Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
7.9
5.0
Journals
Remote Sensing
Special Issues
Advanced Artificial Intelligence and Deep Learning for Remote Sensing II
remotesensing-logo
Submit to Special Issue Submit Abstract to Special Issue Review for Remote Sensing Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

Advanced Artificial Intelligence and Deep Learning for Remote Sensing II

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

Deadline for manuscript submissions: 25 July 2024 | Viewed by 3593

Special Issue Editors

Prof. Dr. Zhenming Peng

E-Mail Website
Guest Editor
School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China
Interests: object detection; remote sensing and scene perception; image processing
Prof. Dr. Zhengzhou Li

E-Mail Website
Guest Editor
College of Microelectronics and Communication Engineering, Chongqing University, Chongqing 401331, China
Interests: radar signal detection; target detection and recognition; radar system

Special Issue Information

Dear Colleagues,

Remote sensing is a fundamental tool for looking at the world from afar. The development of artificial intelligence(AI) and deep learning (DL) applications has paved the way for new research opportunities in various fields such as remote sensing, which uses Earth observation, disaster warning, and environmental monitoring. In recent years, with the continuous development of remote sensing technologies, especially the continuous emergence of different detection sensors and new detection systems, and the continuous accumulation of historical data and samples, it is possible to use AI and DL for big data training, and the field has become a research hotspot.

This Special Issue aims to report the latest advances and trends concerning the advanced AI and DL techniques applied to remote sensing data processing issues. Papers of both theoretical and applicative nature, as well as contributions regarding new AI and DL techniques for the remote sensing research community, are welcome. For this Special Issue, we invite experts and scholars in the field to contribute to the latest research progress of AI and DL in the fields of Earth observation, disaster warning, surface multi-temporal changes, environmental remote sensing, optical remote sensing and different sensor detection and imaging, so as to further promote the technological progress in this field.

The topic includes but is not limited to:

  • Object detection in high-resolution remote sensing imagery.
  • SAR object detection, scene classification.
  • Targets oriented multi temporal change detection.
  • Infrared target detection and recognition.
  • LiDAR point cloud data processing and scenes reconstruction.
  • UAV remote sensing and scenes perception.
  • Big data mining in remote sensing.
  • Interpretable deep learning in remote sensing.

Prof. Dr. Zhenming Peng
Prof. Dr. Zhengzhou Li
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

  • objects detection
  • artificial intelligence
  • deep learning
  • scene reconstruction
  • scene perception
  • data mining
  • change detection
  • object recognition

Related Special Issue

Published Papers (3 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

Jump to: Other

22 pages, 3618 KiB  
Article
An Integrated Detection and Multi-Object Tracking Pipeline for Satellite Video Analysis of Maritime and Aerial Objects
by Zhijuan Su, Gang Wan, Wenhua Zhang, Ningbo Guo, Yitian Wu, Jia Liu, Dianwei Cong, Yutong Jia and Zhanji Wei
Remote Sens. 2024, 16(4), 724; https://doi.org/10.3390/rs16040724 - 19 Feb 2024
Cited by 1 | Viewed by 644
Abstract
Optical remote sensing videos, as a new source of remote sensing data that has emerged in recent years, have significant potential in remote sensing applications, especially national defense. In this paper, a tracking pipeline named TDNet (tracking while detecting based on a neural [...] Read more.
Optical remote sensing videos, as a new source of remote sensing data that has emerged in recent years, have significant potential in remote sensing applications, especially national defense. In this paper, a tracking pipeline named TDNet (tracking while detecting based on a neural network) is proposed for optical remote sensing videos based on a correlation filter and deep neural networks. The pipeline is used to simultaneously track ships and planes in videos. There are many target tracking methods for general video data, but they suffer some difficulties in remote sensing videos with low resolution and those influenced by weather conditions. The tracked targets are usually misty. Therefore, in TDNet, we propose a new multi-target tracking method called MT-KCF and a detecting-assisted tracking (i.e., DAT) module to improve tracking accuracy and precision. Meanwhile, we also design a new target recognition (i.e., NTR) module to recognise newly emerged targets. In order to verify the performance of TDNet, we compare our method with several state-of-the-art tracking methods on optical video remote sensing data sets acquired from the Jilin No. 1 satellite. The experimental results demonstrate the effectiveness and the state-of-the-art performance of the proposed method. The proposed method can achieve more than 90% performance in terms of precision for single-target tracking tasks and more than 85% performance in terms of MOTA for multi-object tracking tasks. Full article
(This article belongs to the Special Issue Advanced Artificial Intelligence and Deep Learning for Remote Sensing II)
►▼ Show Figures

Figure 1

24 pages, 13566 KiB  
Article
EL-NAS: Efficient Lightweight Attention Cross-Domain Architecture Search for Hyperspectral Image Classification
by Jianing Wang, Jinyu Hu, Yichen Liu, Zheng Hua, Shengjia Hao and Yuqiong Yao
Remote Sens. 2023, 15(19), 4688; https://doi.org/10.3390/rs15194688 - 25 Sep 2023
Cited by 1 | Viewed by 865
Abstract
Deep learning (DL) algorithms have demonstrated important breakthroughs for hyperspectral image (HSI) classification. Despite the remarkable success of DL, the burden of a manually designed DL structure with increased depth and size aroused the difficulty for the application in the mobile and embedded [...] Read more.
Deep learning (DL) algorithms have demonstrated important breakthroughs for hyperspectral image (HSI) classification. Despite the remarkable success of DL, the burden of a manually designed DL structure with increased depth and size aroused the difficulty for the application in the mobile and embedded devices in a real application. To tackle this issue, in this paper, we proposed an efficient lightweight attention network architecture search algorithm (EL-NAS) for realizing an efficient automatic design of a lightweight DL structure as well as improving the classification performance of HSI. First, aimed at realizing an efficient search procedure, we construct EL-NAS based on a differentiable network architecture search (NAS), which can greatly accelerate the convergence of the over-parameter supernet in a gradient descent manner. Second, in order to realize lightweight search results with high accuracy, a lightweight attention module search space is designed for EL-NAS. Finally, further for alleviating the problem of higher validation accuracy and worse classification performance, the edge decision strategy is exploited to perform edge decisions through the entropy of distribution estimated over non-skip operations to avoid further performance collapse caused by numerous skip operations. To verify the effectiveness of EL-NAS, we conducted experiments on several real-world hyperspectral images. The results demonstrate that the proposed EL-NAS indicates a more efficient search procedure with smaller parameter sizes and high accuracy performance for HSI classification, even under data-independent and sensor-independent scenarios. Full article
(This article belongs to the Special Issue Advanced Artificial Intelligence and Deep Learning for Remote Sensing II)
►▼ Show Figures

Graphical abstract

Other

Jump to: Research

15 pages, 10515 KiB  
Technical Note
A DeturNet-Based Method for Recovering Images Degraded by Atmospheric Turbulence
by Xiangxi Li, Xingling Liu, Weilong Wei, Xing Zhong, Haotong Ma and Junqiu Chu
Remote Sens. 2023, 15(20), 5071; https://doi.org/10.3390/rs15205071 - 23 Oct 2023
Viewed by 925
Abstract
Atmospheric turbulence is one of the main issues causing image blurring, dithering, and other degradation problems when detecting targets over long distances. Due to the randomness of turbulence, degraded images are hard to restore directly using traditional methods. With the rapid development of [...] Read more.
Atmospheric turbulence is one of the main issues causing image blurring, dithering, and other degradation problems when detecting targets over long distances. Due to the randomness of turbulence, degraded images are hard to restore directly using traditional methods. With the rapid development of deep learning, blurred images can be restored correctly and directly by establishing a nonlinear mapping relationship between the degraded and initial objects based on neural networks. These data-driven end-to-end neural networks offer advantages in turbulence image reconstruction due to their real-time properties and simplified optical systems. In this paper, inspired by the connection between the turbulence phase diagram characteristics and the attentional mechanisms for neural networks, we propose a new deep neural network called DeturNet to enhance the network’s performance and improve the quality of image reconstruction results. DeturNet employs global information aggregation operations and amplifies notable cross-dimensional reception regions, thereby contributing to the recovery of turbulence-degraded images. Full article
(This article belongs to the Special Issue Advanced Artificial Intelligence and Deep Learning for Remote Sensing II)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-3
Back to TopTop