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
Oriented Object Detection in Aerial Image
remotesensing-logo
Submit to Remote Sensing Review for Remote Sensing Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

Oriented Object Detection in Aerial Image

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 (7 May 2023) | Viewed by 2917

Special Issue Editors

Prof. Dr. Hongliang Li

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; image segmentation; visual attention; multimedia processing
Dr. Qingbo Wu

E-Mail Website
Guest Editor
School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China
Interests: image/video quality assessment; perceptual modeling and processing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Due to angle and altitude changes, the objects in aerial images usually display different scales, appearances and directions, which are hard to locate using conventional horizontal bounding boxes. Object detection is a significant and challenging problem in the field of remote sensing and image analysis. However, most existing methods can easily miss or incorrectly locate objects due to the various sizes and aspect ratios of objects. Object detection usually relies on object representation to predict the location and category of objects in an image. Thus, a suitable object representation is key to the success of object detection. Existing popular object detectors are mainly based on two classes of object representations: anchor-based representation and point-based representation. Object detection combines object classification and object localization problems.

Object detection aims to accurately locate and classify objects in an image, which requires precise object representations. An object can be flexibly represented as cross lines in different combinations. It not only can effectively reduce the interference of noise, but also takes into account the continuous object information, which is useful to enhance the discriminability of object features and find the object boundaries. This Special Issue aims to provide a timely and thorough collection of high-quality contributions that explore emerging oriented bounding box-based or bounding box-free object detection methods for aerial images. Original research works presenting new insights, frameworks, and databases are welcome.

Thus, this Special Issue focuses on presenting the latest advances and trends for object detection and identification within the wide field of remote sensing:

  • object detection for the environmental monitoring of the atmosphere;
  • object detection for the environmental monitoring of the sea;
  • object detection for security and military;
  • object detection for urban monitoring;
  • aerial image database for oriented object detection;
  • multi-modal oriented object detection;
  • oriented object detection network architecture;
  • oriented object detection network optimization;
  • oriented RoI transformer;
  • oriented small object detection;
  • high-efficiency architecture for fast oriented object detection.

Prof. Dr. Hongliang Li
Dr. Qingbo Wu
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
  • aerial image
  • machine learning
  • high-resolution/super-pixel remote sensing image segmentation
  • algorithms
  • remote sensing
  • satellites
  • oriented/rotated object detection
  • anchor-free oriented object detection

Published Papers (1 paper)

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

Research

21 pages, 5456 KiB  
Article
G-Rep: Gaussian Representation for Arbitrary-Oriented Object Detection
by Liping Hou, Ke Lu, Xue Yang, Yuqiu Li and Jian Xue
Remote Sens. 2023, 15(3), 757; https://doi.org/10.3390/rs15030757 - 28 Jan 2023
Cited by 17 | Viewed by 2613
Abstract
Typical representations for arbitrary-oriented object detection tasks include the oriented bounding box (OBB), the quadrilateral bounding box (QBB), and the point set (PointSet). Each representation encounters problems that correspond to its characteristics, such as boundary discontinuity, square-like problems, representation ambiguity, and isolated points, [...] Read more.
Typical representations for arbitrary-oriented object detection tasks include the oriented bounding box (OBB), the quadrilateral bounding box (QBB), and the point set (PointSet). Each representation encounters problems that correspond to its characteristics, such as boundary discontinuity, square-like problems, representation ambiguity, and isolated points, which lead to inaccurate detection. Although many effective strategies have been proposed for various representations, there is still no unified solution. Current detection methods based on Gaussian modeling have demonstrated the possibility of resolving this dilemma; however, they remain limited to OBB. To go further, in this paper, we propose a unified Gaussian representation called G-Rep to construct Gaussian distributions for OBB, QBB, and PointSet, which achieves a unified solution to various representations and problems. Specifically, PointSet- or QBB-based object representations are converted into Gaussian distributions and their parameters are optimized using the maximum likelihood estimation algorithm. Then, three optional Gaussian metrics are explored to optimize the regression loss of the detector because of their excellent parameter optimization mechanisms. Furthermore, we also use Gaussian metrics for sampling to align label assignment and regression loss. Experimental results obtained on several publicly available datasets, such as DOTA, HRSC2016, UCAS-AOD, and ICDAR2015, show the excellent performance of the proposed method for arbitrary-oriented object detection. Full article
(This article belongs to the Special Issue Oriented Object Detection in Aerial Image)
►▼ Show Figures

Figure 1

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