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Advanced Application of Artificial Intelligence and Machine Vision in Remote Sensing (Third Edition)

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

Deadline for manuscript submissions: 30 November 2024 | Viewed by 15271

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Special Issue Editor

Dr. Hossein M. Rizeei

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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
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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)

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Related Special Issues

Advanced Application of Artificial Intelligence and Machine Vision in Remote Sensing in Remote Sensing (21 articles)
Advanced Application of Artificial Intelligence and Machine Vision in Remote Sensing II in Remote Sensing (17 articles)

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Research

21 pages, 2923 KiB

Open AccessArticle

Multi-Scale Classification and Contrastive Regularization: Weakly Supervised Large-Scale 3D Point Cloud Semantic Segmentation

by Jingyi Wang, Jingyang He, Yu Liu, Chen Chen, Maojun Zhang and Hanlin Tan

Remote Sens. 2024, 16(17), 3319; https://doi.org/10.3390/rs16173319 - 7 Sep 2024

Viewed by 444

Abstract

With the proliferation of large-scale 3D point cloud datasets, the high cost of per-point annotation has spurred the development of weakly supervised semantic segmentation methods. Current popular research mainly focuses on single-scale classification, which fails to address the significant feature scale differences between background and objects in large scenes. Therefore, we propose MCCR (Multi-scale Classification and Contrastive Regularization), an end-to-end semantic segmentation framework for large-scale 3D scenes under weak supervision. MCCR first aggregates features and applies random downsampling to the input data. Then, it captures the local features of a random point based on multi-layer features and the input coordinates. These features are then fed into the network to obtain the initial and final prediction results, and MCCR iteratively trains the model using strategies such as contrastive learning. Notably, MCCR combines multi-scale classification with contrastive regularization to fully exploit multi-scale features and weakly labeled information. We investigate both point-level and local contrastive regularization to leverage point cloud augmentor and local semantic information and introduce a Decoupling Layer to guide the loss optimization in different spaces. Results on three popular large-scale datasets, S3DIS, SemanticKITTI and SensatUrban, demonstrate that our model achieves state-of-the-art (SOTA) performance on large-scale outdoor datasets with only 0.1% labeled points for supervision, while maintaining strong performance on indoor datasets. Full article

(This article belongs to the Special Issue Advanced Application of Artificial Intelligence and Machine Vision in Remote Sensing (Third Edition))

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26 pages, 5826 KiB

Open AccessArticle

An Efficient Task Implementation Modeling Framework with Multi-Stage Feature Selection and AutoML: A Case Study in Forest Fire Risk Prediction

by Ye Su, Longlong Zhao, Hongzhong Li, Xiaoli Li, Jinsong Chen and Yuankai Ge

Remote Sens. 2024, 16(17), 3190; https://doi.org/10.3390/rs16173190 - 29 Aug 2024

Viewed by 475

Abstract

As data science advances, automated machine learning (AutoML) gains attention for lowering barriers, saving time, and enhancing efficiency. However, with increasing data dimensionality, AutoML struggles with large-scale feature sets. Effective feature selection is crucial for efficient AutoML in multi-task applications. This study proposes an efficient modeling framework combining a multi-stage feature selection (MSFS) algorithm and AutoSklearn, a robust and efficient AutoML framework, to address high-dimensional data challenges. The MSFS algorithm includes three stages: mutual information gain (MIG), recursive feature elimination with cross-validation (RFECV), and a voting aggregation mechanism, ensuring comprehensive consideration of feature correlation, importance, and stability. Based on multi-source and time series remote sensing data, this study pioneers the application of AutoSklearn for forest fire risk prediction. Using this case study, we compare MSFS with five other feature selection (FS) algorithms, including three single FS algorithms and two hybrid FS algorithms. Results show that MSFS selects half of the original features (12/24), effectively handling collinearity (eliminating 11 out of 13 collinear feature groups) and increasing AutoSklearn’s success rate by 15%, outperforming two FS algorithms with the same number of features by 7% and 5%. Among the six FS algorithms and non-FS, MSFS demonstrates the highest prediction performance and stability with minimal variance (0.09%) across five evaluation metrics. MSFS efficiently filters redundant features, enhancing AutoSklearn’s operational efficiency and generalization ability in high-dimensional tasks. The MSFS–AutoSklearn framework significantly improves AutoML’s production efficiency and prediction accuracy, facilitating the efficient implementation of various real-world tasks and the wider application of AutoML. Full article

(This article belongs to the Special Issue Advanced Application of Artificial Intelligence and Machine Vision in Remote Sensing (Third Edition))

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20 pages, 2725 KiB

Open AccessArticle

Soil Organic Carbon Estimation via Remote Sensing and Machine Learning Techniques: Global Topic Modeling and Research Trend Exploration

by Tong Li, Lizhen Cui, Yu Wu, Timothy I. McLaren, Anquan Xia, Rajiv Pandey, Hongdou Liu, Weijin Wang, Zhihong Xu, Xiufang Song, Ram C. Dalal and Yash P. Dang

Remote Sens. 2024, 16(17), 3168; https://doi.org/10.3390/rs16173168 - 27 Aug 2024

Viewed by 946

Abstract

Understanding and monitoring soil organic carbon (SOC) stocks is crucial for ecosystem carbon cycling, services, and addressing global environmental challenges. This study employs the BERTopic model and bibliometric trend analysis exploration to comprehensively analyze global SOC estimates. BERTopic, a topic modeling technique based on BERT (bidirectional encoder representatives from transformers), integrates recent advances in natural language processing. The research analyzed 1761 papers on SOC and remote sensing (RS), in addition to 490 related papers on machine learning (ML) techniques. BERTopic modeling identified nine research themes for SOC estimation using RS, emphasizing spectral prediction models, carbon cycle dynamics, and agricultural impacts on SOC. In contrast, for the literature on RS and ML it identified five thematic clusters: spatial forestry analysis, hyperspectral soil analysis, agricultural deep learning, the multitemporal imaging of farmland SOC, and RS platforms (Sentinel-2 and synthetic aperture radar, SAR). From 1991 to 2023, research on SOC estimation using RS and ML has evolved from basic mapping to topics like carbon sequestration and modeling with Sentinel-2A and big data. In summary, this study traces the historical growth and thematic evolution of SOC research, identifying synergies between RS and ML and focusing on SOC estimation with advanced ML techniques. These findings are critical to global ecosystem SOC assessments and environmental policy formulation. Full article

(This article belongs to the Special Issue Advanced Application of Artificial Intelligence and Machine Vision in Remote Sensing (Third Edition))

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26 pages, 75294 KiB

Open AccessArticle

SOD-YOLO: Small-Object-Detection Algorithm Based on Improved YOLOv8 for UAV Images

by Yangang Li, Qi Li, Jie Pan, Ying Zhou, Hongliang Zhu, Hongwei Wei and Chong Liu

Remote Sens. 2024, 16(16), 3057; https://doi.org/10.3390/rs16163057 - 20 Aug 2024

Viewed by 1165

Abstract

The rapid development of unmanned aerial vehicle (UAV) technology has contributed to the increasing sophistication of UAV-based object-detection systems, which are now extensively utilized in civilian and military sectors. However, object detection from UAV images has numerous challenges, including significant variations in the object size, changing spatial configurations, and cluttered backgrounds with multiple interfering elements. To address these challenges, we propose SOD-YOLO, an innovative model based on the YOLOv8 model, to detect small objects in UAV images. The model integrates the receptive field convolutional block attention module (RFCBAM) in the backbone network to perform downsampling, improving feature extraction efficiency and mitigating the spatial information sparsity caused by downsampling. Additionally, we developed a novel neck architecture called the balanced spatial and semantic information fusion pyramid network (BSSI-FPN) designed for multi-scale feature fusion. The BSSI-FPN effectively balances spatial and semantic information across feature maps using three primary strategies: fully utilizing large-scale features, increasing the frequency of multi-scale feature fusion, and implementing dynamic upsampling. The experimental results on the VisDrone2019 dataset demonstrate that SOD-YOLO-s improves the mAP50 indicator by 3% compared to YOLOv8s while reducing the number of parameters and computational complexity by 84.2% and 30%, respectively. Compared to YOLOv8l, SOD-YOLO-l improves the mAP50 indicator by 7.7% and reduces the number of parameters by 59.6%. Compared to other existing methods, SODA-YOLO-l achieves the highest detection accuracy, demonstrating the superiority of the proposed method. Full article

(This article belongs to the Special Issue Advanced Application of Artificial Intelligence and Machine Vision in Remote Sensing (Third Edition))

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20 pages, 7768 KiB

Open AccessArticle

SimNFND: A Forward-Looking Sonar Denoising Model Trained on Simulated Noise-Free and Noisy Data

by Taihong Yang, Tao Zhang and Yiqing Yao

Remote Sens. 2024, 16(15), 2815; https://doi.org/10.3390/rs16152815 - 31 Jul 2024

Viewed by 434

Abstract

Given the propagation characteristics of sound waves and the complexity of the underwater environment, denoising forward-looking sonar image data presents a formidable challenge. Existing studies often add noise to sonar images and then explore methods for its removal. This approach neglects the inherent complex noise in sonar images, resulting in inaccurate evaluations of traditional denoising methods and poor learning of noise characteristics by deep learning models. To address the lack of high-quality data for FLS denoising model training, we propose a simulation algorithm for forward-looking sonar data based on RGBD data. By utilizing rendering techniques and noise simulation algorithms, high-quality noise-free and noisy sonar data can be rapidly generated from existing RGBD data. Based on these data, we optimize the loss function and training process of the FLS denoising model, achieving significant improvements in noise removal and feature preservation compared to other methods. Finally, this paper performs both qualitative and quantitative analyses of the algorithm’s performance using real and simulated sonar data. Compared to the latest FLS denoising models based on traditional methods and deep learning techniques, our method demonstrates significant advantages in denoising capability. All inference results for the Marine Debris Dataset (MDD) have been made open source, facilitating subsequent research and comparison. Full article

(This article belongs to the Special Issue Advanced Application of Artificial Intelligence and Machine Vision in Remote Sensing (Third Edition))

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22 pages, 20661 KiB

Open AccessArticle

Automated Flood Prediction along Railway Tracks Using Remotely Sensed Data and Traditional Flood Models

by Abdul-Rashid Zakaria, Thomas Oommen and Pasi Lautala

Remote Sens. 2024, 16(13), 2332; https://doi.org/10.3390/rs16132332 - 26 Jun 2024

Viewed by 1208

Abstract

Ground hazards are a significant problem in the global economy, costing millions of dollars in damage each year. Railroad tracks are vulnerable to ground hazards like flooding since they traverse multiple terrains with complex environmental factors and diverse human developments. Traditionally, flood-hazard assessments are generated using models like the Hydrological Engineering Center–River Analysis System (HEC-RAS). However, these maps are typically created for design flood events (10, 50, 100, 500 years) and are not available for any specific storm event, as they are not designed for individual flood predictions. Remotely sensed methods, on the other hand, offer precise flood extents only during the flooding, which means the actual flood extents cannot be determined beforehand. Railroad agencies need daily flood extent maps before rainfall events to manage and plan for the parts of the railroad network that will be impacted during each rainfall event. A new approach would involve using traditional flood-modeling layers and remotely sensed flood model outputs such as flood maps created using the Google Earth Engine. These new approaches will use machine-learning tools in flood prediction and extent mapping. This new approach will allow for determining the extent of flood for each rainfall event on a daily basis using rainfall forecast; therefore, flooding extents will be modeled before the actual flood, allowing railroad managers to plan for flood events pre-emptively. Two approaches were used: support vector machines and deep neural networks. Both methods were fine-tuned using grid-search cross-validation; the deep neural network model was chosen as the best model since it was computationally less expensive in training the model and had fewer type II errors or false negatives, which were the priorities for the flood modeling and would be suitable for developing the automated system for the entire railway corridor. The best deep neural network was then deployed and used to assess the extent of flooding for two floods in 2020 and 2022. The results indicate that the model accurately approximates the actual flooding extent and can predict flooding on a daily temporal basis using rainfall forecasts. Full article

(This article belongs to the Special Issue Advanced Application of Artificial Intelligence and Machine Vision in Remote Sensing (Third Edition))

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19 pages, 6891 KiB

Open AccessArticle

Enhancing Machine Learning Performance in Estimating CDOM Absorption Coefficient via Data Resampling

by Jinuk Kim, Jin Hwi Kim, Wonjin Jang, JongCheol Pyo, Hyuk Lee, Seohyun Byeon, Hankyu Lee, Yongeun Park and Seongjoon Kim

Remote Sens. 2024, 16(13), 2313; https://doi.org/10.3390/rs16132313 - 25 Jun 2024

Viewed by 493

Abstract

Chromophoric dissolved organic matter (CDOM) is a mixture of various types of organic matter and a useful parameter for monitoring complex inland surface waters. Remote sensing has been widely utilized to detect CDOM in various studies; however, in many cases, the dataset is relatively imbalanced in a single region. To address these concerns, data were acquired from hyperspectral images, field reflection spectra, and field monitoring data, and the imbalance problem was solved using a synthetic minority oversampling technique (SMOTE). Using the on-site reflectance ratio of the hyperspectral images, the input variables R_rs (452/497), R_rs (497/580), R_rs (497/618), and R_rs (684/618), which had the highest correlation with the CDOM absorption coefficient a_CDOM (355), were extracted. Random forest and light gradient boosting machine algorithms were applied to create a CDOM prediction algorithm via machine learning, and to apply SMOTE, low-concentration and high-concentration datasets of CDOM were distinguished by 5 m⁻¹. The training and testing datasets were distinguished at a 75%:25% ratio at low and high concentrations, and SMOTE was applied to generate synthetic data based on the training dataset, which is a sub-dataset of the original dataset. Datasets using SMOTE resulted in an overall improvement in the algorithmic accuracy of the training and test step. The random forest model was selected as the optimal model for CDOM prediction. In the best-case scenario of the random forest model, the SMOTE algorithm showed superior performance, with testing R², absolute error (MAE), and root mean square error (RMSE) values of 0.838, 0.566, and 0.777 m⁻¹, respectively, compared to the original algorithm’s test values of 0.722, 0.493, and 0.802 m⁻¹. This study is anticipated to resolve imbalance problems using SMOTE when predicting remote sensing-based CDOM. It is expected to produce and implement a machine learning model with improved reliable performance. Full article

(This article belongs to the Special Issue Advanced Application of Artificial Intelligence and Machine Vision in Remote Sensing (Third Edition))

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28 pages, 26836 KiB

Open AccessArticle

Effective Training and Inference Strategies for Point Classification in LiDAR Scenes

by Mariona Carós, Ariadna Just, Santi Seguí and Jordi Vitrià

Remote Sens. 2024, 16(12), 2153; https://doi.org/10.3390/rs16122153 - 13 Jun 2024

Viewed by 790

Abstract

Light Detection and Ranging systems serve as robust tools for creating three-dimensional representations of the Earth’s surface. These representations are known as point clouds. Point cloud scene segmentation is essential in a range of applications aimed at understanding the environment, such as infrastructure planning and monitoring. However, automating this process can result in notable challenges due to variable point density across scenes, ambiguous object shapes, and substantial class imbalances. Consequently, manual intervention remains prevalent in point classification, allowing researchers to address these complexities. In this work, we study the elements contributing to the automatic semantic segmentation process with deep learning, conducting empirical evaluations on a self-captured dataset by a hybrid airborne laser scanning sensor combined with two nadir cameras in RGB and near-infrared over a 247 km² terrain characterized by hilly topography, urban areas, and dense forest cover. Our findings emphasize the importance of employing appropriate training and inference strategies to achieve accurate classification of data points across all categories. The proposed methodology not only facilitates the segmentation of varying size point clouds but also yields a significant performance improvement compared to preceding methodologies, achieving a mIoU of 94.24% on our self-captured dataset. Full article

(This article belongs to the Special Issue Advanced Application of Artificial Intelligence and Machine Vision in Remote Sensing (Third Edition))

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19 pages, 44215 KiB

Open AccessArticle

Algal Bed Region Segmentation Based on a ViT Adapter Using Aerial Images for Estimating CO₂ Absorption Capacity

by Guang Li, Ren Togo, Keisuke Maeda, Akinori Sako, Isao Yamauchi, Tetsuya Hayakawa, Shigeyuki Nakamae, Takahiro Ogawa and Miki Haseyama

Remote Sens. 2024, 16(10), 1742; https://doi.org/10.3390/rs16101742 - 14 May 2024

Viewed by 775

Abstract

In this study, we propose a novel method for algal bed region segmentation using aerial images. Accurately determining the carbon dioxide absorption capacity of coastal algae requires measurements of algal bed regions. However, conventional manual measurement methods are resource-intensive and time-consuming, which hinders the advancement of the field. To solve these problems, we propose a novel method for automatic algal bed region segmentation using aerial images. In our method, we use an advanced semantic segmentation model, a ViT adapter, and adapt it to aerial images for algal bed region segmentation. Our method demonstrates high accuracy in identifying algal bed regions in an aerial image dataset collected from Hokkaido, Japan. The experimental results for five different ecological regions show that the mean intersection over union (mIoU) and mean F-score of our method in the validation set reach 0.787 and 0.870, the IoU and F-score for the background region are 0.957 and 0.978, and the IoU and F-score for the algal bed region are 0.616 and 0.762, respectively. In particular, the mean recognition area compared with the ground truth area annotated manually is 0.861. Our study contributes to the advancement of blue carbon assessment by introducing a novel semantic segmentation-based method for identifying algal bed regions using aerial images. Full article

(This article belongs to the Special Issue Advanced Application of Artificial Intelligence and Machine Vision in Remote Sensing (Third Edition))

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18 pages, 3629 KiB

Open AccessArticle

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

Cited by 1 | Viewed by 3095

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 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 (Third Edition))

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19 pages, 5673 KiB

Open AccessArticle

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

Cited by 2 | Viewed by 834

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 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 (Third Edition))

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22 pages, 35245 KiB

Open AccessArticle

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

Cited by 1 | Viewed by 1704

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.

^{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 (Third Edition))

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15 pages, 22774 KiB

Open AccessArticle

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 - 4 Mar 2024

Cited by 7 | Viewed by 1680

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 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 (Third Edition))

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