Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
5.5
2.5
Journals
Applied Sciences
Special Issues
Remote Sensing Image Processing and Application, 2nd Edition
applsci-logo
Submit to Applied Sciences 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: closed (31 January 2026) | Viewed by 13986

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
The State Key Laboratory of Information Engineering in Surveying, Mapping, and Remote Sensing (LIESMARS), Wuhan University, Wuhan 430074, China
Interests: multi-modal remote sensing learning; disaster assessment
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 250 words) can be sent to the Editorial Office for assessment.

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.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

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

Related Special Issue

Published Papers (7 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

17 pages, 4059 KB  
Article
Improving Land Cover Classification Accuracy in Satellite Imagery Using Artistic Styles
by Taeyeon Won and Yang Dam Eo
Appl. Sci. 2026, 16(5), 2476; https://doi.org/10.3390/app16052476 - 4 Mar 2026
Viewed by 396
Abstract
This study addresses ambiguity in spatial extent and boundaries in satellite image classification to improve the accuracy of fine-grained object-based Level 2 land cover classification. Unlike conventional data augmentation, we propose a novel Style-Adaptive U-Net that incorporates the visual characteristics of landscape paintings [...] Read more.
This study addresses ambiguity in spatial extent and boundaries in satellite image classification to improve the accuracy of fine-grained object-based Level 2 land cover classification. Unlike conventional data augmentation, we propose a novel Style-Adaptive U-Net that incorporates the visual characteristics of landscape paintings into classification learning. Specifically, we developed a lightweight CNN-based Art Encoder coupled with an Enhanced Style Feature Fusion (ESFF) module to inject artistic features into the network’s feature representation. Based on visual features extracted from works by Egon Schiele, Van Gogh, Claude Monet, and Elyse Dodge, the model utilizes painting styles with distinct boundaries or strong textures to explicitly enhance the boundary recognition capability of objects. Experimental results demonstrate the efficiency and superiority of the proposed model. It achieves a peak Dice score of 0.7631, outperforming the baseline U-Net’s 0.6512, and maintains a manageable processing load with only a 19% increase in parameters. Our comparative analysis shows a distinct representational mechanism by demonstrating that styles with explicit structural features (Schiele, Dodge) improve boundary discrimination, whereas styles emphasizing blurred transitions (Monet) yield limited functional gain. This validates our premise that the network actively utilizes artistic features as functional structural guidance rather than mere aesthetic enhancements, offering an efficient paradigm for resolving geographic ambiguity. Full article
(This article belongs to the Special Issue Remote Sensing Image Processing and Application, 2nd Edition)
►▼ Show Figures

Figure 1

27 pages, 7226 KB  
Article
Interpretable Deep Learning for Landslide Forecasting in Post-Seismic Areas: Integrating SBAS-InSAR and Environmental Factors
by H. Y. Guo and A. M. Martínez-Graña
Appl. Sci. 2026, 16(4), 1852; https://doi.org/10.3390/app16041852 - 12 Feb 2026
Viewed by 836
Abstract
Forecasting post-seismic landslide displacement is challenged by the difficulty in distinguishing short-term acceleration from creep and the risk of spatiotemporal leakage. To address this, an interpretable deep-learning framework is developed, integrating SBAS-InSAR time series with an Attention-enhanced Gated Recurrent Unit (Attention-GRU). Prior to [...] Read more.
Forecasting post-seismic landslide displacement is challenged by the difficulty in distinguishing short-term acceleration from creep and the risk of spatiotemporal leakage. To address this, an interpretable deep-learning framework is developed, integrating SBAS-InSAR time series with an Attention-enhanced Gated Recurrent Unit (Attention-GRU). Prior to modeling, a multi-stage preprocessing strategy, including empirical mode decomposition, is applied to mitigate noise and delineate active deformation zones. Unlike standard architectures, the model’s temporal attention mechanism adaptively amplifies critical precursory acceleration phases. Furthermore, a strict landslide-object-based partitioning strategy is employed to rigorously mitigate spatiotemporal leakage. The framework was evaluated in the Le’an Town landslide cluster using multi-source data. Targeting identified hazardous regions, the method achieved an R2 of 0.93 and reduced MAPE by 42.7% relative to the SVR baseline. This reflects a location-specific predictive capability, within active zones rather than regional generalization. SHapley Additive exPlanations (SHAP) further confirmed the model captures physical relationships, such as sensitivity to 25–35° slopes and vegetation degradation. Ultimately, the proposed framework offers a transparent, physically interpretable tool for operational hazard mitigation. Full article
(This article belongs to the Special Issue Remote Sensing Image Processing and Application, 2nd Edition)
►▼ Show Figures

Figure 1

22 pages, 7392 KB  
Article
Recursive Deep Feature Learning for Hyperspectral Image Super-Resolution
by Jiming Liu, Chen Yi and Hehuan Li
Appl. Sci. 2026, 16(2), 1060; https://doi.org/10.3390/app16021060 - 20 Jan 2026
Viewed by 445
Abstract
The advancement of hyperspectral image super-resolution (HSI-SR) has been significantly propelled by deep learning techniques. However, current methods predominantly rely on 2D or 3D convolutional networks, which are inherently local and thus limited in modeling long-range spectral–depth interactions. This work introduces a novel [...] Read more.
The advancement of hyperspectral image super-resolution (HSI-SR) has been significantly propelled by deep learning techniques. However, current methods predominantly rely on 2D or 3D convolutional networks, which are inherently local and thus limited in modeling long-range spectral–depth interactions. This work introduces a novel network architecture designed to address this gap through recursive deep feature learning. Our model initiates with 3D convolutions to extract preliminary spectral–spatial features, which are progressively refined via densely connected grouped convolutions. A core innovation is a recursively formulated generalized self-attention mechanism, which captures long-range dependencies across the spectral dimension with linear complexity. To reconstruct fine spatial details across multiple scales, a progressive upsampling strategy is further incorporated. Evaluations on several public benchmarks demonstrate that the proposed approach outperforms existing state-of-the-art methods in both quantitative metrics and visual quality. Full article
(This article belongs to the Special Issue Remote Sensing Image Processing and Application, 2nd Edition)
►▼ Show Figures

Figure 1

22 pages, 4169 KB  
Article
Multi-Scale Differentiated Network with Spatial–Spectral Co-Operative Attention for Hyperspectral Image Denoising
by Xueli Chang, Xiaodong Wang, Xiaoyu Huang, Meng Yan and Luxiao Cheng
Appl. Sci. 2025, 15(15), 8648; https://doi.org/10.3390/app15158648 - 5 Aug 2025
Cited by 1 | Viewed by 1234
Abstract
Hyperspectral image (HSI) denoising is a crucial step in image preprocessing as its effectiveness has a direct impact on the accuracy of subsequent tasks such as land cover classification, target recognition, and change detection. However, existing methods suffer from limitations in effectively integrating [...] Read more.
Hyperspectral image (HSI) denoising is a crucial step in image preprocessing as its effectiveness has a direct impact on the accuracy of subsequent tasks such as land cover classification, target recognition, and change detection. However, existing methods suffer from limitations in effectively integrating multi-scale features and adaptively modeling complex noise distributions, making it difficult to construct effective spatial–spectral joint representations. This often leads to issues like detail loss and spectral distortion, especially when dealing with complex mixed noise. To address these challenges, this paper proposes a multi-scale differentiated denoising network based on spatial–spectral cooperative attention (MDSSANet). The network first constructs a multi-scale image pyramid using three downsampling operations and independently models the features at each scale to better capture noise characteristics at different levels. Additionally, a spatial–spectral cooperative attention module (SSCA) and a differentiated multi-scale feature fusion module (DMF) are introduced. The SSCA module effectively captures cross-spectral dependencies and spatial feature interactions through parallel spectral channel and spatial attention mechanisms. The DMF module adopts a multi-branch parallel structure with differentiated processing to dynamically fuse multi-scale spatial–spectral features and incorporates a cross-scale feature compensation strategy to improve feature representation and mitigate information loss. The experimental results show that the proposed method outperforms state-of-the-art methods across several public datasets, exhibiting greater robustness and superior visual performance in tasks such as handling complex noise and recovering small targets. Full article
(This article belongs to the Special Issue Remote Sensing Image Processing and Application, 2nd Edition)
►▼ Show Figures

Figure 1

16 pages, 2607 KB  
Article
Deep Learning-Based Detection and Assessment of Road Damage Caused by Disaster with Satellite Imagery
by Jungeun Cha, Seunghyeok Lee and Hoe-Kyoung Kim
Appl. Sci. 2025, 15(14), 7669; https://doi.org/10.3390/app15147669 - 8 Jul 2025
Cited by 4 | Viewed by 5767
Abstract
Natural disasters can cause severe damage to critical infrastructure such as road networks, significantly delaying rescue and recovery efforts. Conventional road damage assessments rely heavily on manual inspection, which is labor-intensive, time-consuming, and infeasible in large-scale disaster-affected areas. This study aims to propose [...] Read more.
Natural disasters can cause severe damage to critical infrastructure such as road networks, significantly delaying rescue and recovery efforts. Conventional road damage assessments rely heavily on manual inspection, which is labor-intensive, time-consuming, and infeasible in large-scale disaster-affected areas. This study aims to propose a deep learning-based framework to automatically detect and quantitatively assess road damage using high-resolution pre- and post-disaster satellite imagery. To achieve this, the study systematically compares three distinct change detection approaches: single-timeframe overlay, difference-based segmentation, and Siamese feature fusion. Experimental results, validated over multiple runs, show the difference-based model achieved the highest overall F1-score (0.594 ± 0.025), surpassing the overlay and Siamese models by approximately 127.6% and 27.5%, respectively. However, a key finding of this study is that even this best-performing model is constrained by a low detection recall (0.445 ± 0.051) for the ‘damaged road’ class. This reveals that severe class imbalance is a fundamental hurdle in this domain for which standard training strategies are insufficient. This study establishes a crucial benchmark for the field, highlighting that future research must focus on methods that directly address class imbalance to improve detection recall. Despite its quantified limitations, the proposed framework enables the visualization of damage density maps, supporting emergency response strategies such as prioritizing road restoration and accessibility planning in disaster-stricken areas. Full article
(This article belongs to the Special Issue Remote Sensing Image Processing and Application, 2nd Edition)
►▼ Show Figures

Figure 1

27 pages, 13324 KB  
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
Cited by 2 | Viewed by 1278
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 KB  
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 12 | Viewed by 2750
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 1-7
Back to TopTop