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Remote Sensing
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Remote Sensing Data Sets II
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Remote Sensing Data Sets II

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Earth Observation Data".

Deadline for manuscript submissions: closed (25 March 2024) | Viewed by 813

Special Issue Editors

Dr. Jorge Vazquez

E-Mail Website
Guest Editor
Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA 91109, USA
Interests: validation of remote sensing data; application of remote sensing to coastal regions; development of new remote sensing for high resolution; validation of remote sensing data sets in challenging areas, including the arctic and coastal regions
Special Issues, Collections and Topics in MDPI journals
Dr. Dongdong Wang

E-Mail Website
Guest Editor
Institute of Remote Sensing and Geographic Information Systems, Peking University, Beijing, China
Interests: quantitative remote sensing; earth radiation budget; remote sensing data integration
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This is the 2nd Edition of the Special Issue “Remote Sensing Data Sets”.

Remote sensing has revolutionized the way we gather information on several aspects and phenomena of our planet, as it allows us to capture information beyond the reach of human observation, as well as monitor and analyze changes in our environment with unparalleled accuracy. Through the use of advanced technology and satellite imagery, remote sensing data sets have become invaluable tools for various applications across a wide array of fields, including environmental monitoring and disaster management.

This Special Issue aims at articles that address details and characteristics of remote sensing products, that could provide the user community with necessary information for making decisions on the appropriateness of products for specific applications and research problems.

Articles that address the general characteristics of the data sets and specific examples of applications are highly encouraged. Additionally, articles that focus on data quality issues and/or uncertainties are encouraged. Comparison papers that can help users make decisions on the suitability of remote sensing data sets for their applications/research needs are highly encouraged. Specific applications of Remote Sensing Data sets are also welcome.

Dr. Jorge Vazquez
Dr. Dongdong Wang
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.

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

Related Special Issue

Published Papers (1 paper)

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Research

16 pages, 9255 KiB  
Article
Weed Species Identification: Acquisition, Feature Analysis, and Evaluation of a Hyperspectral and RGB Dataset with Labeled Data
by Inbal Ronay, Ran Nisim Lati and Fadi Kizel
Remote Sens. 2024, 16(15), 2808; https://doi.org/10.3390/rs16152808 - 31 Jul 2024
Viewed by 387
Abstract
Site-specific weed management employs image data to generate maps through various methodologies that classify pixels corresponding to crop, soil, and weed. Further, many studies have focused on identifying specific weed species using spectral data. Nonetheless, the availability of open-access weed datasets remains limited. [...] Read more.
Site-specific weed management employs image data to generate maps through various methodologies that classify pixels corresponding to crop, soil, and weed. Further, many studies have focused on identifying specific weed species using spectral data. Nonetheless, the availability of open-access weed datasets remains limited. Remarkably, despite the extensive research employing hyperspectral imaging data to classify species under varying conditions, to the best of our knowledge, there are no open-access hyperspectral weed datasets. Consequently, accessible spectral weed datasets are primarily RGB or multispectral and mostly lack the temporal aspect, i.e., they contain a single measurement day. This paper introduces an open dataset for training and evaluating machine-learning methods and spectral features to classify weeds based on various biological traits. The dataset comprises 30 hyperspectral images, each containing thousands of pixels with 204 unique visible and near-infrared bands captured in a controlled environment. In addition, each scene includes a corresponding RGB image with a higher spatial resolution. We included three weed species in this dataset, representing different botanical groups and photosynthetic mechanisms. In addition, the dataset contains meticulously sampled labeled data for training and testing. The images represent a time series of the weed’s growth along its early stages, critical for precise herbicide application. We conducted an experimental evaluation to test the performance of a machine-learning approach, a deep-learning approach, and Spectral Mixture Analysis (SMA) to identify the different weed traits. In addition, we analyzed the importance of features using the random forest algorithm and evaluated the performance of the selected algorithms while using different sets of features. Full article
(This article belongs to the Special Issue Remote Sensing Data Sets II)
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