Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
5.3
2.5
Journals
Applied Sciences
Special Issues
Remote Sensing Image Processing and Application, 2nd Edition
applsci-logo
Submit to Special Issue Submit Abstract to Special Issue Review for Applied Sciences Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

Remote Sensing Image Processing and Application, 2nd Edition

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Earth Sciences".

Deadline for manuscript submissions: 31 May 2025 | Viewed by 2116

Special Issue Editors

Prof. Dr. Weitao Chen

E-Mail Website
Guest Editor
School of Computer Science, China University of Geosciences, Wuhan 430074, China
Interests: satellite image analysis; satellite image processing; earth observation; geology; remote sensing; classification; feature selection; mapping; geospatial science; deep learning
Special Issues, Collections and Topics in MDPI journals
Dr. Ailong Ma

E-Mail Website
Guest Editor
State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing (LIESMARS), Wuhan University, Wuhan 430079, China
Interests: remote sensing; land cover mapping; object detection; deep learning; disaster response
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Guohua Wu

E-Mail Website
Guest Editor
School of Automation, Central South University, Changsha 410083, China
Interests: planning and scheduling; swarm intelligence; evolutionary computation; intelligent transportation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Today, it is easy to obtain remote sensing images from different types of sensors, such as hyperspectral, multispectral, LiDAR, etc. Remote sensing images (RSIs) are one of the fastest growing research areas because of their wide range of applications. 

As remote sensing technologies and methods continue to improve in recent decades, scientists have made great strides in the field of remote sensing image processing. Satellite, airborne, UAV, and terrestrial imaging techniques are constantly evolving in terms of data volume, quality, and variety. Remarkable efforts have been made to improve their interpretation accuracy, subpixel-level classification, and many other aspects.

This Special Issue will be a collection of articles focusing on new insights, new developments, current challenges, and future prospects in the field of remote sensing image processing. It aims to present the latest advances in innovative image analysis and processing techniques and their contribution to a wide range of application areas, in an effort to predict the future progress of the discipline and practice that they will facilitate in the coming years.

Prof. Dr. Weitao Chen
Dr. Ailong Ma
Prof. Dr. Guohua Wu
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. Applied Sciences 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

  • remote sensing
  • image processing
  • hyperspectral image

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

Further information on MDPI's Special Issue policies can be found here.

Related Special Issue

Published Papers (2 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

27 pages, 13324 KiB  
Article
ShadeNet: Innovating Shade House Detection via High-Resolution Remote Sensing and Semantic Segmentation
by Yinyu Liang, Minduan Xu, Wuzhou Dong and Qingling Zhang
Appl. Sci. 2025, 15(7), 3735; https://doi.org/10.3390/app15073735 - 28 Mar 2025
Viewed by 234
Abstract
Shade houses are critical for modern agriculture, providing optimal growing conditions for shade-sensitive crops. However, their rapid expansion poses ecological challenges, making the accurate extraction of their spatial distribution crucial for sustainable development. The unique dark appearance of shade houses leads to low [...] Read more.
Shade houses are critical for modern agriculture, providing optimal growing conditions for shade-sensitive crops. However, their rapid expansion poses ecological challenges, making the accurate extraction of their spatial distribution crucial for sustainable development. The unique dark appearance of shade houses leads to low accuracy and high misclassification rates in traditional spectral index-based extraction methods, while deep learning approaches face challenges such as insufficient datasets, limited receptive fields, and poor generalization capabilities. To address these challenges, we propose ShadeNet, a novel method for shade house detection using high-resolution remote sensing imagery and semantic segmentation. ShadeNet integrates the Swin Transformer and Mask2Former frameworks, enhanced by a Global-Channel and Local-Spatial Attention (GCLSA) module. This architecture significantly improves multi-scale feature extraction and global feature capture, thereby enhancing extraction accuracy. Tested on a self-labeled dataset, ShadeNet achieved a mean Intersection over Union (mIOU) improvement of 2.75% to 7.37% compared to existing methods, significantly reducing misclassification. The integration of the GCLSA module within the Swin Transformer framework enhances the model’s ability to capture both global and local features, addressing the limitations of traditional CNNs. This innovation provides a robust solution for shade houses detection, supporting sustainable agricultural development and environmental protection. Full article
(This article belongs to the Special Issue Remote Sensing Image Processing and Application, 2nd Edition)
►▼ Show Figures

Figure 1

18 pages, 5944 KiB  
Article
Coastal Zone Classification Based on U-Net and Remote Sensing
by Pei Liu, Changhu Wang, Maosong Ye and Ruimei Han
Appl. Sci. 2024, 14(16), 7050; https://doi.org/10.3390/app14167050 - 12 Aug 2024
Cited by 2 | Viewed by 1358
Abstract
The coastal zone is abundant in natural resources but has become increasingly fragile in recent years due to climate change and extensive, improper exploitation. Accurate land use and land cover (LULC) mapping of coastal zones using remotely sensed data is crucial for monitoring [...] Read more.
The coastal zone is abundant in natural resources but has become increasingly fragile in recent years due to climate change and extensive, improper exploitation. Accurate land use and land cover (LULC) mapping of coastal zones using remotely sensed data is crucial for monitoring environmental changes. Traditional classification methods based on statistical learning require significant spectral differences between ground objects. However, state-of-the-art end-to-end deep learning methods can extract advanced features from remotely sensed data. In this study, we employed ResNet50 as the feature extraction network within the U-Net architecture to achieve accurate classification of coastal areas and assess the model’s performance. Experiments were conducted using Gaofen-2 (GF-2) high-resolution remote sensing data from Shuangyue Bay, a typical coastal area in Guangdong Province. We compared the classification results with those obtained from two popular deep learning models, SegNet and DeepLab v3+, as well as two advanced statistical learning models, Support Vector Machine (SVM) and Random Forest (RF). Additionally, this study further explored the significance of Gray Level Co-occurrence Matrix (GLCM) texture features, Histogram Contrast (HC) features, and Normalized Difference Vegetation Index (NDVI) features in the classification of coastal areas. The research findings indicated that under complex ground conditions, the U-Net model achieved the highest overall accuracy of 86.32% using only spectral channels from GF-2 remotely sensed data. When incorporating multiple features, including spectrum, texture, contrast, and vegetation index, the classification accuracy of the U-Net algorithm significantly improved to 93.65%. The major contributions of this study are twofold: (1) it demonstrates the advantages of deep learning approaches, particularly the U-Net model, for LULC classification in coastal zones using high-resolution remote sensing images, and (2) it analyzes the contributions of spectral and spatial features of GF-2 data for different land cover types through a spectral and spatial combination method. Full article
(This article belongs to the Special Issue Remote Sensing Image Processing and Application, 2nd Edition)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-2
Back to TopTop