Hyperspectral Remote Sensing Imagery for Object Detection
A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing Image Processing".
Deadline for manuscript submissions: 31 July 2026 | Viewed by 127
Special Issue Editors
Interests: hyperspectral vision; pattern recognition; machine learning
Interests: machine/deep learning foundations for remote sensing; remote sensing image classification; object detection; semantic segmentation; change detection; anomaly detection
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Hyperspectral remote sensing has revolutionized Earth observation by capturing detailed spectral information across hundreds of narrow, contiguous bands. This enables the discrimination of materials based on their unique biochemical and physical properties. Driven by the advancements in sensor design, platform deployment (e.g., PRISMA, EnMAP, DESIS), and computing power, technology in this field has developed significantly, opening new frontiers in object detection.
Object detection in hyperspectral imagery (HSI) goes beyond pixel-level classification to identify, localize, and characterize discrete objects, from vehicles and tree species to mineral deposits, even when they are small, obscured, or spectrally similar to the background. This capability is critical for precision agriculture, environmental monitoring, urban planning, and defense.
However, HSI object detection faces several challenges: the “curse of dimensionality”, data redundancy, complex spatial–spectral interactions, and model vulnerability to cross-domain/scenario variations (e.g., illumination changes, geographic shifts). Recent advances in artificial intelligence, particularly deep learning (e.g., convolutional networks, vision transformers), offer promising pathways forward.
This Special Issue, “Hyperspectral Remote Sensing Imagery for Object Detection,” seeks high-quality original research and reviews addressing the latest theoretical, methodological, and application advances for hyperspectral remote sensing object detection. Topics of interest include, but are not limited to, the following:
- Novel Learning Architectures: The design and application of advanced models (e.g., Graph Neural Networks, Vision Transformers, State Space Models, Foundation Models) tailored for HSI object detection.
- Spectral–Spatial Feature Fusion: Methods for effectively integrating spatial context with spectral information to improve detection accuracy and robustness.
- Weakly/Self-Supervised/Few-Shot/Transfer Learning: Approaches to address annotation-scarce training data in HSI object detection.
- Real-Time and Embedded Processing: Algorithm optimization, compression, and hardware acceleration for onboard or near-real-time detection.
- Multimodal and Multi-Temporal Data Fusion: The fusion of HSI with LiDAR, SAR, RGB, or temporal data to enhance detection.
- Explainable AI (XAI) for Object Detection: Interpretable models that reveal decisive spectral–spatial features.
- Benchmark Datasets and Evaluation: New challenging datasets and comprehensive evaluation frameworks.
- Domain-Specific Applications: Innovations in environmental monitoring, agriculture, urban surveillance, mineral exploration, etc.
- Detection with Band-Limited HSI: Methods optimized for HSI with selected or limited spectral bands (e.g., visible to infrared spectral band range).
Dr. Tan Guo
Prof. Dr. Fulin Luo
Prof. Dr. Lei Zhang
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. 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
- hyperspectral remote sensing
- object detection
- explainable AI (XAI)
- spectral-spatial feature fusion
- domain-specific applications
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