Recent Advances in Object Detection with Hyperspectral Remote Sensing Data
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 2025 | Viewed by 48
Special Issue Editors
Interests: remote sensing image processing; hyperspectral data; machine learning; pattern recognition
Special Issue Information
Dear Colleagues,
This Special Issue focuses on the latest developments in using hyperspectral imaging for object detection. Hyperspectral data, which spans a broad range of wavelengths beyond the visible spectrum, offers unique advantages for detecting and analyzing objects in diverse environments. This Issue highlights how machine learning and deep learning techniques, including statistical approaches, learning-based methods, and other learning paradigms, are enhancing object detection accuracy by addressing challenges like dimensionality reduction and spectral-spatial feature fusion. Research also explores the integration of hyperspectral data with other remote sensing technologies, such as LiDAR and multi-spectral imagery, to provide more detailed insights. Additionally, advancements in real-time processing and cloud-based platforms for large-scale data analysis are discussed as essential for future applications in fields like environmental monitoring, agriculture, urban planning, and defense. Overall, this Special Issue presents state-of-the-art methodologies and emerging trends, offering valuable insights for those involved in hyperspectral remote sensing and object detection.
This Special Issue will focus on these cutting-edge developments and provide a platform for advancing the state-of-the-art in hyperspectral remote sensing data for object detection. It will cover (but will not be limited to) the following topics:
- Dimensionality reduction and feature fusion for hyperspectral object detection;
- Advanced object detection algorithms for hyperspectral data;
- Integration of hyperspectral and multi-source remote sensing data;
- Real-world/real-time applications of hyperspectral object detection;
- Opportunities and future trends in hyperspectral object detection.
Dr. Youqiang Zhang
Dr. Xuesong Li
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. 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 data
- object detection
- dimensionality reduction
- feature fusion
- machine learning
- deep learning
- real-time detection
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