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Integrating Deep Learning with Image Perception for Advanced Remote Sensing...
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Integrating Deep Learning with Image Perception for Advanced Remote Sensing Applications

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

Deadline for manuscript submissions: 28 May 2025 | Viewed by 1096

Special Issue Editors

Dr. Xin He

E-Mail Website
Guest Editor
School of Electronic and Information Engineering, Harbin Institute of Technology, Harbin 150001, China
Interests: deep learning; remote sensing; hyperspectral classification
Prof. Dr. Yushi Chen

E-Mail Website
Guest Editor
School of Electronics and Information Engineering, Harbin Institute of Technology, Harbin 150001, China
Interests: remote sensing data analysis; processing with a special focus on deep learning methods
Special Issues, Collections and Topics in MDPI journals
Dr. Jinghui Yang

E-Mail Website
Guest Editor
School of Information Engineering, China University of Geosciences (Beijing), Beijing 100083, China
Interests: hyperspectral imagery; remote sensing; classification; deep learning
Prof. Dr. Aili Wang

E-Mail Website
Guest Editor
Heilongjiang Province Key Laboratory of Laser Spectroscopy Technology and Application, Harbin University of Science and Technology, Harbin 150080, China
Interests: remote sensing image processing; deep learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Remote sensing stands is a cornerstone of modern environmental monitoring and geographical information systems. Its importance lies in its unparalleled ability to gather data over large, often inaccessible areas swiftly and repeatedly. By leveraging advanced sensors and satellite technology, remote sensing facilitates the accurate mapping of terrain, the monitoring of climate change impacts, the assessment of natural disasters, and the efficient management of agricultural resources. It enables scientists and policymakers to make informed decisions based on real-time, comprehensive datasets, fostering sustainable development and effective disaster response strategies. This technology bridges the gap between data acquisition and actionable insights, making it indispensable.

The aim of this Special Issue, entitled "Integrating Deep Learning with Image Perception for Advanced Remote Sensing Applications", is to explore the cutting-edge integration of deep learning techniques with image processing methodologies to enhance remote sensing capabilities. This integration aims to address the complex and diverse challenges associated with remote sensing data, such as improving image classification accuracy, enhancing target detection and recognition, and facilitating more efficient data analysis and interpretation. Articles may address, but are not limited, to the following topics:

  • Hyperspectral/multispectral classification;
  • Multimodal remote sensing analysis;
  • AI for remote sensing applications;
  • Remote sensing data for object detection.

Dr. Xin He
Prof. Dr. Yushi Chen
Dr. Jinghui Yang
Prof. Dr. Aili Wang
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
  • remote sensing data analysis
  • hyperspectral classification
  • multimodal remote sensing data fusion
  • deep learning-based SAR methods

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (2 papers)

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Research

19 pages, 76001 KiB  
Article
MFT-Reasoning RCNN: A Novel Multi-Stage Feature Transfer Based Reasoning RCNN for Synthetic Aperture Radar (SAR) Ship Detection
by Siyu Zhan, Muge Zhong, Yuxuan Yang, Guoming Lu and Xinyu Zhou
Remote Sens. 2025, 17(7), 1170; https://doi.org/10.3390/rs17071170 - 26 Mar 2025
Viewed by 201
Abstract
Conventional ship detection using synthetic aperture radar (SAR) is typically limited to fully focused spatial features of the ship target in SAR images. In this paper, we propose a multi-stage feature transfer (MFT)-based reasoning RCNN (MFT-Reasoning RCNN) to detect ships in SAR images. [...] Read more.
Conventional ship detection using synthetic aperture radar (SAR) is typically limited to fully focused spatial features of the ship target in SAR images. In this paper, we propose a multi-stage feature transfer (MFT)-based reasoning RCNN (MFT-Reasoning RCNN) to detect ships in SAR images. This algorithm can detect the SAR ship target using the MFT strategy and adaptive global reasoning module over all object regions by exploiting diverse knowledge between the ship and its surrounding elements. Specifically, we first calculate the probability of the simultaneous occurrence of environmental and target elements. Then, taking the environmental and target elements as entities, we construct the relationships between them using an adjacency matrix. Finally, we propose an MFT and use filter feature enhancement in the backbone layer to better extract the target features of SAR images and transfer knowledge between datasets. This paper has been tested on more than 10,000 images, and the experimental results demonstrate that our method can effectively detect different-scale ships in SAR images. Full article
(This article belongs to the Special Issue Integrating Deep Learning with Image Perception for Advanced Remote Sensing Applications)
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28 pages, 8967 KiB  
Article
Adaptive Global Dense Nested Reasoning Network into Small Target Detection in Large-Scale Hyperspectral Remote Sensing Image
by Siyu Zhan, Yuxuan Yang, Muge Zhong, Guoming Lu and Xinyu Zhou
Remote Sens. 2025, 17(6), 948; https://doi.org/10.3390/rs17060948 - 7 Mar 2025
Viewed by 507
Abstract
Small and dim target detection is a critical challenge in hyperspectral remote sensing, particularly in complex, large-scale scenes where spectral variability across diverse land cover types complicates the detection process. In this paper, we propose a novel target reasoning algorithm named Adaptive Global [...] Read more.
Small and dim target detection is a critical challenge in hyperspectral remote sensing, particularly in complex, large-scale scenes where spectral variability across diverse land cover types complicates the detection process. In this paper, we propose a novel target reasoning algorithm named Adaptive Global Dense Nested Reasoning Network (AGDNR). This algorithm integrates spatial, spectral, and domain knowledge to enhance the detection accuracy of small and dim targets in large-scale environments and simultaneously enables reasoning about target categories. The proposed method involves three key innovations. Firstly, we develop a high-dimensional, multi-layer nested U-Net that facilitates cross-layer feature transfer, preserving high-level features of small and dim targets throughout the network. Secondly, we present a novel approach for computing physicochemical parameters, which enhances the spectral characteristics of targets while minimizing environmental interference. Thirdly, we construct a geographic knowledge graph that incorporates both target and environmental information, enabling global target reasoning and more effective detection of small targets across large-scale scenes. Experimental results on three challenging datasets show that our method outperforms state-of-the-art approaches in detection accuracy and achieves successful classification of different small targets. Consequently, the proposed method offers a robust solution for the precise detection of hyperspectral small targets in large-scale scenarios. Full article
(This article belongs to the Special Issue Integrating Deep Learning with Image Perception for Advanced Remote Sensing Applications)
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