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Remote Sensing
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Advances in Hyperspectral Data Processing
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Advances in Hyperspectral Data Processing

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

Deadline for manuscript submissions: closed (30 July 2024) | Viewed by 4312

Special Issue Editors

Dr. Karantzalos Konstantinos

E-Mail Website
Guest Editor
Remote Sensing Laboratory, National Technical University of Athens, 15780 Athens, Greece
Interests: hyperspectral imaging; UAVs; earth observation; data fusion; machine learning; computer vision; crop type classification; precision agriculture
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Danfeng Hong

grade E-Mail Website
Guest Editor
Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 10094, China
Interests: remote sensing; hyperspectral image processing; machine learning
Special Issues, Collections and Topics in MDPI journals
Dr. Behnood Rasti

E-Mail Website
Guest Editor
Helmholtz-Zentrum Dresden-Rossendorf, Helmholtz Institute Freiberg for Resource Technology, 09599 Freiberg, Germany
Interests: remote sensing; artificial Intelligence; hyperspectral image analysis; machine learning; denoising
Sindy Sterckx

E-Mail Website
Guest Editor
Vlaamse Instelling voor Technologisch Onderzoek, Mol, Belgium
Interests: remote sensing; water quality; imaging spectroscopy; calibration; satellite; remote sensing applications

Special Issue Information

Dear Colleagues,

The IEEE 13th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS 2023) is scheduled to take place from 31 October to 2 November 2023. WHISPERS is a significant scientific and technical event within the realm of remote sensing and is organized by the IEEE Geoscience and Remote Sensing Society (GRSS).

The conference’s primary focus is on exploring and advancing the field of remote sensing by delving into hyperspectral image and signal processing techniques. WHISPERS provides an invaluable platform for researchers and industry professionals to engage in discussions about the continuously evolving landscape of remote sensing technology and its diverse applications.

In conjunction with the conference, a Special Issue of MDPI Remote Sensing has been planned, which will be open to authors presenting papers at the conference. It is important to note that papers submitted for this Special Issue should not be identical to the papers presented at the WHISPERS conference. Instead, authors are encouraged to provide longer papers, typically 2 to 3 times longer, offering a more comprehensive presentation of their work, enhanced techniques and methodologies, additional datasets, and expanded experimental sections. Authors are also asked to specify the corresponding paper number for WHISPERS 2023 in their cover letter. Failing to provide this information will result in the paper being considered as a regular submission.

Dr. Karantzalos Konstantinos
Prof. Dr. Danfeng Hong
Dr. Behnood Rasti
Sindy Sterckx
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 processing
  • image processing
  • signal processing
  • feature extraction
  • dimension reduction
  • unmixing and source separation

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Further information on MDPI's Special Issue polices can be found here.

Published Papers (3 papers)

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Research

24 pages, 8028 KiB  
Article
SPTrack: Spectral Similarity Prompt Learning for Hyperspectral Object Tracking
by Gaowei Guo, Zhaoxu Li, Wei An, Yingqian Wang, Xu He, Yihang Luo, Qiang Ling, Miao Li and Zaiping Lin
Remote Sens. 2024, 16(16), 2975; https://doi.org/10.3390/rs16162975 - 14 Aug 2024
Viewed by 904
Abstract
Compared to hyperspectral trackers that adopt the “pre-training then fine-tuning” training paradigm, those using the “pre-training then prompt-tuning” training paradigm can inherit the expressive capabilities of the pre-trained model with fewer training parameters. Existing hyperspectral trackers utilizing prompt learning lack an adequate prompt [...] Read more.
Compared to hyperspectral trackers that adopt the “pre-training then fine-tuning” training paradigm, those using the “pre-training then prompt-tuning” training paradigm can inherit the expressive capabilities of the pre-trained model with fewer training parameters. Existing hyperspectral trackers utilizing prompt learning lack an adequate prompt template design, thus failing to bridge the domain gap between hyperspectral data and pre-trained models. Consequently, their tracking performance suffers. Additionally, these networks have a poor generalization ability and require re-training for the different spectral bands of hyperspectral data, leading to the inefficient use of computational resources. In order to address the aforementioned problems, we propose a spectral similarity prompt learning approach for hyperspectral object tracking (SPTrack). First, we introduce a spectral matching map based on spectral similarity, which converts 3D hyperspectral data with different spectral bands into single-channel hotmaps, thus enabling cross-spectral domain generalization. Then, we design a channel and position attention-based feature complementary prompter to learn blended prompts from spectral matching maps and three-channel images. Extensive experiments are conducted on the HOT2023 and IMEC25 data sets, and SPTrack is found to achieve state-of-the-art performance with minimal computational effort. Additionally, we verify the cross-spectral domain generalization ability of SPTrack on the HOT2023 data set, which includes data from three spectral bands. Full article
(This article belongs to the Special Issue Advances in Hyperspectral Data Processing)
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21 pages, 17352 KiB  
Article
On Optimizing Hyperspectral Inversion of Soil Copper Content by Kernel Principal Component Analysis
by Fei Guo, Zhen Xu, Honghong Ma, Xiujin Liu and Lei Gao
Remote Sens. 2024, 16(16), 2914; https://doi.org/10.3390/rs16162914 - 9 Aug 2024
Viewed by 941
Abstract
Heavy metal pollution not only causes detrimental effects on the environment but also poses threats to human health; thus, it is crucial to monitor the heavy metal content in the soil. Hyperspectral technology, characterized by high spectral resolution, rapid response, and non-destructive detection, [...] Read more.
Heavy metal pollution not only causes detrimental effects on the environment but also poses threats to human health; thus, it is crucial to monitor the heavy metal content in the soil. Hyperspectral technology, characterized by high spectral resolution, rapid response, and non-destructive detection, is widely employed in soil composition monitoring. This study aims to investigate the effects of dimensionality reduction methods on the performance of hyperspectral inversion. To this end, 56 soil samples were collected in Daye, with the corresponding hyperspectral data acquired by the advanced ASD Fieldspec4 instrument. We employed the linear dimensionality reduction method, i.e., the principal component analysis (PCA), and non-linear method in terms of kernel PCA (KPCA) with polynomial, radial basis function (RBF), and sigmoid kernels to reduce the dimensionalities of original spectral reflectance and that processed by first-derivative transformation (FDT). Building upon this foundation, we applied the Adaptive Boosting (AdaBoost) algorithm for inverting the soil copper (Cu) content. The performance of each inversion model was evaluated by evaluation indices in terms of the coefficient of determination (R2), root-mean-square error (RMSE), and residual prediction deviation (RPD). The results revealed that the KPCA with polynomial kernel function applied to the FDT-based spectra could yield the optimal inversion accuracy, with corresponding R2, RMSE, and RPD being 0.86, 21.47 mg·kg−1, and 2.72, respectively. This study demonstrates that applying the FDT with KPCA processing can significantly improve the accuracy of the hyperspectral inversion for soil Cu content, providing a potential approach for monitoring heavy metal pollution using hyperspectral technology. Full article
(This article belongs to the Special Issue Advances in Hyperspectral Data Processing)
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20 pages, 5723 KiB  
Article
Object-Enhanced YOLO Networks for Synthetic Aperture Radar Ship Detection
by Kun Wu, Zhijian Zhang, Zeyu Chen and Guohua Liu
Remote Sens. 2024, 16(6), 1001; https://doi.org/10.3390/rs16061001 - 12 Mar 2024
Cited by 3 | Viewed by 1839
Abstract
Synthetic aperture radar (SAR) enables precise object localization and imaging, which has propelled the rapid development of algorithms for maritime ship identification and detection. However, most current deep learning-based algorithms tend to increase network depth to improve detection accuracy, which may result in [...] Read more.
Synthetic aperture radar (SAR) enables precise object localization and imaging, which has propelled the rapid development of algorithms for maritime ship identification and detection. However, most current deep learning-based algorithms tend to increase network depth to improve detection accuracy, which may result in the loss of effective features of the target. In response to this challenge, this paper innovatively proposes an object-enhanced network, OE-YOLO, designed specifically for SAR ship detection. Firstly, we input the original image into an improved CFAR detector, which enhances the network’s ability to localize and perform object extraction by providing more information through an additional channel. Additionally, the Coordinate Attention mechanism (CA) is introduced into the backbone of YOLOv7-tiny to improve the model’s ability to capture spatial and positional information in the image, thereby alleviating the problem of losing the position of small objects. Furthermore, to enhance the model’s detection capability for multi-scale objects, we optimize the neck part of the original model to integrate the Asymptotic Feature Fusion (AFF) network. Finally, the proposed network model is thoroughly tested and evaluated using publicly available SAR image datasets, including the SAR-Ship-Dataset and HRSID dataset. In comparison to the baseline method YOLOv7-tiny, OE-YOLO exhibits superior performance with a lower parameter count. When compared with other commonly used deep learning-based detection methods, OE-YOLO demonstrates optimal performance and more accurate detection results. Full article
(This article belongs to the Special Issue Advances in Hyperspectral Data Processing)
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