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Advanced Artificial Intelligence and Deep Learning for Remote Sensing (3rd 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: 29 July 2025 | Viewed by 728

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: infrared small-target detection; remote sensing image processing; deep learning
Special Issues, Collections and Topics in MDPI journals
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 Issues, Collections and Topics in MDPI journals
Dr. Yimian Dai

E-Mail Website
Guest Editor
College of Computer Science, Nankai University, Tianjin 300350, China
Interests: Target detection; image restoration

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 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, to further promote the technological progress in this field.

The topics include but are not limited to the following:

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

This Special Issue is the third edition of “Advanced Artificial Intelligence and Deep Learning for Remote Sensing II”.

Prof. Dr. Zhenming Peng
Prof. Dr. Zhengzhou Li
Dr. Yimian Dai
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

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

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Related Special Issue

Published Papers (1 paper)

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Research

23 pages, 31391 KiB  
Article
A Method for Airborne Small-Target Detection with a Multimodal Fusion Framework Integrating Photometric Perception and Cross-Attention Mechanisms
by Shufang Xu, Heng Li, Tianci Liu and Hongmin Gao
Remote Sens. 2025, 17(7), 1118; https://doi.org/10.3390/rs17071118 - 21 Mar 2025
Viewed by 433
Abstract
In recent years, the rapid advancement and pervasive deployment of unmanned aerial vehicle (UAV) technology have catalyzed transformative applications across the military, civilian, and scientific domains. While aerial imaging has emerged as a pivotal tool in modern remote sensing systems, persistent challenges remain [...] Read more.
In recent years, the rapid advancement and pervasive deployment of unmanned aerial vehicle (UAV) technology have catalyzed transformative applications across the military, civilian, and scientific domains. While aerial imaging has emerged as a pivotal tool in modern remote sensing systems, persistent challenges remain in achieving robust small-target detection under complex all-weather conditions. This paper presents an innovative multimodal fusion framework incorporating photometric perception and cross-attention mechanisms to address the critical limitations of current single-modality detection systems, particularly their susceptibility to reduced accuracy and elevated false-negative rates in adverse environmental conditions. Our architecture introduces three novel components: (1) a bidirectional hierarchical feature extraction network that enables the synergistic processing of heterogeneous sensor data; (2) a cross-modality attention mechanism that dynamically establishes inter-modal feature correlations through learnable attention weights; (3) an adaptive photometric weighting fusion module that implements spectral characteristic-aware feature recalibration. The proposed system achieves multimodal complementarity through two-phase integration: first by establishing cross-modal feature correspondences through attention-guided feature alignment, then performing weighted fusion based on photometric reliability assessment. Comprehensive experiments demonstrate that our framework achieves an improvement of at least 3.6% in mAP compared to the other models on the challenging LLVIP dataset, and with particular improvements in detection reliability on the KAIST dataset. This research advances the state of the art in aerial target detection by providing a principled approach for multimodal sensor fusion, with significant implications for surveillance, disaster response, and precision agriculture applications. Full article
(This article belongs to the Special Issue Advanced Artificial Intelligence and Deep Learning for Remote Sensing (3rd Edition))
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