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The Age of Big Data: AI Technology for Remote Sensing Image Processing & Application

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

Deadline for manuscript submissions: 20 July 2024 | Viewed by 2205

Special Issue Editors

Dr. Ligou Weng

E-Mail Website
Guest Editor
School of Automation, Nanjing University of Information Science and Technology, Nanjing, China
Interests: big data; machine learning; remote sensing
Prof. Dr. Min Xia

E-Mail Website
Guest Editor
School of Automation, Nanjing University of Information Science and Technology, 219 Ningliu Rd., Nanjing 210044, China
Interests: deep learning; remote sensing image analysis; change detection; semantic analysis; image segmentation
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Guojie Wang

E-Mail Website
Guest Editor
School of Remote Sensing and Goematics Engineering, Nanjing University of Information Science and Technology, 219 Ningliu Rd., Nanjing 210044, China
Interests: land atmosphere interaction; climate change impacts; disaster risk management; artificial intelligence remote sensing; change detection; remote sensing of resources and environment; remote sensing of disasters
Dr. Zheng Duan

E-Mail Website1 Website2
Guest Editor
Department of Physical Geography and Ecosystem Science, Lund University, S-22362 Lund, Sweden
Interests: satellite remote sensing; hydrological modelling; water balance of lakes and reservoirs; glacier modelling; ecosystem modelling; machine learning in remote sensing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We are thrilled to announce a forthcoming Special Issue entitled “The Age of Big Data: AI Technology for Remote Sensing Image Processing & Application” in Remote Sensing. This issue aims to spotlight and address the emergent complexities and potential that big data brings to the field of remote sensing.

The Special Issue will focus on an array of areas encompassing the latest research and advancements in deep learning, artificial intelligence, and machine learning, with specific attention towards their applications in remote sensing image analysis, image segmentation, semantic analysis, and change detection.

Climate change impacts and disaster risk management will be another central theme. We look forward to exploring how remote sensing technologies can monitor and mitigate these global challenges through the study of land–atmosphere interactions, disaster prediction and response, and the assessment of resources and environments.

Water and ecosystem modelling will also be focal points. Contributions investigating hydrological modelling, the water balance of lakes and reservoirs, glacier modelling, and ecosystem modelling using remote sensing data are encouraged.

The Special Issue aspires to create a platform to share and discuss these critical topics and their implications. We welcome submissions that are at the intersection of satellite remote sensing and big data analytics, bridging our understanding of the Earth's dynamic systems with the power of emerging computational techniques.

Dr. Ligou Weng
Prof. Dr. Min Xia
Prof. Dr. Guojie Wang
Dr. Zheng Duan
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

  • remote sensing semantic analysis
  • remote sensing image change detection
  • remote sensing image classification
  • geospatial object recognition and localization
  • land use
  • deep learning
  • remote sensing big data analysis

Published Papers (2 papers)

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Research

20 pages, 10682 KiB  
Article
Identifying Winter Wheat Using Landsat Data Based on Deep Learning Algorithms in the North China Plain
by Qixia Zhang, Guofu Wang, Guojie Wang, Weicheng Song, Xikun Wei and Yifan Hu
Remote Sens. 2023, 15(21), 5121; https://doi.org/10.3390/rs15215121 - 26 Oct 2023
Cited by 1 | Viewed by 985
Abstract
The North China Plain (NCP) represents a significant agricultural production region in China, with winter wheat serving as one of its main grain crops. Accurate identification of winter wheat through remote sensing technology holds significant importance in ensuring food security in the NCP. [...] Read more.
The North China Plain (NCP) represents a significant agricultural production region in China, with winter wheat serving as one of its main grain crops. Accurate identification of winter wheat through remote sensing technology holds significant importance in ensuring food security in the NCP. In this study, we have utilized Landsat 8 and Landsat 9 imagery to identify winter wheat in the NCP. Multiple convolutional neural networks (CNNs) and transformer networks, including ResNet, HRNet, MobileNet, Xception, Swin Transformer and SegFormer, are used in order to understand their uncertainties in identifying winter wheat. At the same time, these deep learning (DL) methods are also compared to the traditional random forest (RF) method. The results indicated that SegFormer outperformed all methods, of which the accuracy is 0.9252, the mean intersection over union (mIoU) is 0.8194 and the F1 score (F1) is 0.8459. These DL methods were then applied to monitor the winter wheat planting areas in the NCP from 2013 to 2022, and the results showed a decreasing trend. Full article
(This article belongs to the Special Issue The Age of Big Data: AI Technology for Remote Sensing Image Processing & Application)
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25 pages, 1802 KiB  
Article
MSFANet: Multi-Scale Strip Feature Attention Network for Cloud and Cloud Shadow Segmentation
by Kai Chen, Xin Dai, Min Xia, Liguo Weng, Kai Hu and Haifeng Lin
Remote Sens. 2023, 15(19), 4853; https://doi.org/10.3390/rs15194853 - 07 Oct 2023
Cited by 8 | Viewed by 851
Abstract
Cloud and cloud shadow segmentation is one of the most critical challenges in remote sensing image processing. Because of susceptibility to factors such as disturbance from terrain features and noise, as well as a poor capacity to generalize, conventional deep learning networks, when [...] Read more.
Cloud and cloud shadow segmentation is one of the most critical challenges in remote sensing image processing. Because of susceptibility to factors such as disturbance from terrain features and noise, as well as a poor capacity to generalize, conventional deep learning networks, when directly used to cloud and cloud shade detection and division, have a tendency to lose fine features and spatial data, leading to coarse segmentation of cloud and cloud shadow borders, false detections, and omissions of targets. To address the aforementioned issues, a multi-scale strip feature attention network (MSFANet) is proposed. This approach uses Resnet18 as the backbone for obtaining semantic data at multiple levels. It incorporates a particular attention module that we name the deep-layer multi-scale pooling attention module (DMPA), aimed at extracting multi-scale contextual semantic data, deep channel feature information, and deep spatial feature information. Furthermore, a skip connection module named the boundary detail feature perception module (BDFP) is introduced to promote information interaction and fusion between adjacent layers of the backbone network. This module performs feature exploration on both the height and width dimensions of the characteristic pattern to enhance the recovery of boundary detail intelligence of the detection targets. Finally, during the decoding phase, a self-attention module named the cross-layer self-attention feature fusion module (CSFF) is employed to direct the aggregation of deeplayer semantic feature and shallow detail feature. This approach facilitates the extraction of feature information to the maximum extent while conducting image restoration. The experimental outcomes unequivocally prove the efficacy of our network in effectively addressing complex cloud-covered scenes, showcasing good performance across the cloud and cloud shadow datasets, the HRC_WHU dataset, and the SPARCS dataset. Our model outperforms existing methods in terms of segmentation accuracy, underscoring its paramount importance in the field of cloud recognition research. Full article
(This article belongs to the Special Issue The Age of Big Data: AI Technology for Remote Sensing Image Processing & Application)
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