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Applications to Internet of Things with Images and Videos from Remote Sensing

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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing Image Processing".

Deadline for manuscript submissions: 30 November 2026 | Viewed by 3836

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Editors

Prof. Dr. Chen Chen

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Guest Editor

School of Telecommunications Engineering, Xidian University, Xi’an 710071, China
Interests: Internet of Things; space-air-ground integrated networks; vehicular networks; intelligent transportation; artificial intelligence
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Liang Zhao

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Guest Editor

College of Computer Science, Shenyang Aerospace University, Shenyang 110136, China
Interests: mobile computing (networking and edge computing of vehicle, UAV, satellite); intelligent transportation
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Prof. Dr. Yoong Choon Chang

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Guest Editor

Lee Kong Chian Faculty of Engineering and Science, Universiti Tunku Abdul Rahman, Bandar Sungai Long, Kajang 43000, Malaysia
Interests: applications of artificial intelligence; image and video signal processing and communication

Dr. Ying Ju

E-Mail Website
Guest Editor

School of Telecommunications Engineering, Xidian University, Xi’an 710071, China
Interests: physical layer security of wireless communications; millimeter-wave communication systems; Internet of Vehicles
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

As the Internet of Things (IoT) continues to evolve, the integration of remote sensing images and videos offers unprecedented opportunities for monitoring and data analysis in various fields. The combination of remote sensing data and IoT applications helps to improve observation and decision-making capabilities, enhancing applications in environmental monitoring and resource management. Despite this, further efforts are needed to develop more reliable, intelligent and efficient solutions to facilitate wider applications of IoT and remote sensing technologies. This Special Issue will promote the development of applying remote sensing images and videos to Internet of Things technology. We hope to propose novel methods and future perspectives regarding the related research and applications, providing data support for various fields.

We encourage submissions of original manuscripts that focus on the applications of remote sensing images and videos with the Internet of Things. Topics may include the following:

Edge computing, cloud computing and big data analytics in remote sensing;
Remote sensing images and videos processing;
Multi-platforms sensing technology, such as satellite, airborne and UAV;
Development of IoT architectures that support remote sensing data transmission and storage;
Intelligent analytics and decision support systems leveraging remote sensing images and videos;
Applications of remote sensing and IoT technologies, such as environmental monitoring, water resource management, etc.

Prof. Dr. Chen Chen
Prof. Dr. Liang Zhao
Prof. Dr. Yoong Choon Chang
Dr. Ying Ju
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-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing 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 2700 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

Internet of Things
remote sensing technology
environmental monitoring
image and video processing
intelligent data analysis
decision support systems

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Published Papers (3 papers)

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Research

29 pages, 22126 KB

Open AccessArticle

Mask-Guided Feature Routing and Adaptive Context Modeling for Wide-FoV UAV Object Detection in IoT Remote Sensing

by Lingfan Wu, Yachun Feng, Hong Zhang and Yawei Li

Remote Sens. 2026, 18(11), 1753; https://doi.org/10.3390/rs18111753 - 30 May 2026

Viewed by 432

Abstract

Object detection in wide-field-of-view (wide-FoV) unmanned aerial vehicle (UAV) imagery for Internet of Things (IoT) remote sensing applications requires accurate recognition of tiny objects under severe background redundancy and extreme scale variation. As the field of view expands, conventional dense detectors tend to waste substantial computation on non-informative regions, while feature downsampling and static receptive fields often cause the dilution of foreground information and scale confusion. To address these issues, we propose MFRC-Det, a unified framework built upon two complementary principles: mask-guided feature routing and adaptive context modeling. Specifically, a Superpixel-Masking Generator (SP-Masker) is introduced to estimate an image-space soft foreground prior by comparing Simple Linear Iterative Clustering (SLIC) superpixel histograms with a peripheral background reference, propagating the resulting scores on a superpixel adjacency graph, and projecting the refined region-level scores back to a pixel-level routing mask. Guided by these priors, a Greedy-Cutter (G-Cutter) converts dense feature maps into compact, foreground-focused patches without repeated backbone evaluation on cropped image regions, thereby reducing redundant background computation while preserving local structural coherence. On top of the retained regions, an Adaptive Receptive-field Selection Network (ARSNet) aggregates multi-scale contextual responses from several learnable receptive-field candidate branches. ARSNet predicts spatial selection weights conditioned on the input features, allowing each location to emphasize a suitable receptive-field response for object representation. Experimental results on VisDrone-DET and UAVDT demonstrate that MFRC-Det achieves competitive detection accuracy with favorable computational efficiency. Specifically, MFRC-Det obtains 36.1% AP, 60.4%

A P_{50}

, and 38.5 FPS on VisDrone-DET and 21.3% AP, 36.8%

A P_{50}

, and 37.4 FPS on UAVDT. These results validate the effectiveness of mask-guided feature routing and adaptive context modeling for wide-FoV UAV object detection and suggest their potential value for computation-efficient aerial perception in IoT remote sensing applications. Full article

(This article belongs to the Special Issue Applications to Internet of Things with Images and Videos from Remote Sensing)

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22 pages, 18628 KB

Open AccessArticle

CISPD: Complementary Illumination–Semantic Prompt Diffusion for Low-Light Remote Sensing Image Enhancement

by Huan Gao, Yuntai Liao, Zongfang Ma and Lin Song

Remote Sens. 2026, 18(9), 1347; https://doi.org/10.3390/rs18091347 - 28 Apr 2026

Viewed by 585

Abstract

When performing nighttime passive visible remote sensing of non-emissive land surfaces, illumination is typically dominated by weak moonlight that varies with lunar phase, producing low-radiance images with degraded textures and thus motivating low-radiance visible remote sensing image enhancement. We propose a Complementary Illumination–Semantic Prompt Diffusion framework (CISPD) that incorporates a semantic-invariant prompt and a self-learned illumination-aware prompt to guide diffusion-based low-light remote sensing image enhancement. During denoising, we sequentially inject two complementary prompts. We first retrieve a self-learned illumination-aware prompt from a learnable pool conditioned on the current latent context to correct non-uniform brightness, and then apply a semantic-invariant prompt extracted from a vision foundation model to reinforce geometric structures and suppress artifacts. To keep the two prompts complementary rather than redundant, we introduce a contrastive constraint that encourages their representations to remain distinct, and the dual prompts jointly steer the diffusion trajectory toward well-exposed results with faithful structures. Experiments on iSAID-dark and darkrs, together with LOLv1 and LOLv2, demonstrate that CISPD achieves the best PSNR and SSIM on iSAID-dark, strong qualitative generalization on darkrs, and competitive quantitative performance on LOLv1 and LOLv2. Full article

(This article belongs to the Special Issue Applications to Internet of Things with Images and Videos from Remote Sensing)

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28 pages, 5125 KB

Open AccessArticle

Dual-Branch Hyperspectral Open-Set Classification with Reconstruction–Prototype Fusion for Satellite IoT Perception

by Jialing Tang, Shengwei Lei, Jingqi Liu, Ning Lv and Haibin Qi

Remote Sens. 2025, 17(22), 3722; https://doi.org/10.3390/rs17223722 - 14 Nov 2025

Cited by 1 | Viewed by 1298

Abstract

The satellite Internet of Things (SatIoT) enables real-time acquisition and large-scale coverage of hyperspectral imagery, providing essential data support for decision-making in domains such as geological exploration, environmental monitoring, and urban management. Hyperspectral remote sensing classification constitutes a critical component of intelligent applications driven by the SatIoT, yet it faces two major challenges: the massive data volume imposes heavy storage and processing burdens on conventional satellite systems, while dimensionality reduction often compromises classification accuracy; furthermore, mainstream neural network models are constrained by insufficient labeled data and spectral shifts, frequently leading to misclassification of unknown categories and degradation of cross-regional performance. To address these issues, this study proposes an open-set hyperspectral classification method with dual branches of reconstruction and prototype-based classification. Specifically, we build upon an autoencoder. We design a spectral–spatial attention module and an information residual connection module. These modules accurately capture spectral–spatial features. This improves the reconstruction accuracy of known classes. It also adapts to the high-dimensional characteristics of satellite data. Prototype representations of unknown classes are constructed by incorporating classification confidence, enabling effective separation in the feature space and targeted recognition of unknown categories in complex scenarios. By jointly leveraging prototype distance and reconstruction error, the proposed method achieves synergistic improvement in both accurate classification of known classes and reliable detection of unknown ones. Comparative experiments and visualization analyses on three publicly available datasets: Salinas-A, PaviaU, and Dioni-demonstrate that the proposed approach significantly outperforms baseline methods such as MDL4OW and IADMRN in terms of unknown detection rate (UDR), open-set overall accuracy (OpenOA), and open-set F1 score, while on the Salinas-A dataset, the performance gap between closed-set and open-set classification is as small as 1.82%, highlighting superior robustness. Full article

(This article belongs to the Special Issue Applications to Internet of Things with Images and Videos from Remote Sensing)

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