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Applied Sciences
Special Issues
Intelligent Computing and Remote Sensing—2nd Edition
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Intelligent Computing and Remote Sensing—2nd Edition

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: 30 August 2024 | Viewed by 1727

Special Issue Editors

Dr. Qizhi Xu

E-Mail Website
Guest Editor
School of Mechatronical Engineering, Beijing Institute of Technology, Beijing 100081, China
Interests: space perception and intelligent computing; data fusion
Special Issues, Collections and Topics in MDPI journals
Dr. Jin Zheng

E-Mail Website
Guest Editor
School of Computer Science and Engineering, Beihang University, Beijing 100191, China
Interests: remote sensing image processing; visual computing; machine learning
Special Issues, Collections and Topics in MDPI journals
Dr. Feng Gao

E-Mail Website
Guest Editor
Faculty of Information Science and Engineering, Ocean University of China, Qingdao 266100, China
Interests: remote sensing image understanding; hyperspectral image processing; artificial intelligence oceanography
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The previous Special Issue showcased the fusion of intelligent computing and remote sensing, addressing key challenges in the field. Building on its success, we are excited to announce the 2nd Edition.

This upcoming Special Issue aims to continue exploring the applications of intelligent computing in remote sensing, welcoming submissions on image processing, on-board information processing, big data analysis, and UAV systems. We invite theoretical research, practical applications, reviews, and surveys.

This is a platform for cutting-edge research and advancements in intelligent computing and remote sensing. Join scholars and experts globally to drive innovation in this field. Contribute to this Special Issue, shaping the future of intelligent computing in remote sensing.

Dr. Qizhi Xu
Dr. Jin Zheng
Dr. Feng Gao
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. Applied Sciences 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 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 learning
  • on-orbit/on-board real-time computing
  • advanced image processing
  • remote sensing big data
  • UAV information intelligent computing
  • radar information intelligent computing
  • information fusion
  • video imaging satellite intelligent computing

Published Papers (3 papers)

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Research

15 pages, 5202 KiB  
Article
Crack Detection of Concrete Based on Improved CenterNet Model
by Huaiqiang Kang, Fengjun Zhou, Shen Gao and Qizhi Xu
Appl. Sci. 2024, 14(6), 2527; https://doi.org/10.3390/app14062527 - 17 Mar 2024
Viewed by 437
Abstract
Cracks on concrete surfaces are vital factors affecting construction safety. Accurate and efficient crack detection can prevent safety-related accidents. Using drones to photograph cracks on a concrete surface and detect them through computer vision technology has the advantages of accurate target recognition, simple [...] Read more.
Cracks on concrete surfaces are vital factors affecting construction safety. Accurate and efficient crack detection can prevent safety-related accidents. Using drones to photograph cracks on a concrete surface and detect them through computer vision technology has the advantages of accurate target recognition, simple practical operation, and low cost. To solve this problem, an improved CenterNet concrete crack-detection model is proposed. Firstly, a channel-space attention mechanism is added to the original model to enhance the ability of the convolution neural network to pay attention to the image. Secondly, a feature selection module is introduced to scale the feature map in the downsampling stage to a uniform size and combine it in the channel dimension. In the upsampling stage, the feature selection module adaptively selects the combined features and fuses them with the output features of the upsampling. Finally, the target size loss is optimized from a Smooth L1 Loss to IoU Loss to lessen its inability to adapt to targets of different sizes. The experimental results show that the improved CenterNet model reduces the FPS by 123.7 Hz, increases the GPU memory by 62 MB, increases the FLOPs by 3.81 times per second, and increases the AP by 15.4% compared with the original model. The GPU memory occupancy remained stable during the training process and exhibited good real-time performance and robustness. Full article
(This article belongs to the Special Issue Intelligent Computing and Remote Sensing—2nd Edition)
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19 pages, 8613 KiB  
Article
Erosion Monitoring in Benggang Based on Control-Free Images and Nap-of-the-Object Photogrammetry Techniques
by Linting Zhong, Jianfeng Lai, Guangxi Lai, Xiang Ji, Yue Zhang, Fangshi Jiang, Yanhe Huang and Jinshi Lin
Appl. Sci. 2024, 14(5), 2112; https://doi.org/10.3390/app14052112 - 04 Mar 2024
Viewed by 549
Abstract
Unmanned aerial vehicle (UAV)-based nap-of-the-object photogrammetry techniques can be utilized to periodically monitor the erosion of nearly vertical cliffs. However, the broader applicability of such techniques is hindered by the necessity of deploying multiple ground control points around collapsing walls. This study aims [...] Read more.
Unmanned aerial vehicle (UAV)-based nap-of-the-object photogrammetry techniques can be utilized to periodically monitor the erosion of nearly vertical cliffs. However, the broader applicability of such techniques is hindered by the necessity of deploying multiple ground control points around collapsing walls. This study aims to accurately assess Benggang erosion before and after the rainy season by analyzing the optimal flight proximity distance using close-range photogrammetric techniques. The assessment centers on positioning accuracy, point cloud data, and digital surface model (DSM) data. Nap-of-the-object photogrammetry techniques are integrated with control-free image methods to conduct aerial surveys of Benggang, generating high-resolution three-dimensional (3D) DSMs. The feasibility of this control-free-image-based nap-of-the-object photogrammetry technique is evaluated based on positioning accuracy and measurement errors, comparing the generated DSMs with real-time kinematic (RTK) measured coordinate data. The results indicate that a flight proximity distance of 20 m is optimal for obtaining data in the Benggang area using control-free-image-based nap-of-the-object photogrammetry. This scheme yields an average reprojection error of approximately 0.01 pixels in data processing before and after rainfall, showing strong consistency in the spatial distribution of the two-stage 3D models. The mean absolute error in planar accuracy is between 0.01 m and 0.02 m, and that in elevation accuracy is approximately 0.03 m, with the lowest errors reaching the millimeter level. Therefore, control-free images combined with nap-of-the-object photogrammetry techniques can meet relevant demands for monitoring landslide erosional areas, providing technical support for extensive, safe, and efficient Benggang erosion monitoring. Full article
(This article belongs to the Special Issue Intelligent Computing and Remote Sensing—2nd Edition)
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18 pages, 3750 KiB  
Article
GPU Accelerated Processing Method for Feature Point Extraction and Matching in Satellite SAR Images
by Lei Dong, Niangang Jiao, Tingtao Zhang, Fangjian Liu and Hongjian You
Appl. Sci. 2024, 14(4), 1528; https://doi.org/10.3390/app14041528 - 14 Feb 2024
Viewed by 511
Abstract
This paper addresses the challenge of extracting feature points and image matching in Synthetic Aperture Radar (SAR) satellite images, particularly focusing on large-scale embedding. The widely used Scale Invariant Transform (SIFT) algorithm, successful in computer vision and optical satellite image matching, faces challenges [...] Read more.
This paper addresses the challenge of extracting feature points and image matching in Synthetic Aperture Radar (SAR) satellite images, particularly focusing on large-scale embedding. The widely used Scale Invariant Transform (SIFT) algorithm, successful in computer vision and optical satellite image matching, faces challenges when applied to satellite SAR images due to the presence of speckle noise, leading to increased matching errors. The SAR–SIFT method is explored and analyzed in-depth, considering the unique characteristics of satellite SAR images. To enhance the efficiency of matching identical feature points in two satellite SAR images, the paper proposes a Graphics Processing Unit (GPU) mapping implementation based on the SAR–SIFT algorithm. The paper introduces a multi-GPU collaborative acceleration strategy for SAR image matching. This strategy addresses the challenge of matching feature points in the region and embedding multiple SAR images in large areas. The goal is to achieve efficient matching processing of multiple SAR images in extensive geographical regions. The proposed multi-GPU collaborative acceleration algorithm is validated through experiments involving feature point extraction and matching using 21 GF-3 SAR images. The results demonstrate the feasibility and efficiency of the algorithm in enhancing the processing speed of matching feature points in large-scale satellite SAR images. Overall, the paper contributes to the advancement of SAR image processing techniques, specifically in feature point extraction and matching in large-scale applications. Full article
(This article belongs to the Special Issue Intelligent Computing and Remote Sensing—2nd Edition)
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