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Remote Sensing and Data Integration
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Remote Sensing and Data Integration

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Remote Sensors".

Deadline for manuscript submissions: closed (31 December 2021) | Viewed by 7548

Special Issue Editors

Prof. Dr. Ismael Navas-Delgado

E-Mail Website
Guest Editor
Departamento de Lenguajes y Ciencias de la Computación. E.T.S. de Ingeniería Informática. University of Malaga, Malaga, Spain
Interests: databases; semantic web; linked data; data analysis; bioinformatics; precision agriculture; remote sensing; sensor data processing; systems biology artificial intelligence; algorithms; information systems
Prof. Dr. José Manuel García-Nieto

E-Mail Website
Guest Editor
Departamento de Lenguajes y Ciencias de la Computación, E.T.S. de Ingeniería Informática, ITIS Software, University of Malaga, 29071 Málaga, Spain
Interests: evolutionary computation; particle swarm optimization; machine learning; big data analysis
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Francisco Javier Mesas Carrascosa

E-Mail Website
Guest Editor
Department of Graphic and Geometric Engineering, University of Cordoba, 14071 Córdoba, Spain
Interests: UAV; remote sensing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Remote sensing is widely used nowadays in Earth observation processes, taking advantage of the different technologies being used in satellites. However, these data usually have low-medium spatial resolution and low temporal resolution, making the training of machine learning and deep learning algorithms a challenging task. So, these data need to be complemented with other data sources to enable better interpretations. Thus, public open data sources, as well as unmanned aerial systems and proximity sensors, are critical for the exploitation by integrating and analysing the collected data.

This Special Issue is addressed to the use of solutions to integrate remote sensing data with other data sources, in the context of Earth observation applications such as:

  • Ground, proximal and aerial remote sensing technologies for crop monitoring,
  • Urban and population dynamics characterization (locating new construction and settlements or building alterations, among others)
  • Forecasting, mapping and monitoring natural disasters
  • Land-use and land-cover monitoring and modeling for decision making,
  • Forest and vegetation dinamics monitoring and modeling,
  • Land surface water, atmosphere and environment dynamics, and many more.

 Topics of interest include, but are not limited to the following:

  • Remote sensors
  • Sensor technology and application
  • Internet of Things
  • Signal processing, data fusion and deep learning in sensor systems
  • Sensing systems
  • Localization and object tracking
  • Pixel and object based classification
  • Remote sensing ontologies
  • Remote sensing big data
  • Web Semantics
  • Multiscale data processing
  • Sustainability and climate change
  • Precision agriculture
  • Smart cities and urbanism

Prof. Ismael Navas-Delgado
Prof. José Manuel García-Nieto
Prof. Dr. Francisco Javier Mesas Carrascosa
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. Sensors 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 2600 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
  • data integration
  • linked data
  • machine learning
  • deep learning
  • earth observation

Published Papers (2 papers)

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Research

19 pages, 2783 KiB  
Article
Machine Learning Methods and Synthetic Data Generation to Predict Large Wildfires
by Fernando-Juan Pérez-Porras, Paula Triviño-Tarradas, Carmen Cima-Rodríguez, Jose-Emilio Meroño-de-Larriva, Alfonso García-Ferrer and Francisco-Javier Mesas-Carrascosa
Sensors 2021, 21(11), 3694; https://doi.org/10.3390/s21113694 - 26 May 2021
Cited by 22 | Viewed by 4549
Abstract
Wildfires are becoming more frequent in different parts of the globe, and the ability to predict when and where they will occur is a complex process. Identifying wildfire events with high probability of becoming a large wildfire is an important task for supporting [...] Read more.
Wildfires are becoming more frequent in different parts of the globe, and the ability to predict when and where they will occur is a complex process. Identifying wildfire events with high probability of becoming a large wildfire is an important task for supporting initial attack planning. Different methods, including those that are physics-based, statistical, and based on machine learning (ML) are used in wildfire analysis. Among the whole, those based on machine learning are relatively novel. In addition, because the number of wildfires is much greater than the number of large wildfires, the dataset to be used in a ML model is imbalanced, resulting in overfitting or underfitting the results. In this manuscript, we propose to generate synthetic data from variables of interest together with ML models for the prediction of large wildfires. Specifically, five synthetic data generation methods have been evaluated, and their results are analyzed with four ML methods. The results yield an improvement in the prediction power when synthetic data are used, offering a new method to be taken into account in Decision Support Systems (DSS) when managing wildfires. Full article
(This article belongs to the Special Issue Remote Sensing and Data Integration)
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23 pages, 15655 KiB  
Article
Pansharpening of WorldView-2 Data via Graph Regularized Sparse Coding and Adaptive Coupled Dictionary
by Wenqing Wang, Han Liu and Guo Xie
Sensors 2021, 21(11), 3586; https://doi.org/10.3390/s21113586 - 21 May 2021
Cited by 1 | Viewed by 1711
Abstract
The spectral mismatch between a multispectral (MS) image and its corresponding panchromatic (PAN) image affects the pansharpening quality, especially for WorldView-2 data. To handle this problem, a pansharpening method based on graph regularized sparse coding (GRSC) and adaptive coupled dictionary is proposed in [...] Read more.
The spectral mismatch between a multispectral (MS) image and its corresponding panchromatic (PAN) image affects the pansharpening quality, especially for WorldView-2 data. To handle this problem, a pansharpening method based on graph regularized sparse coding (GRSC) and adaptive coupled dictionary is proposed in this paper. Firstly, the pansharpening process is divided into three tasks according to the degree of correlation among the MS and PAN channels and the relative spectral response of WorldView-2 sensor. Then, for each task, the image patch set from the MS channels is clustered into several subsets, and the sparse representation of each subset is estimated through the GRSC algorithm. Besides, an adaptive coupled dictionary pair for each task is constructed to effectively represent the subsets. Finally, the high-resolution image subsets for each task are obtained by multiplying the estimated sparse coefficient matrix by the corresponding dictionary. A variety of experiments are conducted on the WorldView-2 data, and the experimental results demonstrate that the proposed method achieves better performance than the existing pansharpening algorithms in both subjective analysis and objective evaluation. Full article
(This article belongs to the Special Issue Remote Sensing and Data Integration)
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