Special Issue "Artificial Intelligence and Machine Learning for multi-source Remote Sensing"

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

Deadline for manuscript submissions: 20 December 2022 | Viewed by 1712

Special Issue Editors

Prof. Dr. Silvia Liberata Ullo
E-Mail Website
Guest Editor
Department of Engineering (DING), University of Sannio, Benevento, Italy
Interests: remote sensing; data analysis; elaboration of satellite data for earth observation; machine learning applied to satellite images; sensor networks
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Parameshachari Bidare Divakarachari
E-Mail Website
Guest Editor
GSSS Institute of Engg & Technology for Women, Mysuru, Karnataka 570016, India
Interests: image processing; pattern; recognition; data science; IOT
Prof. Dr. Pia Addabbo
E-Mail Website
Guest Editor
Department of Engineering (DING), University of Sannio, Benevento, Italy
Interests: statistical signal processing; image processing; passive remote sensing (hyperspectral sensor); active remote sensing (RADAR, SAR, GNSS-R)
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Recently, artificial intelligence and machine learning have gained attention and have been used to achieve great success in both the research community and industry, especially in the field of multi-source remote sensing. Due to the recent progress in machine learning, particularly in deep learning, many techniques for image analysis and understanding have been applied to solve real problems, and deep learning has been widely used to perform other computer vision tasks, such as video classification and image super-resolution. Learning an effective feature representation from a large amount of data through artificial intelligence techniques is useful for extracting the underlying structural features of the data even when a small amount of data is available. It also results in a better representation than hand-crafted features as the features learned through artificial intelligence techniques adapt well to the tasks at hand.

Currently, massive streams of Earth Observation data are being systematically collected from different cutting-edge optical and radar sensors, on-board satellites, and aerial and terrestrial platforms. These data include both images and video sequences at different spatial, spectral, and temporal resolutions and can be used to constantly monitor the Earth's surface. In order to fully exploit these datasets and deliver crucial information for numerous engineering, environmental, safety, and security applications, novel artificial intelligence and machine learning methods are required that will enable us to efficiently dissect and interpret the data and draw conclusions that the broader public can turn into action.

The scope of this Special Issue is interdisciplinary and seeks collaborative contributions from academia and industrial experts in the areas of geoscience and remote sensing, signal processing, computer vision, machine learning, and data science.

Prof. Dr. Silvia Liberata Ullo
Prof. Dr. Parameshachari Bidare Divakarachari
Prof. Dr. Pia Addabbo
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 2500 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

  • transfer learning and statistical learning methods for image classification
  • multispectral imaging and deep neural networks for precision farming
  • computer vision using deep convolutional networks for spatio-temporal remote sensing applications
  • automatic building segmentation using multi-constraint convolutional networks
  • urban land use mapping and analysis in the big data era
  • deep learning techniques for remote sensing image classification
  • detecting pipeline pathways in satellite images with deep learning
  • marine vision-based situational awareness using deep learning
  • computer vision for automatic ship detection in remote sensing images
  • hyper-spectral image classification using similarity-measurement-based recurrent neural networks
  • unsupervised deep feature extraction for remote sensing image classification

Published Papers (1 paper)

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Research

Article
Merging Multisatellite and Gauge Precipitation Based on Geographically Weighted Regression and Long Short-Term Memory Network
Remote Sens. 2022, 14(16), 3939; https://doi.org/10.3390/rs14163939 - 13 Aug 2022
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Abstract
To generate high-quality spatial precipitation estimates, merging rain gauges with a single-satellite precipitation product (SPP) is a common approach. However, a single SPP cannot capture the spatial pattern of precipitation well, and its resolution is also too low. This study proposed an integrated [...] Read more.
To generate high-quality spatial precipitation estimates, merging rain gauges with a single-satellite precipitation product (SPP) is a common approach. However, a single SPP cannot capture the spatial pattern of precipitation well, and its resolution is also too low. This study proposed an integrated framework for merging multisatellite and gauge precipitation. The framework integrates the geographically weighted regression (GWR) for improving the spatial resolution of precipitation estimations and the long short-term memory (LSTM) network for improving the precipitation estimation accuracy by exploiting the spatiotemporal correlation pattern between multisatellite precipitation products and rain gauges. Specifically, the integrated framework was applied to the Han River Basin of China for generating daily precipitation estimates from the data of both rain gauges and four SPPs (TRMM_3B42, CMORPH, PERSIANN-CDR, and GPM-IMAGE) during the period of 2007–2018. The results show that the GWR-LSTM framework significantly improves the spatial resolution and accuracy of precipitation estimates (resolution of 0.05°, correlation coefficient of 0.86, and Kling–Gupta efficiency of 0.6) over original SPPs (resolution of 0.25° or 0.1°, correlation coefficient of 0.36–0.54, Kling–Gupta efficiency of 0.30–0.52). Compared with other methods, the correlation coefficient for the whole basin is improved by approximately 4%. Especially in the lower reaches of the Han River, the correlation coefficient is improved by 15%. In addition, this study demonstrates that merging multiple-satellite and gauge precipitation is much better than merging partial products of multiple satellite with gauge observations. Full article
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