Special Issue "Machine Learning for Remote Sensing Image/Signal 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: 31 December 2021.

Special Issue Editors

Dr. Pedro Latorre-Carmona
E-Mail Website
Guest Editor
Department of Computer Engineering, University of Burgos, Avda Cantabria s/n, 09006 Burgos, Spain
Interests: multispectral; colour and grey scale image processing; colorimetry; vision physics; pattern recognition
Special Issues and Collections in MDPI journals
Prof. Dr. Antonio J. Plaza
E-Mail Website
Guest Editor
Department of Technology of Computers and Communications, University of Extremadura, Escuela Politécnica de Cáceres, Avenida de la Universidad s/n, 10003 Cáceres, Spain
Interests: hyperspectral remote sensing; high performance computing; graphics processing units

Special Issue Information

Dear Colleagues,

Machine learning techniques have been applied in remote sensing for more than 20 years now. We are, however, experiencing an explosion of new capabilities and application areas where machine learning in remote sensing is playing and will continue to play a capital role. In particular, new sensors with ever increasing capabilities and new computing hardware and software capabilities are allowing us to tackle problems that were considerably difficult to approach just a few years ago.

This Special Issue is aimed at presenting new machine learning techniques and new application areas in remote sensing. We particularly welcome papers focused on, although not limited to, one or more of the following topics:

  • Deep learning techniques for remote sensing
  • Machine learning techniques for inference and retrieval of bio–geo–physical variables
  • Machine learning for remote sensing data classification and regression
  • Multi-temporal and multi-sensor data fusion, assimilation, and processing
  • Machine learning platforms for big data and highly demanding remote sensing applications
  • Machine learning for multispectral and hyperspectral remote sensing platforms and applications
  • Machine learning for uncertainty analysis and assessment in remote sensing
  • Machine learning for remote sensing estimation and characterization of highly variable and dynamic earth processes

We would like this Special Issue to become an example of the most up-to-date machine learning approaches used to solve some of the problems considered by the remote sensing community.

Prof. Dr. Pedro Latorre-Carmona
Prof. Dr. Antonio J. Plaza
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 2400 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

  • deep learning
  • inference and retrieval
  • classification
  • data fusion
  • high performance computing
  • multispectral and hyperspectral data processing
  • uncertainty analysis and assessment
  • dynamic earth processes

Published Papers (2 papers)

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Research

Article
Improved Transformer Net for Hyperspectral Image Classification
Remote Sens. 2021, 13(11), 2216; https://doi.org/10.3390/rs13112216 - 05 Jun 2021
Viewed by 955
Abstract
In recent years, deep learning has been successfully applied to hyperspectral image classification (HSI) problems, with several convolutional neural network (CNN) based models achieving an appealing classification performance. However, due to the multi-band nature and the data redundancy of the hyperspectral data, the [...] Read more.
In recent years, deep learning has been successfully applied to hyperspectral image classification (HSI) problems, with several convolutional neural network (CNN) based models achieving an appealing classification performance. However, due to the multi-band nature and the data redundancy of the hyperspectral data, the CNN model underperforms in such a continuous data domain. Thus, in this article, we propose an end-to-end transformer model entitled SAT Net that is appropriate for HSI classification and relies on the self-attention mechanism. The proposed model uses the spectral attention mechanism and the self-attention mechanism to extract the spectral–spatial features of the HSI image, respectively. Initially, the original HSI data are remapped into multiple vectors containing a series of planar 2D patches after passing through the spectral attention module. On each vector, we perform linear transformation compression to obtain the sequence vector length. During this process, we add the position–coding vector and the learnable–embedding vector to manage capturing the continuous spectrum relationship in the HSI at a long distance. Then, we employ several multiple multi-head self-attention modules to extract the image features and complete the proposed network with a residual network structure to solve the gradient dispersion and over-fitting problems. Finally, we employ a multilayer perceptron for the HSI classification. We evaluate SAT Net on three publicly available hyperspectral datasets and challenge our classification performance against five current classification methods employing several metrics, i.e., overall and average classification accuracy and Kappa coefficient. Our trials demonstrate that SAT Net attains a competitive classification highlighting that a Self-Attention Transformer network and is appealing for HSI classification. Full article
(This article belongs to the Special Issue Machine Learning for Remote Sensing Image/Signal Processing)
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Article
Rice-Yield Prediction with Multi-Temporal Sentinel-2 Data and 3D CNN: A Case Study in Nepal
Remote Sens. 2021, 13(7), 1391; https://doi.org/10.3390/rs13071391 - 04 Apr 2021
Cited by 1 | Viewed by 1504
Abstract
Crop yield estimation is a major issue of crop monitoring which remains particularly challenging in developing countries due to the problem of timely and adequate data availability. Whereas traditional agricultural systems mainly rely on scarce ground-survey data, freely available multi-temporal and multi-spectral remote [...] Read more.
Crop yield estimation is a major issue of crop monitoring which remains particularly challenging in developing countries due to the problem of timely and adequate data availability. Whereas traditional agricultural systems mainly rely on scarce ground-survey data, freely available multi-temporal and multi-spectral remote sensing images are excellent tools to support these vulnerable systems by accurately monitoring and estimating crop yields before harvest. In this context, we introduce the use of Sentinel-2 (S2) imagery, with a medium spatial, spectral and temporal resolutions, to estimate rice crop yields in Nepal as a case study. Firstly, we build a new large-scale rice crop database (RicePAL) composed by multi-temporal S2 and climate/soil data from the Terai districts of Nepal. Secondly, we propose a novel 3D Convolutional Neural Network (CNN) adapted to these intrinsic data constraints for the accurate rice crop yield estimation. Thirdly, we study the effect of considering different temporal, climate and soil data configurations in terms of the performance achieved by the proposed approach and several state-of-the-art regression and CNN-based yield estimation methods. The extensive experiments conducted in this work demonstrate the suitability of the proposed CNN-based framework for rice crop yield estimation in the developing country of Nepal using S2 data. Full article
(This article belongs to the Special Issue Machine Learning for Remote Sensing Image/Signal Processing)
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