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Multi-Modal Image Processing Methods, Systems, and Applications: 2nd Edition
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Multi-Modal Image Processing Methods, Systems, and Applications: 2nd Edition

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Sensing and Imaging".

Deadline for manuscript submissions: 10 September 2025 | Viewed by 1334

Special Issue Editors

Dr. Kechen Song

E-Mail Website
Guest Editor
School of Mechanical Engineering & Automation, Northeastern University, Shenyang 110819, China
Interests: applied surface science; vision detection for surface defects; multi-modal image analysis and application
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Yunhui Yan

E-Mail Website
Guest Editor
School of Mechanical Engineering & Automation, Northeastern University, WenHua Road 3-11, Heping District, Shenyang 110819, China
Interests: feature extraction; image segmentation; object detection; image fusion; computer vision
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Multi-sensor systems are widely deployed for various real-world applications, thereby further enhancing the visual perception of machines by obtaining multi-modal images. RGB-Depth (RGBD) images and RGB-Thermal infrared images are currently the most commonly used multi-modal images, among which D images provide stereoscopic information on the object, and the T images strengthen the imaging capability of the machine vision system under insufficient illumination. Multi-modal images enable a more accurate and robust detection in vision tasks, such as object detection and segmentation, image fusion and enhancement, object tracking and positioning, etc. At the same time, various real-world applications are easier to accomplish with the use of multi-modal images, such as autonomous driving, industrial defect detection, robot control, and remote sensing inspection. However, the visual perception gain brought by multi-modal images also increases the difficulty of data processing. The acquisition and balance of different modal information, combination and enhancement of multiple features, and the generalization and robustness of multi-sensor systems in complex scenes are all challenges encountered in multi-modal image processing.

To address these challenges, this Special Issue calls for original and innovative methodological contributions to tackle the issues of multi-modal image processing. These papers may cover all areas of multi-modal vision tasks, from fundamental theoretical methods to the latest innovative multi-sensor system designs. Topics of interest include the detection and recognition of multi-modal images, multi-modal industrial applications, autonomous driving, robot vision and control, remote sensing data processing, etc. Critical reviews and surveys of multi-modal images and multi-sensor systems are also encouraged.

Dr. Kechen Song
Prof. Dr. Yunhui Yan
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. Sensors 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 2600 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

  • multi-sensor systems
  • multi-modal images (RGB-D/T)
  • multi-modal object detection
  • multi-modal segmentation
  • multi-modal image fusion
  • multi-modal object tracking
  • automatic drive
  • robotic visual perception
  • unmanned aerial vehicles (UAVs)

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Related Special Issue

Published Papers (3 papers)

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Research

17 pages, 12952 KiB  
Article
Advancing Textile Damage Segmentation: A Novel RGBT Dataset and Thermal Frequency Normalization
by Farshid Rayhan, Jitesh Joshi, Guangyu Ren, Lucie Hernandez, Bruna Petreca, Sharon Baurley, Nadia Berthouze and Youngjun Cho
Sensors 2025, 25(7), 2306; https://doi.org/10.3390/s25072306 - 5 Apr 2025
Viewed by 255
Abstract
RGB-Thermal (RGBT) semantic segmentation is an emerging technology for identifying objects and materials in high dynamic range scenes. Thermal imaging particularly enhances feature extraction at close range for applications such as textile damage detection. In this paper, we present RGBT-Textile, a novel dataset [...] Read more.
RGB-Thermal (RGBT) semantic segmentation is an emerging technology for identifying objects and materials in high dynamic range scenes. Thermal imaging particularly enhances feature extraction at close range for applications such as textile damage detection. In this paper, we present RGBT-Textile, a novel dataset specifically developed for close-range textile and damage segmentation. We meticulously designed the data collection protocol, software tools, and labeling process in collaboration with textile scientists. Additionally, we introduce ThermoFreq, a novel thermal frequency normalization method that reduces temperature noise effects in segmentation tasks. We evaluate our dataset alongside six existing RGBT datasets using state-of-the-art (SOTA) models. Experimental results demonstrate the superior performance of the SOTA models with ThermoFreq, highlighting its effectiveness in addressing noise challenges inherent in RGBT semantic segmentation across diverse environmental conditions. We make our dataset publicly accessible to foster further research and collaborations. Full article
(This article belongs to the Special Issue Multi-Modal Image Processing Methods, Systems, and Applications: 2nd Edition)
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24 pages, 14100 KiB  
Article
SDA-Net: A Spatially Optimized Dual-Stream Network with Adaptive Global Attention for Building Extraction in Multi-Modal Remote Sensing Images
by Xuran Pan, Kexing Xu, Shuhao Yang, Yukun Liu, Rui Zhang and Ping He
Sensors 2025, 25(7), 2112; https://doi.org/10.3390/s25072112 - 27 Mar 2025
Viewed by 252
Abstract
Building extraction plays a pivotal role in enabling rapid and accurate construction of urban maps, thereby supporting urban planning, smart city development, and urban management. Buildings in remote sensing imagery exhibit diverse morphological attributes and spectral signatures, yet their reliable interpretation through single-modal [...] Read more.
Building extraction plays a pivotal role in enabling rapid and accurate construction of urban maps, thereby supporting urban planning, smart city development, and urban management. Buildings in remote sensing imagery exhibit diverse morphological attributes and spectral signatures, yet their reliable interpretation through single-modal data remains constrained by heterogeneous terrain conditions, occlusions, and spatially variable illumination effects inherent to complex geographical landscapes. The integration of multi-modal data for building extraction offers significant advantages by leveraging complementary features from diverse data sources. However, the heterogeneity of multi-modal data complicates effective feature extraction, while the multi-scale cross-modal feature fusion encounters a semantic gap issue. To address these challenges, a novel building extraction network based on multi-modal remote sensing data called SDA-les (AGAFMs) was designed in the decoding stage to fuse multi-modal features at various scales, which dynamically adjust the importance of features from a global perspective to better balance the semantic information. The superior performance of the proposed method is demonstrated through comprehensive evaluations on the ISPRS Potsdam dataset with 97.66% F1 score and 95.42% IoU, the ISPRS Vaihingen dataset with 96.56% F1 score and 93.35% IoU, and the DFC23 Track2 dataset with 91.35% F1 score and 84.08% IoU. Full article
(This article belongs to the Special Issue Multi-Modal Image Processing Methods, Systems, and Applications: 2nd Edition)
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28 pages, 41613 KiB  
Article
Acquisition and Modeling of Material Appearance Using a Portable, Low Cost, Device
by Davide Marelli, Simone Bianco and Gianluigi Ciocca
Sensors 2025, 25(4), 1143; https://doi.org/10.3390/s25041143 - 13 Feb 2025
Viewed by 620
Abstract
Material appearance acquisition allows researchers to capture the optical properties of surfaces and use them in different tasks such as material analysis, digital twins reproduction, 3D configurators, augmented and virtual reality, etc. Precise acquisition of such properties requires complex and expensive hardware. In [...] Read more.
Material appearance acquisition allows researchers to capture the optical properties of surfaces and use them in different tasks such as material analysis, digital twins reproduction, 3D configurators, augmented and virtual reality, etc. Precise acquisition of such properties requires complex and expensive hardware. In this paper, we aim to answer the following research challenge: Can we design an accurate enough but low-cost and portable device for material appearance acquisition? We present the rationale behind the design of our device using consumer-grade hardware components. Ultimately, our device costs EUR 80 and can acquire surface patches of size 5 × 5 cm with a 40 pix/mm resolution. Our device exploits a traditional RGB camera to capture a surface using 24 different images, each photographed using different lighting conditions. The different lighting conditions are generated by exploiting the LED rings included in our device; specifically, each of the 24 images is acquired by turning on one individual LED at time. We also illustrate the custom processing pipelines developed to support capturing and generating the material data in terms of albedo, normal, and roughness maps. The accuracy of the acquisition process is comprehensively evaluated both quantitatively and qualitatively. Results show that our low-cost device can faithfully acquire different materials. The usefulness of our device is further demonstrated by a textile virtual catalog application that we designed for rendering virtual fabrics on a mobile apparatus. Full article
(This article belongs to the Special Issue Multi-Modal Image Processing Methods, Systems, and Applications: 2nd Edition)
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