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Advanced Artificial Intelligence Algorithm for the Analysis of Remote Sensing...
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Advanced Artificial Intelligence Algorithm for the Analysis of Remote Sensing Images (Third Edition)

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 2025) | Viewed by 3627

Special Issue Editors

Prof. Dr. Gangyao Kuang

E-Mail Website
Guest Editor
College of Electronic Science, National University of Defense Technology, Changsha 410073, China
Interests: remote sensing; SAR image processing; change detection; ground moving target indication; polarimetric SAR image classification
Special Issues, Collections and Topics in MDPI journals
Dr. Siqian Zhang

E-Mail
Guest Editor
College of Electronic Science, National University of Defense Technology, Changsha 410073, China
Interests: remote sensing; SAR image processing; SAR signal processing; object detection; image classification; feature extraction; simulation modeling
Special Issues, Collections and Topics in MDPI journals
Dr. Xin Su

E-Mail Website
Guest Editor
School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China
Interests: multitemporal SAR image processing; change detection; SAR image classification; object detection and tracking
Special Issues, Collections and Topics in MDPI journals
Dr. Olga Sykioti

E-Mail Website
Guest Editor
Senior Researcher Institute for Astronomy, Astrophysics, Space Applications and Remote Sensing, National Observatory of Athens, Vas. Pavlou and I. Metaxa, 15236 Penteli, Greece
Interests: remote sensing; multispectral/hyperspectral imaging; imaging spectroscopy; optical/SAR sensors; image processing; geology; lithological and mineral mapping; terrestrial surface mapping
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In the field of Earth observation, the massive amount of remote sensing data obtained by a large number of satellites in orbit or manned/unmanned aerial vehicles (UAVs) poses opportunities and challenges for the analysis of remote sensing images. Artificial intelligence is an emerging technology that is very suitable for big data applications. Therefore, how to interpret remote sensing images automatically, efficiently, and accurately is a hot and difficult topic in the research into and application of remote sensing technology. In recent years, artificial intelligence, especially deep learning techniques, has had a significant impact on the field of remote sensing, providing promising tools to overcome many challenging issues in the analysis of remote sensing images in terms of accuracy and reliability.

This is the third volume of the Special Issue of Remote Sensing on “Advanced Artificial Intelligence Algorithm for the Analysis of Remote Sensing Images”. In this Special Issue, we intend to compile a series of papers that merge the analysis and use of remote sensing images with AI techniques. We expect that new research will address practical problems in remote sensing image applications with the help of advanced AI methods.

Articles may address, but are not limited to, the following topics:

  • Advanced AI architectures for image classification;
  • Advanced AI-based target detection/recognition/tracking;
  • Change detection/semantic segmentation for remote sensing;
  • Multi-senor data fusion/multi-modal data analysis;
  • Image super-resolution/restoration for remote sensing;
  • Unsupervised/weakly supervised learning for image processing;
  • Advanced AI techniques for remote sensing applications;
  • Clustering (including classic and more advanced tools, such as subspace clustering, clustering ensemble, etc.);
  • Spectral unmixing, adopting either linear or non-linear models, using Bayesian or non-Bayesian approaches for parameter estimation;
  • Dimensionality reduction;
  • Data transformations.

Prof. Dr. Gangyao Kuang
Dr. Siqian Zhang
Dr. Xin Su
Dr. Olga Sykioti
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
  • image processing
  • target detection
  • change detection
  • data fusion
  • multispectral and hyperspectral images
  • synthetic aperture radar images
  • satellite video

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Published Papers (2 papers)

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Research

32 pages, 2219 KiB  
Article
SSFAN: A Compact and Efficient Spectral-Spatial Feature Extraction and Attention-Based Neural Network for Hyperspectral Image Classification
by Chunyang Wang, Chao Zhan, Bibo Lu, Wei Yang, Yingjie Zhang, Gaige Wang and Zongze Zhao
Remote Sens. 2024, 16(22), 4202; https://doi.org/10.3390/rs16224202 - 11 Nov 2024
Viewed by 1332
Abstract
Hyperspectral image (HSI) classification is a crucial technique that assigns each pixel in an image to a specific land cover category by leveraging both spectral and spatial information. In recent years, HSI classification methods based on convolutional neural networks (CNNs) and Transformers have [...] Read more.
Hyperspectral image (HSI) classification is a crucial technique that assigns each pixel in an image to a specific land cover category by leveraging both spectral and spatial information. In recent years, HSI classification methods based on convolutional neural networks (CNNs) and Transformers have significantly improved performance due to their strong feature extraction capabilities. However, these improvements often come with increased model complexity, leading to higher computational costs. To address this, we propose a compact and efficient spectral-spatial feature extraction and attention-based neural network (SSFAN) for HSI classification. The SSFAN model consists of three core modules: the Parallel Spectral-Spatial Feature Extraction Block (PSSB), the Scan Block, and the Squeeze-and-Excitation MLP Block (SEMB). After preprocessing the HSI data, it is fed into the PSSB module, which contains two parallel streams, each comprising a 3D convolutional layer and a 2D convolutional layer. The 3D convolutional layer extracts spectral and spatial features from the input hyperspectral data, while the 2D convolutional layer further enhances the spatial feature representation. Next, the Scan Block module employs a layered scanning strategy to extract spatial information at different scales from the central pixel outward, enabling the model to capture both local and global spatial relationships. The SEMB module combines the Spectral-Spatial Recurrent Block (SSRB) and the MLP Block. The SSRB, with its adaptive weight assignment mechanism in the SToken Module, flexibly handles time steps and feature dimensions, performing deep spectral and spatial feature extraction through multiple state updates. Finally, the MLP Block processes the input features through a series of linear transformations, GELU activation functions, and Dropout layers, capturing complex patterns and relationships within the data, and concludes with an argmax layer for classification. Experimental results show that the proposed SSFAN model delivers superior classification performance, outperforming the second-best method by 1.72%, 5.19%, and 1.94% in OA, AA, and Kappa coefficient, respectively, on the Indian Pines dataset. Additionally, it requires less training and testing time compared to other state-of-the-art deep learning methods. Full article
(This article belongs to the Special Issue Advanced Artificial Intelligence Algorithm for the Analysis of Remote Sensing Images (Third Edition))
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16 pages, 6200 KiB  
Communication
A Transformer-Unet Generative Adversarial Network for the Super-Resolution Reconstruction of DEMs
by Xin Zheng, Zhaoqi Xu, Qian Yin, Zelun Bao, Zhirui Chen and Sizhu Wang
Remote Sens. 2024, 16(19), 3676; https://doi.org/10.3390/rs16193676 - 1 Oct 2024
Cited by 2 | Viewed by 1144
Abstract
A new model called the Transformer-Unet Generative Adversarial Network (TUGAN) is proposed for super-resolution reconstruction of digital elevation models (DEMs). Digital elevation models are used in many fields, including environmental science, geology and agriculture. The proposed model uses a self-similarity Transformer (SSTrans) as [...] Read more.
A new model called the Transformer-Unet Generative Adversarial Network (TUGAN) is proposed for super-resolution reconstruction of digital elevation models (DEMs). Digital elevation models are used in many fields, including environmental science, geology and agriculture. The proposed model uses a self-similarity Transformer (SSTrans) as the generator and U-Net as the discriminator. SSTrans, a model that we previously proposed, can yield good reconstruction results in structurally complex areas but has little advantage when the surface is simple and smooth because too many additional details have been added to the data. To resolve this issue, we propose the novel TUGAN model, where U-Net is capable of multilayer jump connections, which enables the discriminator to consider both global and local information when making judgments. The experiments show that TUGAN achieves state-of-the-art results for all types of terrain details. Full article
(This article belongs to the Special Issue Advanced Artificial Intelligence Algorithm for the Analysis of Remote Sensing Images (Third Edition))
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