Submit to Special Issue Submit Abstract to Special Issue Review for Remote Sensing Propose a Special Issue

Journal Menu

Journal Browser

Special Issue "Artificial Intelligence Algorithm for Remote Sensing Imagery Processing III"

Special Issue Editors
Special Issue Information
Keywords
Related Special Issue
Published Papers

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

Deadline for manuscript submissions: 31 December 2023 | Viewed by 3829

Share This Special Issue

Special Issue Editors

Prof. Dr. Chunhui Zhao

E-Mail Website
Guest Editor

College of Information and Comminication Engineering, Harbin Engineering University, Harbin 150001, China
Interests: remote sensing image processing; intelligent information processing
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Danfeng Hong

E-Mail Website
Guest Editor

Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 10094, China
Interests: remote sensing; hyperspectral image processing; machine learning
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Qingsheng Xue

E-Mail Website
Guest Editor

Faculty of Information Science and Engineering, Ocean University of China, Qingdao 266100, China
Interests: hyperspectral remote sensing; underwater remote sensing
Special Issues, Collections and Topics in MDPI journals

Dr. Mohammad Awrangjeb

E-Mail Website
Guest Editor

Institute for Integrated and Intelligent Systems, Griffith University, Nathan, QLD 4111, Australia
Interests: deep learning; remote sensing image processing; point cloud processing; change detection; object recognition; object modelling; remote sensing data registration; remote sensing of environment
Special Issues, Collections and Topics in MDPI journals

Dr. Shou Feng
E-Mail Website
Guest Editor

College of Information and Communication Engineering, Harbin Engineering University, Harbin 150001, China
Interests: hyperspectral image processing
Special Issues, Collections and Topics in MDPI journals

Dr. Nan Su

E-Mail Website
Guest Editor

College of Information and Comminication Engineering, Harbin Engineering University, Harbin 150001, China
Interests: multimodal object detection and tracking
Special Issues, Collections and Topics in MDPI journals

Dr. Yiming Yan

E-Mail Website
Guest Editor

College of Information and Communication Engineering, Harbin Engineering University, Harbin 150001, China
Interests: multi-modal information extraction; image retrieval; 3D modeling
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Remote sensing technology is an important technical means for human beings to perceive the world, and multimodal remote sensing technology has become the mainstream of current research. With the rapid development of artificial intelligence technology, many new remote sensing image processing methods and algorithms have been proposed. Moreover, rapid advances in remote sensing methods have also promoted the application of associated algorithms and techniques to problems in many related fields, such as classification, segmentation and clustering, target detection, etc. This Special Issue aims to report and cover the latest advances and trends about the Artificial Intelligence Algorithm for Remote Sensing Imagery Processing. Papers of both theoretical methods and applicative techniques, as well as contributions regarding new advanced methodologies to relevant scenarios of remote sensing images, are welcome. We look forward to receiving your contributions.

Prof. Dr. Chunhui Zhao
Prof. Dr. Danfeng Hong
Prof. Dr. Qingsheng Xue
Dr. Mohammad Awrangjeb
Dr. Shou Feng
Dr. Nan Su
Dr. Yiming Yan
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
machine learning and deep learning for remote sensing
optical/multispectral/hyperspectral image processing
LiDAR and SAR
ocean and underwater remote sensing
target detection, anomaly detection, and change detection
semantic segmentation and classification
object re-identification using cross-domain/cross-dimensional images
object 3D modeling and mesh optimization
applications in remote sensing

Related Special Issue

Artificial Intelligence Algorithm for Remote Sensing Imagery Processing Ⅱ in Remote Sensing (13 articles)

Published Papers (7 papers)

Download All Papers

Order results

Result details

Show export options Show export options

Select all

Export citation of selected articles as:

Research

Jump to: Other

29 pages, 8806 KiB

Open AccessArticle

Polarimetric Synthetic Aperture Radar Ship Potential Area Extraction Based on Neighborhood Semantic Differences of the Latent Dirichlet Allocation Bag-of-Words Topic Model

Weixing Qiu

and

Zongxu Pan

Remote Sens. 2023, 15(23), 5601; https://doi.org/10.3390/rs15235601 - 01 Dec 2023

Viewed by 224

Abstract

Recently, deep learning methods have been widely studied in the field of polarimetric synthetic aperture radar (PolSAR) ship detection. However, extracting polarimetric and spatial features on the whole PolSAR image will result in high computational complexity. In addition, in the massive data ship detection task, the image to be detected contains a large number of invalid areas, such as land and seawater without ships. Therefore, using ship coarse detection methods to quickly locate the potential areas of ships, that is, ship potential area extraction, is an important prerequisite for PolSAR ship detection. Since existing unsupervised PolSAR ship detection methods based on pixel-level features often rely on fine sea–land segmentation pre-processing and have poor applicability to images with complex backgrounds, in order to solve the abovementioned issue, this paper proposes a PolSAR ship potential area extraction method based on the neighborhood semantic differences of an LDA bag-of-words topic model. Specifically, a polarimetric feature suitable for the scattering diversity condition is selected, and a polarimetric feature map is constructed; the superpixel segmentation method is used to generate the bag of words on the feature map, and latent high-level semantic features are extracted and classified with the improved LDA bag-of-words topic model method to obtain the PolSAR ship potential area extraction result, i.e., the PolSAR ship coarse detection result. The experimental results on the self-established PolSAR dataset validate the effectiveness and demonstrate the superiority of our method. Full article

(This article belongs to the Special Issue Artificial Intelligence Algorithm for Remote Sensing Imagery Processing III)

20 pages, 7262 KiB

Open AccessArticle

Learning to Adapt Adversarial Perturbation Consistency for Domain Adaptive Semantic Segmentation of Remote Sensing Images

and

Remote Sens. 2023, 15(23), 5498; https://doi.org/10.3390/rs15235498 - 25 Nov 2023

Viewed by 323

Abstract

Semantic segmentation techniques for remote sensing images (RSIs) have been widely developed and applied. However, most segmentation methods depend on sufficiently annotated data for specific scenarios. When a large change occurs in the target scenes, model performance drops significantly. Therefore, unsupervised domain adaptation (UDA) for semantic segmentation is proposed to alleviate the reliance on expensive per-pixel densely labeled data. In this paper, two key issues of existing domain adaptive (DA) methods are considered: (1) the factors that cause data distribution shifts in RSIs may be complex and diverse, and existing DA approaches cannot adaptively optimize for different domain discrepancy scenarios; (2) domain-invariant feature alignment, based on adversarial training (AT), is prone to excessive feature perturbation, leading to over robust models. To address these issues, we propose an AdvCDA method that guides the model to adapt adversarial perturbation consistency. We combine consistency regularization to consider interdomain feature alignment as perturbation information in the feature space, and thus propose a joint AT and self-training (ST) DA method to further promote the generalization performance of the model. Additionally, we propose a confidence estimation mechanism that determines network stream training weights so that the model can adaptively adjust the optimization direction. Extensive experiments have been conducted on Potsdam, Vaihingen, and LoveDA remote sensing datasets, and the results demonstrate that the proposed method can significantly improve the UDA performance in various cross-domain scenarios. Full article

(This article belongs to the Special Issue Artificial Intelligence Algorithm for Remote Sensing Imagery Processing III)

► Show Figures

Figure 1

19 pages, 7244 KiB

Open AccessArticle

Domain-Invariant Feature and Generative Adversarial Network Boundary Enhancement for Multi-Source Unsupervised Hyperspectral Image Classification

and

Remote Sens. 2023, 15(22), 5306; https://doi.org/10.3390/rs15225306 - 09 Nov 2023

Viewed by 436

Abstract

Hyperspectral image (HIS) classification, a crucial component of remote sensing technology, is currently challenged by edge ambiguity and the complexities of multi-source domain data. An innovative multi-source unsupervised domain adaptive algorithm (MUDA) structure is proposed in this work to overcome these issues. Our approach incorporates a domain-invariant feature unfolding algorithm, which employs the Fourier transform and Maximum Mean Discrepancy (MMD) distance to maximize invariant feature dispersion. Furthermore, the proposed approach efficiently extracts intraclass and interclass invariant features. Additionally, a boundary-constrained adversarial network generates synthetic samples, reinforcing the source domain feature space boundary and enabling accurate target domain classification during the transfer process. Furthermore, comparative experiments on public benchmark datasets demonstrate the superior performance of our proposed methodology over existing techniques, offering an effective strategy for hyperspectral MUDA. Full article

(This article belongs to the Special Issue Artificial Intelligence Algorithm for Remote Sensing Imagery Processing III)

► Show Figures

Figure 1

27 pages, 6520 KiB

Open AccessArticle

A Task-Risk Consistency Object Detection Framework Based on Deep Reinforcement Learning

Jiazheng Wen

Huanyu Liu

and

Junbao Li

Remote Sens. 2023, 15(20), 5031; https://doi.org/10.3390/rs15205031 - 19 Oct 2023

Viewed by 588

Abstract

A discernible gap has materialized between the expectations for object detection tasks in optical remote sensing images and the increasingly sophisticated design methods. The flexibility of deep learning object detection algorithms allows the selection and combination of multiple basic structures and model sizes, but this selection process relies heavily on human experience and lacks reliability when faced with special scenarios or extreme data distribution. To address these inherent challenges, this study proposes an approach that leverages deep reinforcement learning within the framework of vision tasks. This study introduces a Task-Risk Consistent Intelligent Detection Framework (TRC-ODF) for object detection in optical remote sensing images. The proposed framework designs a model optimization strategy based on deep reinforcement learning that systematically integrates the available information from images and vision processes. The core of the reinforcement learning agent is the proposed task-risk consistency reward mechanism, which is the driving force behind the optimal prediction allocation in the decision-making process. To verify the effectiveness of the proposed framework, multiple sets of empirical evaluations are conducted on representative optical remote sensing image datasets: RSOD, NWPU VHR-10, and DIOR. When applying the proposed framework to representative advanced detection models, the mean average precision (mAP@0.5 and mAP@0.5:0.95) is improved by 0.8–5.4 and 0.4–2.7, respectively. The obtained results showcase the considerable promise and potential of the TRC-ODF framework to address the challenges associated with object detection in optical remote sensing images. Full article

(This article belongs to the Special Issue Artificial Intelligence Algorithm for Remote Sensing Imagery Processing III)

► Show Figures

Graphical abstract

19 pages, 8089 KiB

Open AccessArticle

An Improved S²A-Net Algorithm for Ship Object Detection in Optical Remote Sensing Images

and

Remote Sens. 2023, 15(18), 4559; https://doi.org/10.3390/rs15184559 - 16 Sep 2023

Viewed by 634

Abstract

Ship detection based on remote sensing images holds significant importance in both military and economic domains. Ships within such images exhibit diverse scales, dense distributions, arbitrary orientations, and narrow shapes, which pose challenges for accurate recognition. This paper introduces an improved S²A-Net (Single-shot Alignment Network) based oriented object detection algorithm for ship detection. In network structure, pyramid squeeze attention is embedded in order to focus on key features and a context information module is designed to enhance the context understanding capability of the network. In the training strategy, considering the distortion problems such as blurring and low contrast in remote sensing images, a fog density and depth decomposition-based unpaired image dehazing network D4 is adopted to improve the image quality, besides, an image weight sampling strategy is proposed to enhance the training opportunities of small and difficult samples, thereby mitigating the issue of imbalanced ship category distribution. Experimental results demonstrate that the improved S²A-Net algorithm achieves the mean average precision of 77.27% for ship detection in the FAIR1M dataset, which is 5.6% better than the original S²A-Net algorithm, and outperforms the current common object detection algorithms. Full article

(This article belongs to the Special Issue Artificial Intelligence Algorithm for Remote Sensing Imagery Processing III)

► Show Figures

Graphical abstract

16 pages, 12986 KiB

Open AccessArticle

Label Smoothing Auxiliary Classifier Generative Adversarial Network with Triplet Loss for SAR Ship Classification

and

Remote Sens. 2023, 15(16), 4058; https://doi.org/10.3390/rs15164058 - 16 Aug 2023

Viewed by 484

Abstract

Deep-learning-based SAR ship classification has become a research hotspot in the military and civilian fields and achieved remarkable performance. However, the volume of available SAR ship classification data is relatively small, meaning that previous deep-learning-based methods have usually struggled with overfitting problems. Moreover, due to the limitation of the SAR imaging mechanism, the large intraclass diversity and small interclass similarity further degrade the classification performance. To address these issues, we propose a label smoothing auxiliary classifier generative adversarial network with triplet loss (LST-ACGAN) for SAR ship classification. In our method, an ACGAN is introduced to generate SAR ship samples with category labels. To address the model collapse problem in the ACGAN, the smooth category labels are assigned to generated samples. Moreover, triplet loss is integrated into the ACGAN for discriminative feature learning to enhance the margin of different classes. Extensive experiments on the OpenSARShip dataset demonstrate the superior performance of our method compared to the previous methods. Full article

(This article belongs to the Special Issue Artificial Intelligence Algorithm for Remote Sensing Imagery Processing III)

► Show Figures

Figure 1

Other

Jump to: Research

15 pages, 2212 KiB

Open AccessTechnical Note

MOON: A Subspace-Based Multi-Branch Network for Object Detection in Remotely Sensed Images

and

Remote Sens. 2023, 15(17), 4201; https://doi.org/10.3390/rs15174201 - 26 Aug 2023

Cited by 1 | Viewed by 434

Abstract

The effectiveness of training-based object detection heavily depends on the amount of sample data. But in the field of remote sensing, the amount of sample data is difficult to meet the needs of network training due to the non-cooperative imaging modes and complex imaging conditions. Moreover, the imbalance of the sample data between different categories may lead to the long-tail problem during the training. Given that similar sensors, data acquisition approaches, and data structures could make the targets in different categories possess certain similarities, those categories can be modeled together within a subspace rather than the entire space to leverage the amounts of sample data in different subspaces. To this end, a subspace-dividing strategy and a subspace-based multi-branch network is proposed for object detection in remotely sensed images. Specifically, a combination index is defined to depict this kind of similarity, a generalized category consisting of similar categories is proposed to represent the subspace, and a new subspace-based loss function is devised to address the relationship between targets in one subspace and across different subspaces to integrate the sample data from similar categories within a subspace and to balance the amounts of sample data between different subspaces. Furthermore, a subspace-based multi-branch network is constructed to ensure the subspace-aware regression. Experiments on the DOTA and HRSC2016 datasets demonstrated the superiority of our proposed method. Full article

(This article belongs to the Special Issue Artificial Intelligence Algorithm for Remote Sensing Imagery Processing III)

► Show Figures

Journal Menu

Journal Browser

Special Issue "Artificial Intelligence Algorithm for Remote Sensing Imagery Processing III"

Share This Special Issue

Special Issue Editors

Special Issue Information

Keywords

Related Special Issue

Published Papers (7 papers)

Research

Other

Further Information

Guidelines

MDPI Initiatives

Follow MDPI