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Symmetry in Big Geospatial and Remote Sensing Data Driven Autonomous...
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Symmetry in Big Geospatial and Remote Sensing Data Driven Autonomous Vehicles

A special issue of Symmetry (ISSN 2073-8994). This special issue belongs to the section "Computer".

Deadline for manuscript submissions: closed (31 October 2022) | Viewed by 5176

Special Issue Editor

Prof. Dr. Bo Du

E-Mail Website
Guest Editor
School of Computer, Centre for Quantum Computation and Intelligent Systems, Wuhan University, Wuhan, China
Interests: big data mining management and analysis; multimedia technology and big data analysis; multimedia signal processing; machine learning and intelligent interaction; computer vision; computer applications; pattern recognition; artificial intelligence; data mining and analysis; audio and video processing; intelligent computing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Autonomous vehicles are capable of sensing the environment and running with little or even no human interventions. Compared to traditional human-driving vehicles, autonomous vehicles have the potential to reduce traffic accidents, traffic congestions, and fuel consumption, and they are expected to be the future direction of intelligent transportation.

Big geospatial and remote sensing data play a fundamental role in autonomous vehicles, which is helpful in acquiring the patterns of driving/travel behavior, human mobility, and traffic flow, and in sensing a more large-scale environment and giving more accurate, traffic-aware navigation. Generally, geospatial and remote sensing data include road network data, digital elevation model (DEM) data, vehicle and human trajectory data, traffic flow data, traffic accident data, traffic satellite image data, and location-based social media data. The storage and deep understanding of geospatial and remote sensing data face many challenges. In this Special Issue, we invite researchers to address the challenges in “Symmetry in Big Geospatial and Remote Sensing Data-Driven Autonomous Vehicles”.

Dr. Bo Du
Guest Editor

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. Symmetry is an international peer-reviewed open access monthly 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 2400 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 understanding of big geospatial and remote sensing data
  • Geospatial and remote sensing datasets construction for autonomous vehicles
  • Big geospatial and remote sensing data preprocessing, including data cleaning, super-resolution reconstruction, feature selection and extraction, multimodal data fusion, etc.
  • Date mining/deep learning/reinforcement learning/transfer learning/multitask learning/federated learning on big geospatial and remote sensing data
  • Vehicle detection and tracking
  • Traffic flow/vehicles trajectory prediction, traffic/driving behavior analytics
  • Geospatial and remote sensing data-driven environment recognition and perception
  • Traffic-aware routing and navigation
  • Geospatial crowdsourcing for autonomous vehicles
  • Internet of Vehicles
  • Geospatial and remote sensing data-driven autonomous vehicle applications.

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Published Papers (2 papers)

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Research

10 pages, 4546 KiB  
Article
Human–Object Interaction Detection with Ratio-Transformer
by Tianlang Wang, Tao Lu, Wenhua Fang and Yanduo Zhang
Symmetry 2022, 14(8), 1666; https://doi.org/10.3390/sym14081666 - 11 Aug 2022
Cited by 2 | Viewed by 1849
Abstract
Human–object interaction (HOI) is a human-centered object detection task that aims to identify the interactions between persons and objects in an image. Previous end-to-end methods have used the attention mechanism of a transformer to spontaneously identify the associations between persons and objects in [...] Read more.
Human–object interaction (HOI) is a human-centered object detection task that aims to identify the interactions between persons and objects in an image. Previous end-to-end methods have used the attention mechanism of a transformer to spontaneously identify the associations between persons and objects in an image, which effectively improved detection accuracy; however, a transformer can increase computational demands and slow down detection processes. In addition, the end-to-end method can result in asymmetry between foreground and background information. The foreground data may be significantly less than the background data, while the latter consumes more computational resources without significantly improving detection accuracy. Therefore, we proposed an input-controlled transformer, “ratio-transformer” to solve an HOI task, which could not only limit the amount of information in the input transformer by setting a sampling ratio, but also significantly reduced the computational demands while ensuring detection accuracy. The ratio-transformer consisted of a sampling module and a transformer network. The sampling module divided the input feature map into foreground versus background features. The irrelevant background features were a pooling sampler, which were then fused with the foreground features as input data for the transformer. As a result, the valid data input into the Transformer network remained constant, while irrelevant information was significantly reduced, which maintained the foreground and background information symmetry. The proposed network was able to learn the feature information of the target itself and the association features between persons and objects so it could query to obtain the complete HOI interaction triplet. The experiments on the VCOCO dataset showed that the proposed method reduced the computational demand of the transformer by 57% without any loss of accuracy, as compared to other current HOI methods. Full article
(This article belongs to the Special Issue Symmetry in Big Geospatial and Remote Sensing Data Driven Autonomous Vehicles)
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21 pages, 2197 KiB  
Article
A Band Selection Approach for Hyperspectral Image Based on a Modified Hybrid Rice Optimization Algorithm
by Zhiwei Ye, Wenhui Cai, Shiqin Liu, Kainan Liu, Mingwei Wang and Wen Zhou
Symmetry 2022, 14(7), 1293; https://doi.org/10.3390/sym14071293 - 22 Jun 2022
Cited by 14 | Viewed by 2643
Abstract
Hyperspectral image (HSI) analysis has become one of the most active topics in the field of remote sensing, which could provide powerful assistance for sensing a larger-scale environment. Nevertheless, a large number of high-correlation and redundancy bands in HSI data provide a massive [...] Read more.
Hyperspectral image (HSI) analysis has become one of the most active topics in the field of remote sensing, which could provide powerful assistance for sensing a larger-scale environment. Nevertheless, a large number of high-correlation and redundancy bands in HSI data provide a massive challenge for image recognition and classification. Hybrid Rice Optimization (HRO) is a novel meta-heuristic, and its population is approximately divided into three groups with an equal number of individuals according to self-equilibrium and symmetry, which has been successfully applied in band selection. However, there are some limitations of primary HRO with respect to the local search for better solutions and this may result in overlooking a promising solution. Therefore, a modified HRO (MHRO) based on an opposition-based-learning (OBL) strategy and differential evolution (DE) operators is proposed for band selection in this paper. Firstly, OBL is adopted in the initialization phase of MHRO to increase the diversity of the population. Then, the exploitation ability is enhanced by embedding DE operators into the search process at each iteration. Experimental results verify that the proposed method shows superiority in both the classification accuracy and selected number of bands compared to other algorithms involved in the paper. Full article
(This article belongs to the Special Issue Symmetry in Big Geospatial and Remote Sensing Data Driven Autonomous Vehicles)
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