topics in MDPI journals
Dr. Chi-Hung Chuang

E-Mail Website
Guest Editor
Computer Science and Information Engineering, Chung Yuan Christian University, Taoyuan 32001, Taiwan
Interests: machine learning; deep learning; virtual and augmented reality; image processing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Recently, with the advancement of technology, the applications of remote sensing data in environmental protection and sustainable development are becoming more and more popular. However, big remote sensing data also bring some problems such as how to effectively and efficiently process these remote sensing data. Machine learning and deep learning are the keys to this trend. Therefore, this Special Issue of Sustainability aims to demonstrate state-of-the-art works in employing machine learning, deep learning, and image processing algorithms in remote sensing data for environmental applications.

In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:

  • Hyperspectral, multispectral image processing
  • Machine learning/Deep learning/Image processing for hyperspectral, multispectral data analysis
  • Machine learning/Deep learning/Image processing in environmental analysis
  • Remote Sensing applications in environment/sustainability
  • Data mining and the development of statistical models in remote sensing data
  • Signal processing in remote sensing data analysis
  • Sustainability concepts in remote sensing applications

We look forward to receiving your contributions.

Dr. Ying-Nong Chen
Dr. Chi-Hung Chuang
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sustainability is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • machine learning
  • deep learning
  • image processing
  • hyperspectral image
  • environment
  • sustainability
  • remote sensing

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Published Papers (1 paper)

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Research

21 pages, 7027 KiB  
Article
Detection of Black and Odorous Water in Gaofen-2 Remote Sensing Images Using the Modified DeepLabv3+ Model
by Jianjun Huang, Jindong Xu, Weiqing Yan, Peng Wu and Haihua Xing
Sustainability 2024, 16(1), 92; https://doi.org/10.3390/su16010092 - 21 Dec 2023
Cited by 1 | Viewed by 1571
Abstract
The detection of black and odorous water using remote sensing technology has become an effective method. The high-resolution remote sensing images can extract target features better than low-resolution images. However, the high-resolution images often introduce complex background details and intricate textures, which often [...] Read more.
The detection of black and odorous water using remote sensing technology has become an effective method. The high-resolution remote sensing images can extract target features better than low-resolution images. However, the high-resolution images often introduce complex background details and intricate textures, which often have problems with accurate feature extraction. In this paper, based on remote sensing images acquired by the Gaofen-2 satellite, we proposed a Modified DeepLabv3+ model to detect black and odorous water. To reduce the complexity of the encoder part of the model, Modified Deeplabv3+ incorporates a lightweight MobileNetV2 network. A convolutional attention module was introduced to improve the focus on the features of black and odorous water. Then, a fuzzy block was crafted to reduce the uncertainty of the raw data. Additionally, a new loss function was formulated to solve the problem of category imbalance. A series of experiments were conducted on both remote sensing images for the black and odorous water detection (RSBD) dataset and the water pollution dataset, demonstrating that the Modified DeepLabv3+ model outperforms other commonly used semantic segmentation networks. It effectively captures detailed information and reduces image segmentation errors. In addition, in order to better identify black and odorous water and enrich the spectral information of the image, we have generated derived bands using the black and odorous water index. These derived bands were fused together with the original image to construct the RSBD-II dataset. The experimental results show that adding a black and odorous water feature index can achieve a better detection effect. Full article
(This article belongs to the Special Issue Remote Sensing and Image Processing in Environmental Field)
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