Special Issue "Machine Learning Using Medium and High-Resolution Remote Sensing Datasets"

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 March 2022.

Special Issue Editors

Dr. Katharine M. Johnson
E-Mail Website
Guest Editor
North Carolina Institute for Climate Studies, North Carolina State University, USA
Interests: human-environment land use dynamics; geographic information science; lidar.
Dr. Yuhan Rao
E-Mail Website
Guest Editor
North Carolina Institute for Climate Studies, North Carolina State University, USA
Interests: climate change; machine learning; environmental remote sensing.
Dr. Jaime Zabalza
E-Mail Website
Guest Editor
University of Strathclyde, Glasgow, UK
Interests: signal and image processing; hyperspectral imaging; remote sensing; data mining; machine learning; artificial intelligence.
Special Issues and Collections in MDPI journals
Prof. Dr. Giuseppe Modica
E-Mail Website
Guest Editor
Dipartimento di Agraria, Università degli Studi Mediterranea di Reggio Calabria, Località Feo di Vito, I-89122 Reggio Calabria, Italy
Interests: land cover and land use change dynamics; satellite and UAV remote sensing; landscape analysis and interpretation; remote sensing of vegetation; geographic object-based image analysis; machine learning.
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

Remote sensing data have become higher resolution and more accessible in recent years. The rapid developments in machine learning technologies (including deep learning) have led to innovative applications with remote sensing data, such as extracting information for environmental studies, fusing remote sensing data from different platforms for agriculture monitoring, and identifying features across landscapes (e.g., land cover/land use, historic cultural features of past land use, the vertical structure of forest). Although machine learning has shown great potential for remote sensing applications, the lack of high-quality training data, the explainability, and the reproducibility have limited the wide adoption of machine learning in remote sensing communities. Fortunately, recent efforts in building benchmark training data for remote sensing applications and explainable machine learning technologies are changing the landscape of machine learning applications in remote sensing communities. These recent developments in data and technologies have allowed for trustworthy applications of remote sensing data for solving pressing environmental issues. In this Special Issue, we aim to showcase innovative research using machine learning and remote sensing data in climate and environmental studies as well as human–landscape dynamics and the Anthropocene. Topics of interest include, but are not limited to:

  • climate informatics;
  • land use/land cover classification;
  • creating benchmark machine learning (ML) training data;
  • historic environmental data;
  • explainability of ML models for environmental studies;
  • automated feature extraction and/or classification.
  • Machine Learning applications in Google Earth Engine (GEE)

Dr. Katharine M. Johnson
Dr. Yuhan Rao
Dr. Jaime Zabalza
Prof. Dr. Giuseppe Modica
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

  • machine learning
  • climate
  • environment
  • land use/land cover classification
  • human–environment land use dynamics
  • automated feature extraction

Published Papers (2 papers)

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Research

Article
Pixel- vs. Object-Based Landsat 8 Data Classification in Google Earth Engine Using Random Forest: The Case Study of Maiella National Park
Remote Sens. 2021, 13(12), 2299; https://doi.org/10.3390/rs13122299 - 11 Jun 2021
Cited by 1 | Viewed by 1700
Abstract
With the general objective of producing a 2018–2020 Land Use/Land Cover (LULC) map of the Maiella National Park (central Italy), useful for a future long-term LULC change analysis, this research aimed to develop a Landsat 8 (L8) data composition and classification process using [...] Read more.
With the general objective of producing a 2018–2020 Land Use/Land Cover (LULC) map of the Maiella National Park (central Italy), useful for a future long-term LULC change analysis, this research aimed to develop a Landsat 8 (L8) data composition and classification process using Google Earth Engine (GEE). In this process, we compared two pixel-based (PB) and two object-based (OB) approaches, assessing the advantages of integrating the textural information in the PB approach. Moreover, we tested the possibility of using the L8 panchromatic band to improve the segmentation step and the object’s textural analysis of the OB approach and produce a 15-m resolution LULC map. After selecting the best time window of the year to compose the base data cube, we applied a cloud-filtering and a topography-correction process on the 32 available L8 surface reflectance images. On this basis, we calculated five spectral indices, some of them on an interannual basis, to account for vegetation seasonality. We added an elevation, an aspect, a slope layer, and the 2018 CORINE Land Cover classification layer to improve the available information. We applied the Gray-Level Co-Occurrence Matrix (GLCM) algorithm to calculate the image’s textural information and, in the OB approaches, the Simple Non-Iterative Clustering (SNIC) algorithm for the image segmentation step. We performed an initial RF optimization process finding the optimal number of decision trees through out-of-bag error analysis. We randomly distributed 1200 ground truth points and used 70% to train the RF classifier and 30% for the validation phase. This subdivision was randomly and recursively redefined to evaluate the performance of the tested approaches more robustly. The OB approaches performed better than the PB ones when using the 15 m L8 panchromatic band, while the addition of textural information did not improve the PB approach. Using the panchromatic band within an OB approach, we produced a detailed, 15-m resolution LULC map of the study area. Full article
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Article
A Novel Query Strategy-Based Rank Batch-Mode Active Learning Method for High-Resolution Remote Sensing Image Classification
Remote Sens. 2021, 13(11), 2234; https://doi.org/10.3390/rs13112234 - 07 Jun 2021
Viewed by 826
Abstract
An informative training set is necessary for ensuring the robust performance of the classification of very-high-resolution remote sensing (VHRRS) images, but labeling work is often difficult, expensive, and time-consuming. This makes active learning (AL) an important part of an image analysis framework. AL [...] Read more.
An informative training set is necessary for ensuring the robust performance of the classification of very-high-resolution remote sensing (VHRRS) images, but labeling work is often difficult, expensive, and time-consuming. This makes active learning (AL) an important part of an image analysis framework. AL aims to efficiently build a representative and efficient library of training samples that are most informative for the underlying classification task, thereby minimizing the cost of obtaining labeled data. Based on ranked batch-mode active learning (RBMAL), this paper proposes a novel combined query strategy of spectral information divergence lowest confidence uncertainty sampling (SIDLC), called RBSIDLC. The base classifier of random forest (RF) is initialized by using a small initial training set, and each unlabeled sample is analyzed to obtain the classification uncertainty score. A spectral information divergence (SID) function is then used to calculate the similarity score, and according to the final score, the unlabeled samples are ranked in descending lists. The most “valuable” samples are selected according to ranked lists and then labeled by the analyst/expert (also called the oracle). Finally, these samples are added to the training set, and the RF is retrained for the next iteration. The whole procedure is iteratively implemented until a stopping criterion is met. The results indicate that RBSIDLC achieves high-precision extraction of urban land use information based on VHRRS; the accuracy of extraction for each land-use type is greater than 90%, and the overall accuracy (OA) is greater than 96%. After the SID replaces the Euclidean distance in the RBMAL algorithm, the RBSIDLC method greatly reduces the misclassification rate among different land types. Therefore, the similarity function based on SID performs better than that based on the Euclidean distance. In addition, the OA of RF classification is greater than 90%, suggesting that it is feasible to use RF to estimate the uncertainty score. Compared with the three single query strategies of other AL methods, sample labeling with the SIDLC combined query strategy yields a lower cost and higher quality, thus effectively reducing the misclassification rate of different land use types. For example, compared with the Batch_Based_Entropy (BBE) algorithm, RBSIDLC improves the precision of barren land extraction by 37% and that of vegetation by 14%. The 25 characteristics of different land use types screened by RF cross-validation (RFCV) combined with the permutation method exhibit an excellent separation degree, and the results provide the basis for VHRRS information extraction in urban land use settings based on RBSIDLC. Full article
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