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Advanced Application of Artificial Intelligence and Machine Vision in Remote Sensing III

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

Deadline for manuscript submissions: 30 April 2024 | Viewed by 2716

Special Issue Editor

Dr. Hossein M. Rizeei

E-Mail Website
Guest Editor
1. Faculty of Engineering and IT, University of Technology Sydney, Ultimo, NSW, Australia
2. McGregor Coxall Australia Pty Ltd., Sydney, NSW, Australia
Interests: machine learning; geospatial 3D analysis; geospatial database querying; web GIS; airborne/spaceborne image processing; feature extraction; time-series analysis in forecasting modelling and domain adaptation in various environmental applications
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We are excited to introduce a new Special Issue, building upon the success of our previous endeavor, “Advanced Application of Artificial Intelligence and Machine Vision in Remote Sensing”. This forthcoming edition dives deeper into the confluence of cutting-edge technology, with a particular focus on drone-based and LiDAR-based image processing as well as the integration of artificial intelligence (AI) in urban planning.

Over the last decade, AI and machine learning (ML) techniques have significantly impacted image processing and spatial analysis across various applications. AI has empowered us to unlock the true potential of imagery data, employing tailored algorithms for tasks such as classification, regression, clustering, spatial correlation modeling, and more. Deep neural networks, commonly known as deep learning, stand out as powerful tools within this domain, performing functions like pattern recognition, feature detection, trend prediction, instance segmentation, semantic segmentation, and image classification within neural network frameworks.

Traditionally, structured remotely sensed data often required painstaking manual labelling for training models, a subjective and non-transferable process. To address these challenges, "machine vision" (MV) has emerged as a holistic solution, streamlining the workflow from image acquisition to knowledge extraction. MV leverages AI technology to minimise computation time and maximise replicable accuracy, encompassing software products and hardware architectures such as CPUs, GPU/FPGA combinations, parallel processing, and computer vision techniques.

In this Special Issue, we invite scholarly manuscripts proposing frameworks that combine machine vision with state-of-the-art AI techniques and geospatial information systems to automate the processing of remotely sensed imagery from diverse sources, including drones, LiDAR, radar, SAR, and multispectral sensors. The primary objective is to achieve higher precision in a range of spatial applications, from urban planning to environmental studies, weather and climate analysis, the energy sector, natural resource management, landscape assessment, and geo-hazard monitoring.

As we explore this Special Issue, we anticipate groundbreaking contributions that will reshape urban planning and related domains. These endeavors, enriched by drone-based and LiDAR-based image processing, alongside innovative image processing AI, will guide us towards a more intelligent, efficient, and sustainable future.

Dr. Hossein M. Rizeei
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. 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

  • artificial intelligence (AI)
  • machine vision (MV)
  • machine learning (ML)
  • geospatial information systems (GIS)
  • optimisation
  • spatial framework
  • deep learning (DL)

Related Special Issues

Published Papers (4 papers)

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Research

18 pages, 3629 KiB  
Article
RS-LLaVA: A Large Vision-Language Model for Joint Captioning and Question Answering in Remote Sensing Imagery
by Yakoub Bazi, Laila Bashmal, Mohamad Mahmoud Al Rahhal, Riccardo Ricci and Farid Melgani
Remote Sens. 2024, 16(9), 1477; https://doi.org/10.3390/rs16091477 - 23 Apr 2024
Viewed by 373
Abstract
In this paper, we delve into the innovative application of large language models (LLMs) and their extension, large vision-language models (LVLMs), in the field of remote sensing (RS) image analysis. We particularly emphasize their multi-tasking potential with a focus on image captioning and [...] Read more.
In this paper, we delve into the innovative application of large language models (LLMs) and their extension, large vision-language models (LVLMs), in the field of remote sensing (RS) image analysis. We particularly emphasize their multi-tasking potential with a focus on image captioning and visual question answering (VQA). In particular, we introduce an improved version of the Large Language and Vision Assistant Model (LLaVA), specifically adapted for RS imagery through a low-rank adaptation approach. To evaluate the model performance, we create the RS-instructions dataset, a comprehensive benchmark dataset that integrates four diverse single-task datasets related to captioning and VQA. The experimental results confirm the model’s effectiveness, marking a step forward toward the development of efficient multi-task models for RS image analysis. Full article
(This article belongs to the Special Issue Advanced Application of Artificial Intelligence and Machine Vision in Remote Sensing III)
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19 pages, 5673 KiB  
Article
M-SKSNet: Multi-Scale Spatial Kernel Selection for Image Segmentation of Damaged Road Markings
by Junwei Wang, Xiaohan Liao, Yong Wang, Xiangqiang Zeng, Xiang Ren, Huanyin Yue and Wenqiu Qu
Remote Sens. 2024, 16(9), 1476; https://doi.org/10.3390/rs16091476 - 23 Apr 2024
Viewed by 295
Abstract
It is a challenging task to accurately segment damaged road markings from images, mainly due to their fragmented, dense, small-scale, and blurry nature. This study proposes a multi-scale spatial kernel selection net named M-SKSNet, a novel model that integrates a transformer and a [...] Read more.
It is a challenging task to accurately segment damaged road markings from images, mainly due to their fragmented, dense, small-scale, and blurry nature. This study proposes a multi-scale spatial kernel selection net named M-SKSNet, a novel model that integrates a transformer and a multi-dilated large kernel convolutional neural network (MLKC) block to address these issues. Through integrating multiple scales of information, the model can extract high-quality and semantically rich features while generating damage-specific representations. This is achieved by leveraging both the local and global contexts, as well as self-attention mechanisms. The performance of M-SKSNet is evaluated both quantitatively and qualitatively, and the results show that M-SKSNet achieved the highest improvement in F1 by 3.77% and in IOU by 4.6%, when compared to existing models. Additionally, the effectiveness of M-SKSNet in accurately extracting damaged road markings from images in various complex scenarios (including city roads and highways) is demonstrated. Furthermore, M-SKSNet is found to outperform existing alternatives in terms of both robustness and accuracy. Full article
(This article belongs to the Special Issue Advanced Application of Artificial Intelligence and Machine Vision in Remote Sensing III)
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22 pages, 35245 KiB  
Article
DCEF2-YOLO: Aerial Detection YOLO with Deformable Convolution–Efficient Feature Fusion for Small Target Detection
by Yeonha Shin, Heesub Shin, Jaewoo Ok, Minyoung Back, Jaehyuk Youn and Sungho Kim
Remote Sens. 2024, 16(6), 1071; https://doi.org/10.3390/rs16061071 - 18 Mar 2024
Viewed by 816
Abstract
Deep learning technology for real-time small object detection in aerial images can be used in various industrial environments such as real-time traffic surveillance and military reconnaissance. However, detecting small objects with few pixels and low resolution remains a challenging problem that requires performance [...] Read more.
Deep learning technology for real-time small object detection in aerial images can be used in various industrial environments such as real-time traffic surveillance and military reconnaissance. However, detecting small objects with few pixels and low resolution remains a challenging problem that requires performance improvement. To improve the performance of small object detection, we propose DCEF 2-YOLO. Our proposed method enables efficient real-time small object detection by using a deformable convolution (DFConv) module and an efficient feature fusion structure to maximize the use of the internal feature information of objects. DFConv preserves small object information by preventing the mixing of object information with the background. The optimized feature fusion structure produces high-quality feature maps for efficient real-time small object detection while maximizing the use of limited information. Additionally, modifying the input data processing stage and reducing the detection layer to suit small object detection also contributes to performance improvement. When compared to the performance of the latest YOLO-based models (such as DCN-YOLO and YOLOv7), DCEF 2-YOLO outperforms them, with a mAP of +6.1% on the DOTA-v1.0 test set, +0.3% on the NWPU VHR-10 test set, and +1.5% on the VEDAI512 test set. Furthermore, it has a fast processing speed of 120.48 FPS with an RTX3090 for 512 × 512 images, making it suitable for real-time small object detection tasks. Full article
(This article belongs to the Special Issue Advanced Application of Artificial Intelligence and Machine Vision in Remote Sensing III)
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15 pages, 22774 KiB  
Article
Object Detection in Remote Sensing Images Based on Adaptive Multi-Scale Feature Fusion Method
by Chun Liu, Sixuan Zhang, Mengjie Hu and Qing Song
Remote Sens. 2024, 16(5), 907; https://doi.org/10.3390/rs16050907 - 04 Mar 2024
Viewed by 745
Abstract
Multi-scale object detection is critical for analyzing remote sensing images. Traditional feature pyramid networks, which are aimed at accommodating objects of varying sizes through multi-level feature extraction, face significant challenges due to the diverse scale variations present in remote sensing images. This situation [...] Read more.
Multi-scale object detection is critical for analyzing remote sensing images. Traditional feature pyramid networks, which are aimed at accommodating objects of varying sizes through multi-level feature extraction, face significant challenges due to the diverse scale variations present in remote sensing images. This situation often forces single-level features to span a broad spectrum of object sizes, complicating accurate localization and classification. To tackle these challenges, this paper proposes an innovative algorithm that incorporates an adaptive multi-scale feature enhancement and fusion module (ASEM), which enhances remote sensing image object detection through sophisticated multi-scale feature fusion. Our method begins by employing a feature pyramid to gather coarse multi-scale features. Subsequently, it integrates a fine-grained feature extraction module at each level, utilizing atrous convolutions with varied dilation rates to refine multi-scale features, which markedly improves the information capture from widely varied object scales. Furthermore, an adaptive enhancement module is applied to the features of each level by employing an attention mechanism for feature fusion. This strategy concentrates on the features of critical scale, which significantly enhance the effectiveness of capturing essential feature information. Compared with the baseline method, namely, Rotated FasterRCNN, our method achieved an mAP of 74.21% ( 0.81%) on the DOTA-v1.0 dataset and an mAP of 84.90% (+9.2%) on the HRSC2016 dataset. These results validated the effectiveness and practicality of our method and demonstrated its significant application value in multi-scale remote sensing object detection tasks. Full article
(This article belongs to the Special Issue Advanced Application of Artificial Intelligence and Machine Vision in Remote Sensing III)
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