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Advanced Signal and Image Processing Techniques for Sensor Applications—2nd Edition

A Special Issue of Sensors (ISSN 1424-8220) belonging to the section "Sensing and Imaging".

Deadline for manuscript submissions: 10 December 2026 | Viewed by 1355

Editors


E-Mail Website
Guest Editor
Department of Electrical and Computer Engineering, Tuskegee University, 307 Luther H. Foster Hall, Tuskegee, AL 36088, USA
Interests: signal processing; image processing; pattern recognition; communications; power electronics; computer vision; machine learning; biomedical engineering; smart grids; RF; radar; remote sensing; hyper-spectral imaging; education
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
Department of Electrical and Computer Engineering, Tuskegee University, 301 Luther H. Foster Hall, Tuskegee, AL 36088, USA
Interests: sensor signal processing; image processing pattern recognition; machine learning; radar signal processing; intelligent infrastructure systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Following the success of the previous Special Issue, “Advanced Signal and Image Processing Techniques for Sensor Applications”, we are pleased to announce the next Issue in the series, entitled “Advanced Signal and Image Processing Techniques for Sensor Applications—2nd Edition”.

With the rapid advancement of sensor technology, vast and continuously growing volumes of data across diverse domains and modalities have become readily available. However, the direct presentation of raw sensor data is sometimes inappropriate due to the presence of noise, distortion, and other artifacts. To extract relevant and meaningful information from sensor data, further enhancement of acquired signals—such as noise reduction in one-dimensional electroencephalographic (EEG) signals or color correction in endoscopic images—together with their analysis using computer-based medical systems, is required. Data processing and the subsequent extraction of useful information therefore play a central role and are key themes of this Special Issue.

This Special Issue of Sensors aims to highlight advances in the development, testing and application of signal and image processing algorithms and techniques for a wide range of sensors and sensing methodologies. Experimental and theoretical contributions, as well as original research articles and review papers, are welcome.

Research areas may include, but are not limited to, the following:

  • Advanced sensor characterization techniques;
  • Ambient assisted living;
  • Biomedical signal and image analysis;
  • Signal and image processing (e.g., deblurring, denoising, super-resolution);
  • Signal and image understanding (e.g., object detection and recognition, action recognition, semantic segmentation, novel feature extraction);
  • Internet of Things (IoT);
  • Machine learning (e.g., deep learning) in signal and image processing;
  • Radar signal processing;
  • Real-time signal and image processing algorithms and architectures (e.g., FPGA, DSP, GPU);
  • Remote sensing processing;
  • Sensor data fusion and integration;
  • Sensor error modelling and online calibration;
  • Smart environments and smart cities;
  • Wearable sensor signal processing and its applications;
  • Self-supervised and/or foundation models for signal and image processing from sensor data;
  • Multimodal sensor fusion and cross-domain representation learning using heterogeneous sensor data;
  • Edge AI, TinyML, and energy-efficient signal and image processing for embedded and real-time sensing platforms.

We look forward to receiving your contributions.

Dr. Jesmin Farzana Khan
Dr. Mandoye Ndoye
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. 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

  • signal processing
  • image processing
  • machine learning
  • wireless sensor networks
  • internet of things
  • deep neural networks
  • dictionary learning
  • compressive sensing
  • big data
  • brain–computer interface
  • artificial intelligence

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Published Papers (1 paper)

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Research

29 pages, 12399 KB  
Article
SpaSE-UNet3D: Sensor-Driven Wildfire Detection and Progression Prediction from VIIRS Multispectral Imagery
by Nikolaos Mavros and Dimitrios Katsaros
Sensors 2026, 26(16), 5116; https://doi.org/10.3390/s26165116 - 12 Aug 2026
Viewed by 967
Abstract
Timely wildfire monitoring depends critically on optical and thermal infrared sensor observations from spaceborne instruments. The TS-SatFire benchmark (2025) consolidates multispectral VIIRS image stacks from Suomi-NPP and NOAA-20 for three tasks: active fire (AF) detection, burned area (BA) mapping, and fire progression (FP) [...] Read more.
Timely wildfire monitoring depends critically on optical and thermal infrared sensor observations from spaceborne instruments. The TS-SatFire benchmark (2025) consolidates multispectral VIIRS image stacks from Suomi-NPP and NOAA-20 for three tasks: active fire (AF) detection, burned area (BA) mapping, and fire progression (FP) prediction. We make two contributions. First, a systematic label-quality audit reveals that many fires lack ground-truth annotations; 18 training fires and 2 test fires were excluded for AF, and the two unannotated test fires cannot be scored by any model. We further document the benchmark’s scoring procedure, which differs from ours in ways that make the two sets of figures incomparable, and the BA label encoding in the released GeoTIFFs; the BA task is only audited. Second, we propose SpaSE-UNet3D, a spatial squeeze-and-excitation 3D U-Net whose spatial-only (1,3,3) convolutions avoid temporal mixing on short observation windows, while SE channel attention reweights the VIIRS spectral bands dynamically. With micro-averaging over all test pixels, it reaches F1 = 0.8549±0.0005 on AF and 0.3845±0.0221 on FP at TS = 2, matching or exceeding the strongest published baselines on their respective terms. A single-day AF input reaches 0.8520±0.0008, within 0.003 of the two-day figure, indicating that one acquisition carries most of the detectable signal, whereas published baselines use up to six days; on FP, we use one third of their temporal context. An ablation shows the spatial-only design matches the accuracy of a full (3,3,3) network with 2.72× fewer parameters. Code and results are publicly available. Full article
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