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
Advances in Geospatial Object Detection and Tracking Using AI
remotesensing-logo
Submit to Remote Sensing Review for Remote Sensing Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

Advances in Geospatial Object Detection and Tracking Using AI

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 January 2023) | Viewed by 26765

Special Issue Editors

Dr. Xin Wu

E-Mail Website
Guest Editor
Beijing Institute of Technology (BIT), Beijing 100081, China
Interests: object detection; image processing; machine learning;
Prof. Dr. Danfeng Hong

grade 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
Dr. Sicong Liu

E-Mail Website
Guest Editor
College of Surveying and Geo-Informatics, Tongji University, Shanghai 200092, China
Interests: multitemporal data analysis and processing; change detection; spectral signal processing; information fusion; multispectral/hyperspectral images processing; remote sensing applications
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Pedram Ghamisi

grade E-Mail Website
Guest Editor
1.Helmholtz Institute Freiberg for Resource Technology, Helmholtz-Zentrum Dresden-Rossendorf (HZDR), D-09599 Freiberg, Germany
2. Institute of Advanced Research in Artificial Intelligence (IARAI), 1030 Wien, Austria
Interests: hyperspectral image interpretation; multisensor and multitemporal data fusion
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Geospatial object detection and tracking, as an important research topic in the field of remote sensing, has been widely investigated and studied in a variety of civil and military missions, such as target detection, pathway estimation, environmental monitoring and management, geological hazard detection, precision agriculture, and urban planning. Previous methods usually depend on a great quantity of manpower and computing power. Consequently, the ability to deal with large-scale or real scenes remains limited. Currently, with the rapid development of hardware systems and intelligent platforms, AI-based algorithms and models have been paid increasing attention by researchers in many fields. Using AI techniques enables more accurate object detection and tracking in remote sensing. Therefore, to fully embody the advances of AI techniques in geospatial object detection and tracking tasks, this Special Issue focuses on the subject of geospatial object detection and tracking using advanced AI techniques.

Potential topics for this Special Issue include, but are not limited to:

  • New detection and tracking models and frameworks in remote sensing;
  • Advanced machine/deep learning for efficient object detection using unsupervised, supervised, semi-supervised, weakly-supervised, and self-supervised learning strategies;
  • Object detection in time-series remote sensing data;
  • Multi-source and multi-modal object detection and tracking;
  • Small object detection and tracking in complex environment;
  • Intelligent information extraction for object recognition and detection;
  • Build new benchmark datasets for geospatial object detection and tracking;
  • Uncertainty on object detection and tracking;
  • Large-scale change detection or monitoring using AI.

Dr. Xin Wu
Dr. Danfeng Hong
Prof. Dr. Sicong Liu
Dr. Pedram Ghamisi
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.

Published Papers (6 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: Review

23 pages, 9703 KiB  
Article
Sparse Channel Pruning and Assistant Distillation for Faster Aerial Object Detection
by Chenwei Deng, Donglin Jing, Zhihan Ding and Yuqi Han
Remote Sens. 2022, 14(21), 5347; https://doi.org/10.3390/rs14215347 - 25 Oct 2022
Cited by 5 | Viewed by 1511
Abstract
In recent years, object detectors based on convolutional neural networks have been widely used on remote sensing images. However, the improvement of their detection performance depends on a deeper convolution layer and a complex convolution structure, resulting in a significant increase in the [...] Read more.
In recent years, object detectors based on convolutional neural networks have been widely used on remote sensing images. However, the improvement of their detection performance depends on a deeper convolution layer and a complex convolution structure, resulting in a significant increase in the storage space and computational complexity. Although previous works have designed a variety of new lightweight convolution and compression algorithms, these works often require complex manual design and cause the detector to be greatly modified, which makes it difficult to directly apply the algorithms to different detectors and general hardware. Therefore, this paper proposes an iterative pruning framework based on assistant distillation. Specifically, a structured sparse pruning strategy for detectors is proposed. By taking the channel scaling factor as a representation of the weight importance, the channels of the network are pruned and the detector is greatly slimmed. Then, a teacher assistant distillation model is proposed to recover the network performance after compression. The intermediate models retained in the pruning process are used as assistant models. By way of the teachers distilling the assistants and the assistants distilling the students, the students’ underfitting caused by the difference in capacity between teachers and students is eliminated, thus effectively restoring the network performance. By using this compression framework, we can greatly compress the network without changing the network structure and can obtain the support of any hardware platform and deep learning library. Extensive experiments show that compared with existing detection networks, our method can achieve an effective balance between speed and accuracy on three commonly used remote sensing target datasets (i.e., NWPU VHR-10, RSOD, and DOTA). Full article
(This article belongs to the Special Issue Advances in Geospatial Object Detection and Tracking Using AI)
►▼ Show Figures

Figure 1

19 pages, 3090 KiB  
Article
Few-Shot Aircraft Detection in Satellite Videos Based on Feature Scale Selection Pyramid and Proposal Contrastive Learning
by Zhuang Zhou, Shengyang Li, Weilong Guo and Yanfeng Gu
Remote Sens. 2022, 14(18), 4581; https://doi.org/10.3390/rs14184581 - 14 Sep 2022
Cited by 6 | Viewed by 1858
Abstract
To date, few-shot object detection methods have received extensive attention in the field of remote sensing, and no relevant research has been conducted using satellite videos. It is difficult to identify foreground objects in satellite videos duo to their small size and low [...] Read more.
To date, few-shot object detection methods have received extensive attention in the field of remote sensing, and no relevant research has been conducted using satellite videos. It is difficult to identify foreground objects in satellite videos duo to their small size and low contrast and the domain differences between base and novel classes under few-shot conditions. In this paper, we propose a few-shot aircraft detection method with a feature scale selection pyramid and proposal contrastive learning for satellite videos. Specifically, a feature scale selection pyramid network (FSSPN) is constructed to replace the traditional feature pyramid network (FPN), which alleviates the limitation of the inconsistencies in gradient computation between different layers for small-scale objects. In addition, we add proposal contrastive learning items to the loss function to achieve more robust representations of objects. Moreover, we expand the freezing parameters of the network in the fine-tuning stage to reduce the interference of visual differences between the base and novel classes. An evaluation of large-scale experimental data showed that the proposed method makes full use of the advantages of the two-stage fine-tuning strategy and the characteristics of satellite video to enhance the few-shot detection performance. Full article
(This article belongs to the Special Issue Advances in Geospatial Object Detection and Tracking Using AI)
►▼ Show Figures

Graphical abstract

20 pages, 37160 KiB  
Article
Context Information Refinement for Few-Shot Object Detection in Remote Sensing Images
by Yan Wang, Chaofei Xu, Cuiwei Liu and Zhaokui Li
Remote Sens. 2022, 14(14), 3255; https://doi.org/10.3390/rs14143255 - 06 Jul 2022
Cited by 19 | Viewed by 4841
Abstract
Recently, few-shot object detection based on fine-tuning has attracted much attention in the field of computer vision. However, due to the scarcity of samples in novel categories, obtaining positive anchors for novel categories is difficult, which implicitly introduces the foreground–background imbalance problem. It [...] Read more.
Recently, few-shot object detection based on fine-tuning has attracted much attention in the field of computer vision. However, due to the scarcity of samples in novel categories, obtaining positive anchors for novel categories is difficult, which implicitly introduces the foreground–background imbalance problem. It is difficult to identify foreground objects from complex backgrounds due to various object sizes and cluttered backgrounds. In this article, we propose a novel context information refinement few-shot detector (CIR-FSD) for remote sensing images. In particular, we design a context information refinement (CIR) module to extract discriminant context features. This module uses dilated convolutions and dense connections to capture rich context information from different receptive fields and then uses a binary map as the supervision label to refine the context information. In addition, we improve the region proposal network (RPN). Concretely, the RPN is fine-tuned on novel categories, and the constraint of non-maximum suppression (NMS) is relaxed, which can obtain more positive anchors for novel categories. Experiments on two remote sensing public datasets show the effectiveness of our detector. Full article
(This article belongs to the Special Issue Advances in Geospatial Object Detection and Tracking Using AI)
►▼ Show Figures

Graphical abstract

21 pages, 5256 KiB  
Article
Transformer with Transfer CNN for Remote-Sensing-Image Object Detection
by Qingyun Li, Yushi Chen and Ying Zeng
Remote Sens. 2022, 14(4), 984; https://doi.org/10.3390/rs14040984 - 17 Feb 2022
Cited by 67 | Viewed by 9807
Abstract
Object detection in remote-sensing images (RSIs) is always a vibrant research topic in the remote-sensing community. Recently, deep-convolutional-neural-network (CNN)-based methods, including region-CNN-based and You-Only-Look-Once-based methods, have become the de-facto standard for RSI object detection. CNNs are good at local feature extraction but they [...] Read more.
Object detection in remote-sensing images (RSIs) is always a vibrant research topic in the remote-sensing community. Recently, deep-convolutional-neural-network (CNN)-based methods, including region-CNN-based and You-Only-Look-Once-based methods, have become the de-facto standard for RSI object detection. CNNs are good at local feature extraction but they have limitations in capturing global features. However, the attention-based transformer can obtain the relationships of RSI at a long distance. Therefore, the Transformer for Remote-Sensing Object detection (TRD) is investigated in this study. Specifically, the proposed TRD is a combination of a CNN and a multiple-layer Transformer with encoders and decoders. To detect objects from RSIs, a modified Transformer is designed to aggregate features of global spatial positions on multiple scales and model the interactions between pairwise instances. Then, due to the fact that the source data set (e.g., ImageNet) and the target data set (i.e., RSI data set) are quite different, to reduce the difference between the data sets, the TRD with the transferring CNN (T-TRD) based on the attention mechanism is proposed to adjust the pre-trained model for better RSI object detection. Because the training of the Transformer always needs abundant, well-annotated training samples, and the number of training samples for RSI object detection is usually limited, in order to avoid overfitting, data augmentation is combined with a Transformer to improve the detection performance of RSI. The proposed T-TRD with data augmentation (T-TRD-DA) is tested on the two widely-used data sets (i.e., NWPU VHR-10 and DIOR) and the experimental results reveal that the proposed models provide competitive results (i.e., centuple mean average precision of 87.9 and 66.8 with at most 5.9 and 2.4 higher than the comparison methods on the NWPU VHR-10 and the DIOR data sets, respectively) compared to the competitive benchmark methods, which shows that the Transformer-based method opens a new window for RSI object detection. Full article
(This article belongs to the Special Issue Advances in Geospatial Object Detection and Tracking Using AI)
►▼ Show Figures

Graphical abstract

20 pages, 7029 KiB  
Article
A Lightweight Detection Model for SAR Aircraft in a Complex Environment
by Mingwu Li, Gongjian Wen, Xiaohong Huang, Kunhong Li and Sizhe Lin
Remote Sens. 2021, 13(24), 5020; https://doi.org/10.3390/rs13245020 - 10 Dec 2021
Cited by 9 | Viewed by 3052
Abstract
Recently, deep learning has been widely used in synthetic aperture radar (SAR) aircraft detection. However, the complex environment of the airport—consider the boarding bridges, for instance—greatly interferes with aircraft detection. Besides, the detection speed is also an important indicator in practical applications. To [...] Read more.
Recently, deep learning has been widely used in synthetic aperture radar (SAR) aircraft detection. However, the complex environment of the airport—consider the boarding bridges, for instance—greatly interferes with aircraft detection. Besides, the detection speed is also an important indicator in practical applications. To alleviate these problems, we propose a lightweight detection model (LDM), mainly including a reuse block (RB) and an information correction block (ICB) based on the Yolov3 framework. The RB module helps the neural network extract rich aircraft features by aggregating multi-layer information. While the RB module brings more effective information, there is also redundant and useless information aggregated by the reuse block, which is harmful to detection precision. Therefore, to accurately extract more aircraft features, we propose an ICB module combining scattering mechanism characteristics by extracting the gray features and enhancing spatial information, which helps suppress interference in a complex environment and redundant information. Finally, we conducted a series of experiments on the SAR aircraft detection dataset (SAR-ADD). The average precision was 0.6954, which is superior to the precision values achieved by other methods. In addition, the average detection time of LDM was only 6.38 ms, making it much faster than other methods. Full article
(This article belongs to the Special Issue Advances in Geospatial Object Detection and Tracking Using AI)
►▼ Show Figures

Graphical abstract

Review

Jump to: Research

34 pages, 2804 KiB  
Review
Object Tracking Based on Satellite Videos: A Literature Review
by Zhaoxiang Zhang, Chenghang Wang, Jianing Song and Yuelei Xu
Remote Sens. 2022, 14(15), 3674; https://doi.org/10.3390/rs14153674 - 31 Jul 2022
Cited by 11 | Viewed by 4314
Abstract
Video satellites have recently become an attractive method of Earth observation, providing consecutive images of the Earth’s surface for continuous monitoring of specific events. The development of on-board optical and communication systems has enabled the various applications of satellite image sequences. However, satellite [...] Read more.
Video satellites have recently become an attractive method of Earth observation, providing consecutive images of the Earth’s surface for continuous monitoring of specific events. The development of on-board optical and communication systems has enabled the various applications of satellite image sequences. However, satellite video-based target tracking is a challenging research topic in remote sensing due to its relatively low spatial and temporal resolution. Thus, this survey systematically investigates current satellite video-based tracking approaches and benchmark datasets, focusing on five typical tracking applications: traffic target tracking, ship tracking, typhoon tracking, fire tracking, and ice motion tracking. The essential aspects of each tracking target are summarized, such as the tracking architecture, the fundamental characteristics, primary motivations, and contributions. Furthermore, popular visual tracking benchmarks and their respective properties are discussed. Finally, a revised multi-level dataset based on WPAFB videos is generated and quantitatively evaluated for future development in the satellite video-based tracking area. In addition, 54.3% of the tracklets with lower Difficulty Score (DS) are selected and renamed as the Easy group, while 27.2% and 18.5% of the tracklets are grouped into the Medium-DS group and the Hard-DS group, respectively. Full article
(This article belongs to the Special Issue Advances in Geospatial Object Detection and Tracking Using AI)
►▼ Show Figures

Graphical abstract

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-6
Back to TopTop