Special Issue "Advanced Machine Learning Techniques for High-Resolution Remote Sensing Data Analysis"

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

Deadline for manuscript submissions: 31 October 2021.

Special Issue Editors

Dr. Alireza Taravat
Website
Guest Editor
Deimos Space UK Ltd, Building R103, Fermi Avenue, Harwell, Oxford, OX11 0QR, UK
Interests: SAR image analysis; Optical remote sensing; Deep learning; Machine learning; Classification; Segmentation; Change detection; Precision farming; Forest management; Renewable energy; Ocean colour
Dr. Naoto Yokoya
Website
Guest Editor
RIKEN Center for Advanced Intelligence Project, Tokyo, 103-0027, Japan
Interests: Remote Sensing; Image Processing; Data Fusion; Machine Learning; Disaster Management
Special Issues and Collections in MDPI journals
Prof. Hongjun Su
Website
Guest Editor
School of Earth Science and Engineering, Hohai University; 8 Focheng West Road; Jiangning District; Nanjing 211100; China
Interests: Hyperspectral remote sensing; image analysis; machine learning; computational intelligence
Special Issues and Collections in MDPI journals
Prof. Cristina Rubio-Escudero
Website
Guest Editor
Department of Computer Languages and Systems, University of Sevillal, Avda. Reina Mercedes s/n 41012 Sevilla Spain
Interests: Data Science, Data Mining
Dr. Antonio Morales Esteban
Website
Guest Editor
Department of Building Structures and Geotechnical Engineering, Escuela Técnica Superior de Arquitectura (ETSA), Universidad de Sevilla (España), Av. Reina Mercedes 2. 41012, Sevilla (Spain)
Interests: Seismic engineering, dynamic analysis, geotechnics, artificial neural networks
Dr. José L. Amaro-Mellado
Website SciProfiles
Guest Editor
1. Instituto Geográfico Nacional (National Geographic Institute) of Spain, Andalusian Division, 41013, Seville Spain
2. Department of Graphic Engineering, University of Seville, 41012, Spain
Interests: Geomatics, GIS, geosciences, natural hazards, seismology, engineering education, remote sensing
Prof. Dr. Francisco Martínez-Álvarez
Website
Guest Editor
Prof. Ata Jahangir Moshayedi
Website
Guest Editor
School Of Information Engineering, Jiangxi University Of Science And Technology, China
Interests: Machine vision, Remote sensing
Prof. Biplab Banerjee
Website
Guest Editor
IIT Bombay, Powai, Mumbai. 400076, India
Interests: Deep Learning, Computer Vision
Dr. Mercedes E. Paoletti
Website SciProfiles
Guest Editor
Department of computer technology and communications, Polytechnic School of Cáceres, University of Extremadura (avenida de la Universidad s/n, 10003, Cáceres CÁCERES, Spain)
Interests: hyperspectral remote sensing; deep learning; Graphics Processing Units (GPUs); High Performance Computing (HPC) techniques
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

Current and future high-resolution satellite earth observation missions will provide data coverage that has never been available before and with a largely untapped potential. High-resolution hyperspectral and LiDAR sensors are also gaining attention as they become cheaper in operation and more suitable for use on UAVs or small satellites. This extends the traditional set of multispectral optical and SAR imagery to new fields of application. However, there is still a lack of advance models for manipulation and exploitation of new earth observation big data. On the other hand, imagery analytics and interpretation, which are often still performed by human experts, require an increase in the level of automation in the process of value-added generation from data. Hence, powerful data mining algorithms are required to mine useful information. Even with so much literature devoted to this topic, there is still so much we do not know about machine learning models in the remote sensing field. This Special Issue aims to foster the application of advanced machine learning and deep learning algorithms to remote sensing problems. The scope is broad, but contributions with a sufficiently specific focus are preferred.

For this Special Issue, we welcome contributions related to:

  • Understanding of advanced ML and DL architecture for Earth Observation data analysis;
  • Transfer learning, cross-sensor learning;
  • DL model fusion;
  • Advanced ML models for high-resolution RS image segmentation and classification;
  • High-resolution RS data fusion (Optical, SAR, and LiDAR) using ML models;
  • High-resolution RS time-series analysis using ML and DL models.
Dr. Alireza Taravat
Dr. Naoto Yokoya
Prof. Jon Atli Benediktsson
Prof. Hongjun Su
Prof. Cristina Rubio-Escudero
Dr. Antonio Morales Esteban
Dr. José L. Amaro-Mellado
Prof. Francisco Martínez-Álvarez
Prof. Ata Jahangir Moshayedi
Prof. Biplab Banerjee
Ms. Mercedes Paoletti
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 papers will be 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 2200 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

  • Remote sensing
  • Deep learning
  • Machine learning
  • Image processing
  • Transfer learning
  • Automatic onboard processing
  • Convolutional neural network
  • Recurrent neural network

Published Papers (3 papers)

Order results
Result details
Select all
Export citation of selected articles as:

Research

Open AccessArticle
Extreme Gradient Boosting Model for Rain Retrieval using Radar Reflectivity from Various Elevation Angles
Remote Sens. 2020, 12(14), 2203; https://doi.org/10.3390/rs12142203 - 09 Jul 2020
Abstract
The purpose of this study was to develop an optimal estimation model for rainfall rate retrievals using radar reflectivity, thereby gaining an effective grasp of rainfall information for disaster prevention uses. A process was designed for evaluating the optimal retrieval models using various [...] Read more.
The purpose of this study was to develop an optimal estimation model for rainfall rate retrievals using radar reflectivity, thereby gaining an effective grasp of rainfall information for disaster prevention uses. A process was designed for evaluating the optimal retrieval models using various dataset combinations with radar reflectivity and ground meteorological attributes. Various ground meteorological attributes (such as relative humidity, wind speed, precipitation, etc.) were obtained using the land-based weather stations affiliated with Taiwan’s Central Weather Bureau (CWB). This study used nine radar reflectivity provided by the Hualien weather surveillance radar station’s Volume Cover Pattern 21 system. The developed models are built using multiple machine learning algorithms, including linear regression (REG), support vector regression (SVR), and extreme gradient boosting (XGBoost), in addition to the Marshall–Palmer formula (MP). The study examined 14 typhoons that occurred from 2008 to 2017 at Chenggong station in southeast Taiwan, and Lanyu station in the outlying islands, and the top four major rainfall events were designated as test typhoons—Nanmadol (2011), Tembin (2012), Matmo (2014), and Nepartak (2016). The results indicated that for rainfall retrievals, radar reflectivity at a scanning (elevation) angle of 6.0° combined with ground meteorological attributes were the optimal input variables for the Chenggong station, whereas radar reflectivity at an elevation angle of 4.3° combined with ground meteorological attributes were optimal for the Lanyu station. In terms of model performance, XGBoost models had the lowest error index at Chenggong and Lanyu stations compared with MP, REG, and SVR models. XGBoost models at Lanyu station had the highest efficiency coefficient (0.903), and those at Chenggong station had the second highest (0.885). As a result, pairing the combination of optimal radar reflectivity and ground meteorological attributes, as verified by the evaluation process, with a high-efficiency algorithm (XGBoost) can effectively increase the accuracy of rainfall retrieval during typhoons. Full article
Show Figures

Graphical abstract

Open AccessArticle
How Well Do Deep Learning-Based Methods for Land Cover Classification and Object Detection Perform on High Resolution Remote Sensing Imagery?
Remote Sens. 2020, 12(3), 417; https://doi.org/10.3390/rs12030417 - 28 Jan 2020
Cited by 5
Abstract
Land cover information plays an important role in mapping ecological and environmental changes in Earth’s diverse landscapes for ecosystem monitoring. Remote sensing data have been widely used for the study of land cover, enabling efficient mapping of changes of the Earth surface from [...] Read more.
Land cover information plays an important role in mapping ecological and environmental changes in Earth’s diverse landscapes for ecosystem monitoring. Remote sensing data have been widely used for the study of land cover, enabling efficient mapping of changes of the Earth surface from Space. Although the availability of high-resolution remote sensing imagery increases significantly every year, traditional land cover analysis approaches based on pixel and object levels are not optimal. Recent advancement in deep learning has achieved remarkable success on image recognition field and has shown potential in high spatial resolution remote sensing applications, including classification and object detection. In this paper, a comprehensive review on land cover classification and object detection approaches using high resolution imagery is provided. Through two case studies, we demonstrated the applications of the state-of-the-art deep learning models to high spatial resolution remote sensing data for land cover classification and object detection and evaluated their performances against traditional approaches. For a land cover classification task, the deep-learning-based methods provide an end-to-end solution by using both spatial and spectral information. They have shown better performance than the traditional pixel-based method, especially for the categories of different vegetation. For an objective detection task, the deep-learning-based object detection method achieved more than 98% accuracy in a large area; its high accuracy and efficiency could relieve the burden of the traditional, labour-intensive method. However, considering the diversity of remote sensing data, more training datasets are required in order to improve the generalisation and the robustness of deep learning-based models. Full article
Show Figures

Graphical abstract

Open AccessArticle
Hybrid Computational Intelligence Models for Improvement Gully Erosion Assessment
Remote Sens. 2020, 12(1), 140; https://doi.org/10.3390/rs12010140 - 01 Jan 2020
Cited by 9
Abstract
Gullying is a type of soil erosion that currently represents a major threat at the societal scale and will likely increase in the future. In Iran, soil erosion, and specifically gullying, is already causing significant distress to local economies by affecting agricultural productivity [...] Read more.
Gullying is a type of soil erosion that currently represents a major threat at the societal scale and will likely increase in the future. In Iran, soil erosion, and specifically gullying, is already causing significant distress to local economies by affecting agricultural productivity and infrastructure. Recognizing this threat has recently led the Iranian geomorphology community to focus on the problem across the whole country. This study is in line with other efforts where the optimal method to map gully-prone areas is sought by testing state-of-the-art machine learning tools. In this study, we compare the performance of three machine learning algorithms, namely Fisher’s linear discriminant analysis (FLDA), logistic model tree (LMT) and naïve Bayes tree (NBTree). We also introduce three novel ensemble models by combining the aforementioned base classifiers to the Random SubSpace (RS) meta-classifier namely RS-FLDA, RS-LMT and RS-NBTree. The area under the receiver operating characteristic (AUROC), true skill statistics (TSS) and kappa criteria are used for calibration (goodness-of-fit) and validation (prediction accuracy) datasets to compare the performance of the different algorithms. In addition to susceptibility mapping, we also study the association between gully erosion and a set of morphometric, hydrologic and thematic properties by adopting the evidential belief function (EBF). The results indicate that hydrology-related factors contribute the most to gully formation, which is also confirmed by the susceptibility patterns displayed by the RS-NBTree ensemble. The RS-NBTree is the model that outperforms the other five models, as indicated by the prediction accuracy (area under curve (AUC) = 0.898, Kappa = 0.748 and TSS = 0.697), and goodness-of-fit (AUC = 0.780, Kappa = 0.682 and TSS = 0.618). The analyses are performed with the same gully presence/absence balanced modeling design. Therefore, the differences in performance are dependent on the algorithm architecture. Overall, the EBF model can detect strong and reasonable dependencies towards gully-prone conditions. The RS-NBTree ensemble model performed significantly better than the others, suggesting greater flexibility towards unknown data, which may support the applications of these methods in transferable susceptibility models in areas that are potentially erodible but currently lack gully data. Full article
Show Figures

Graphical abstract

Back to TopTop