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Machine Learning Using Medium and High-Resolution Remote Sensing Datasets

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

Deadline for manuscript submissions: closed (31 March 2022) | Viewed by 19619

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Special Issue Editors

Dr. Katharine M. Johnson

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Guest Editor

North Carolina Institute for Climate Studies, North Carolina State University, Raleigh, NC, USA
Interests: human-environment land use dynamics; geographic information science; lidar.

Dr. Yuhan Rao

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Guest Editor

North Carolina Institute for Climate Studies, North Carolina State University, Raleigh, NC, USA
Interests: climate change; machine learning; environmental remote sensing

Dr. Jaime Zabalza

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Guest Editor

Department of Electronic and Electrical Engineering, University of Strathclyde, Glasgow G1 1XW, UK
Interests: signal and image processing; hyperspectral imaging; remote sensing; data mining; machine learning; artificial intelligence
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Giuseppe Modica

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Dipartimento di Scienze Veterinarie, Università degli Studi di Messina, Viale G. Palatucci s.n., 98168 Messina, 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, Collections and Topics 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 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 2700 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

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Published Papers (3 papers)

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Research

17 pages, 8721 KiB

Open AccessArticle

Mapping Relict Charcoal Hearths in New England Using Deep Convolutional Neural Networks and LiDAR Data

by Ji Won Suh, Eli Anderson, William Ouimet, Katharine M. Johnson and Chandi Witharana

Remote Sens. 2021, 13(22), 4630; https://doi.org/10.3390/rs13224630 - 17 Nov 2021

Cited by 16 | Viewed by 4085

Abstract

Advanced deep learning methods combined with regional, open access, airborne Light Detection and Ranging (LiDAR) data have great potential to study the spatial extent of historic land use features preserved under the forest canopy throughout New England, a region in the northeastern United States. Mapping anthropogenic features plays a key role in understanding historic land use dynamics during the 17th to early 20th centuries, however previous studies have primarily used manual or semi-automated digitization methods, which are time consuming for broad-scale mapping. This study applies fully-automated deep convolutional neural networks (i.e., U-Net) with LiDAR derivatives to identify relict charcoal hearths (RCHs), a type of historical land use feature. Results show that slope, hillshade, and Visualization for Archaeological Topography (VAT) rasters work well in six localized test regions (spatial scale: <1.5 km², best F1 score: 95.5%), but also at broader extents at the town level (spatial scale: 493 km², best F1 score: 86%). The model performed best in areas with deciduous forest and high slope terrain (e.g., >15 degrees) (F1 score: 86.8%) compared to coniferous forest and low slope terrain (e.g., <15 degrees) (F1 score: 70.1%). Overall, our results contribute to current methodological discussions regarding automated extraction of historical cultural features using deep learning and LiDAR. Full article

(This article belongs to the Special Issue Machine Learning Using Medium and High-Resolution Remote Sensing Datasets)

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20 pages, 5916 KiB

Open AccessArticle

Pixel- vs. Object-Based Landsat 8 Data Classification in Google Earth Engine Using Random Forest: The Case Study of Maiella National Park

by Andrea Tassi, Daniela Gigante, Giuseppe Modica, Luciano Di Martino and Marco Vizzari

Remote Sens. 2021, 13(12), 2299; https://doi.org/10.3390/rs13122299 - 11 Jun 2021

Cited by 76 | Viewed by 10810

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 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

(This article belongs to the Special Issue Machine Learning Using Medium and High-Resolution Remote Sensing Datasets)

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21 pages, 7529 KiB

Open AccessArticle

A Novel Query Strategy-Based Rank Batch-Mode Active Learning Method for High-Resolution Remote Sensing Image Classification

by Xin Luo, Huaqiang Du, Guomo Zhou, Xuejian Li, Fangjie Mao, Di’en Zhu, Yanxin Xu, Meng Zhang, Shaobai He and Zihao Huang

Remote Sens. 2021, 13(11), 2234; https://doi.org/10.3390/rs13112234 - 7 Jun 2021

Cited by 9 | Viewed by 2996

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 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

(This article belongs to the Special Issue Machine Learning Using Medium and High-Resolution Remote Sensing Datasets)

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