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Advanced Machine Learning Models for Remote Sensing Applications and Data Analysis—Recent Developments

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

Deadline for manuscript submissions: 31 July 2024 | Viewed by 888

Special Issue Editors


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Guest Editor
Department of Electrical, Computer and Biomedical Engineering, Toronto Metropolitan University, Toronto, ON M5B 2K3, Canada
Interests: signal processing; optimization; machine learning; remote sensing

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Guest Editor
Department of Electronic and Electrical Engineering, Strathclyde University, Glasgow G1 1XQ, UK
Interests: deep learning; polarimetric SAR; signal processing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Machine learning is being applied to remote sensing at a continuously increasing rate thanks to the rapid advancement in commercially available computational power, which has facilitated the development of advanced machine learning models, such as the deep learning models. Over the last ten years or so, deep learning models such as convolution neural networks, recurrent neural networks, generative adversarial networks and, recently, transformers have been widely used for different remote sensing applications. The development of these models has also benefited from the higher availability of publicly available remote sensing data, as the efficacy of these models depends upon the use of sufficient training data. In addition, some models have been developed that use a lower amount of data or can generate synthetic data to facilitate the training process. Machine and deep learning models have been developed for many different types of remote sensing data types, such as hyperspectral, optical, and imaging radar data. This development enables performance achievement in an automated manner that surpasses that of the traditional models on topics such as agriculture yield prediction, climate change and calamity detection and prediction, natural and man-made structure monitoring, etc. However, significant room for improvement remains in analyzing and improving the generalization ability of these models on actual test data that may differ from the training data. Another expected research direction is the use and analysis of quantum machine learning models in remote sensing. Given the increasing population and emerging climatic challenges, it is imperative to continue the development of advanced machine learning models for remote sensing.

This Special Issue is aimed at disseminating recent studies that develop new machine and deep learning models and their practical applications in remote sensing data for classification, modeling, change detection, time-series prediction, data quality improvement, etc. This topic directly falls within the scope of MDPI Remote Sensing, especially AI Remote Sensing.

Both review and original research articles are invited. This Special Issue is not only aimed at the applications of new deep learning and quantum machine learning methods to real remote sensing data, but its intended target is also novel applications and/or analyses of existing machine and deep learning models, including performance improvement with limited data, data fusion, and transfer learning. Intended application areas include, but are not limited to, land and ocean monitoring, climate and agriculture prediction, calamity prediction and assessment, structural monitoring, data post-processing for data quality improvement, etc.

Dr. Ahmed Shaharyar Khwaja
Dr. Filippo Biondi
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

  • deep learning
  • quantum machine learning
  • optical data
  • imaging radar data
  • multispectral and hyperspectral data
  • environment monitoring and prediction
  • calamity prediction and detection
  • agriculture monitoring and prediction
  • post-calamity evaluation
  • time-series for structural monitoring
  • transfer learning and one-shot learning
  • semi-supervised learning

Published Papers (2 papers)

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Research

19 pages, 3222 KiB  
Article
DiffuPrompter: Pixel-Level Automatic Annotation for High-Resolution Remote Sensing Images with Foundation Models
by Huadong Li, Ying Wei, Han Peng and Wei Zhang
Remote Sens. 2024, 16(11), 2004; https://0-doi-org.brum.beds.ac.uk/10.3390/rs16112004 - 2 Jun 2024
Viewed by 279
Abstract
Instance segmentation is pivotal in remote sensing image (RSI) analysis, aiding in many downstream tasks. However, annotating images with pixel-wise annotations is time-consuming and laborious. Despite some progress in automatic annotation, the performance of existing methods still needs improvement due to the high [...] Read more.
Instance segmentation is pivotal in remote sensing image (RSI) analysis, aiding in many downstream tasks. However, annotating images with pixel-wise annotations is time-consuming and laborious. Despite some progress in automatic annotation, the performance of existing methods still needs improvement due to the high precision requirements for pixel-level annotation and the complexity of RSIs. With the support of large-scale data, some foundational models have made significant progress in semantic understanding and generalization capabilities. In this paper, we delve deep into the potential of the foundational models in automatic annotation and propose a training-free automatic annotation method called DiffuPrompter, achieving pixel-level automatic annotation of RSIs. Extensive experimental results indicate that the proposed method can provide reliable pseudo-labels, significantly reducing the annotation costs of the segmentation task. Additionally, the cross-domain validation experiments confirm the powerful effectiveness of large-scale pseudo-data in improving model generalization performance. Full article
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27 pages, 43971 KiB  
Article
Spatiotemporal Mechanism-Based Spacetimeformer Network for InSAR Deformation Prediction and Identification of Retrogressive Thaw Slumps in the Chumar River Basin
by Jing Wang, Xiwei Fan, Zhijie Zhang, Xuefei Zhang, Wenyu Nie, Yuanmeng Qi and Nan Zhang
Remote Sens. 2024, 16(11), 1891; https://0-doi-org.brum.beds.ac.uk/10.3390/rs16111891 - 24 May 2024
Viewed by 341
Abstract
The increasing incidence of retrogressive thaw slumps (RTSs) in permafrost regions underscores the need for detailed spatial and temporal analysis using InSAR technology to monitor and predict dynamic changes in the process of RTSs. Nevertheless, current InSAR deformation forecasting methods employing deep learning [...] Read more.
The increasing incidence of retrogressive thaw slumps (RTSs) in permafrost regions underscores the need for detailed spatial and temporal analysis using InSAR technology to monitor and predict dynamic changes in the process of RTSs. Nevertheless, current InSAR deformation forecasting methods employing deep learning strategies such as the traditional long short-term memory (LSTM) and recent transformer models encounter difficulties in effectively capturing temporal features. Moreover, they are limited in their ability to directly integrate spatial information. In this paper, an innovative deep learning approach named Spacetimeformer is proposed for predicting medium- and short-term InSAR deformation of RTSs in the Chumar River area. This method employs a transformer architecture with a spatiotemporal attention mechanism, which enhances the long-term prediction capabilities of time series models and dynamic spatial modeling. It is applicable to multivariate InSAR spatiotemporal deformation prediction problems. The findings include a list of 72 RTSs compiled based on derived InSAR deformation maps and Sentinel-2 optical images, of which 64 have an average deformation rate exceeding 10 mm/year, indicating signs of permafrost degradation. The density distribution of the displacement maps predicted by the Spacetimeformer model aligned well with the InSAR deformation maps obtained from the small baseline subset (SBAS) method, with the overall prediction deviation controlled within 20 mm. In addition, the point-scale prediction results were compared with LSTM and transformer models. This study indicates that the Spacetimeformer network achieved good results in predicting the deformation of RTSs, with a root mean square error of 1.249 mm. The Spacetimeformer method for deformation prediction with the spacetime mechanism presented in this study can serve as a general framework for multivariate deformation prediction based on InSAR results. It can also quantitatively assess the spatial deformation characteristics and deformation trends of RTSs. Full article
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