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Advanced Artificial Intelligence for Remote Sensing: Methodology and Applications

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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 June 2024 | Viewed by 11599

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Special Issue Editors

Dr. Guangliang Cheng

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Guest Editor

Department of Computer Science, University of Liverpool, Liverpool, UK
Interests: computer vision; remote sensing; change detection; hyperspectral image classification; road extraction
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Qi Zhao

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Guest Editor

School of Electronic and Information Engineering, Beihang University, No. 37 Xueyuan Road, Haidian District, Beijing 100191, China
Interests: remote sensing; image recognition; domain adaptation; few-shot learning; light-weight neural network
Special Issues, Collections and Topics in MDPI journals

Dr. Paolo Tripicchio

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Guest Editor

Gustavo Stefanini Advanced Robotics Research Center, Scuola Superiore Sant’Anna di Studi Universitari e di Perfezionamento, Pisa, Italy
Interests: artificial intelligence; computer vision; 3D reconstruction; image processing; localization methods; mapping; inspection robotics; deep Learning; industrial monitoring; smart sensors; photogrammetry; LiDAR; SAR; farming applications
Special Issues, Collections and Topics in MDPI journals

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,

The Special Issue on “Advanced Artificial Intelligence for Remote Sensing: Methodology and Applications” aims to explore the intersection of artificial intelligence (AI) and remote sensing, showcasing cutting-edge methodologies and their diverse applications in this field. Remote sensing, the science of collecting information about the earth's surface from a distance, has become an invaluable tool for monitoring and understanding our planet’s dynamics and changes. Concurrently, artificial intelligence techniques have advanced significantly, offering powerful tools for data analysis and decision making. The integration of AI techniques with remote sensing data has revolutionized the way we analyze, interpret, and extract meaningful information from large-scale Earth observation datasets, enabling us to address critical environmental, social, and economic challenges.

This Special Issue provides a platform for researchers and practitioners to present their latest research findings, methodologies, and applications in the realm of AI for remote sensing. It serves as a forum to exchange knowledge and foster collaborations among experts from diverse disciplines, including computer science, remote sensing, geoscience, and environmental studies. The issue aims to advance the understanding and utilization of AI techniques for remote sensing applications, pushing the boundaries of what can be achieved in terms of data analysis, information extraction, and decision support.

This Special Issue emphasizes the latest advancements in AI algorithms, models, and techniques that have been specifically developed or adapted for remote sensing applications. It aims to showcase novel methodologies, innovative applications, and case studies that demonstrate the potential of AI in addressing real-world challenges in agriculture, urban planning, forestry, climate change, and other domains.

We invite submissions that address various aspects of AI methodologies and their applications in remote sensing. Potential topics of interest include, but are not limited to:

AI-driven image classification and recognition in remote sensing.
Deep learning techniques for feature extraction and representation learning from remote sensing data.
The fusion of multi-source remote sensing data using AI-based approaches.
Semantic segmentation and object detection in remote sensing images
AI-based approaches for change detection and monitoring using remote sensing data.
AI-enabled hyperspectral and LiDAR data analysis.
Transfer learning and domain adaptation for remote sensing applications.
Few-shot image recognition/semantic segmentation/object detection, etc.
Domain adaptation problems in the remote sensing area.
Case studies and applications of AI in remote sensing for agriculture, urban planning, forestry, climate change, etc.

Authors are invited to submit original research articles, reviews, or survey papers that contribute to the field of advanced AI for remote sensing. All submissions will undergo a rigorous peer review process to ensure the quality and relevance of accepted papers. Manuscripts should follow the guidelines provided by the journal and should clearly address the Special Issue theme

Dr. Guangliang Cheng
Prof. Dr. Qi Zhao
Dr. Paolo Tripicchio
Dr. Hossein M. Rizeei
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

artificial intelligence
remote sensing
image recognition
semantic segmentation
object detection
change detection
environmental monitoring
deep learning
few-shot learning
domain adaptation

Published Papers (13 papers)

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Research

22 pages, 1984 KiB

Open AccessArticle

Zero-Shot Sketch-Based Remote-Sensing Image Retrieval Based on Multi-Level and Attention-Guided Tokenization

by Bo Yang, Chen Wang, Xiaoshuang Ma, Beiping Song, Zhuang Liu and Fangde Sun

Remote Sens. 2024, 16(10), 1653; https://doi.org/10.3390/rs16101653 - 07 May 2024

Viewed by 79

Abstract

Effectively and efficiently retrieving images from remote-sensing databases is a critical challenge in the realm of remote-sensing big data. Utilizing hand-drawn sketches as retrieval inputs offers intuitive and user-friendly advantages, yet the potential of multi-level feature integration from sketches remains underexplored, leading to suboptimal retrieval performance. To address this gap, our study introduces a novel zero-shot, sketch-based retrieval method for remote-sensing images, leveraging multi-level feature extraction, self-attention-guided tokenization and filtering, and cross-modality attention update. This approach employs only vision information and does not require semantic knowledge concerning the sketch and image. It starts by employing multi-level self-attention guided feature extraction to tokenize the query sketches, as well as self-attention feature extraction to tokenize the candidate images. It then employs cross-attention mechanisms to establish token correspondence between these two modalities, facilitating the computation of sketch-to-image similarity. Our method significantly outperforms existing sketch-based remote-sensing image retrieval techniques, as evidenced by tests on multiple datasets. Notably, it also exhibits robust zero-shot learning capabilities in handling unseen categories and strong domain adaptation capabilities in handling unseen novel remote-sensing data. The method’s scalability can be further enhanced by the pre-calculation of retrieval tokens for all candidate images in a database. This research underscores the significant potential of multi-level, attention-guided tokenization in cross-modal remote-sensing image retrieval. For broader accessibility and research facilitation, we have made the code and dataset used in this study publicly available online. Full article

(This article belongs to the Special Issue Advanced Artificial Intelligence for Remote Sensing: Methodology and Applications)

24 pages, 39892 KiB

Open AccessArticle

Evaluation of Ten Deep-Learning-Based Out-of-Distribution Detection Methods for Remote Sensing Image Scene Classification

by Sicong Li, Ning Li, Min Jing, Chen Ji and Liang Cheng

Remote Sens. 2024, 16(9), 1501; https://doi.org/10.3390/rs16091501 - 24 Apr 2024

Viewed by 322

Abstract

Although deep neural networks have made significant progress in tasks related to remote sensing image scene classification, most of these tasks assume that the training and test data are independently and identically distributed. However, when remote sensing scene classification models are deployed in the real world, the model will inevitably encounter situations where the distribution of the test set differs from that of the training set, leading to unpredictable errors during the inference and testing phase. For instance, in the context of large-scale remote sensing scene classification applications, it is difficult to obtain all the feature classes in the training phase. Consequently, during the inference and testing phases, the model will categorize images of unidentified unknown classes into known classes. Therefore, the deployment of out-of-distribution (OOD) detection within the realm of remote sensing scene classification is crucial for ensuring the reliability and safety of model application in real-world scenarios. Despite significant advancements in OOD detection methods in recent years, there remains a lack of a unified benchmark for evaluating various OOD methods specifically in remote sensing scene classification tasks. We designed different benchmarks on three classical remote sensing datasets to simulate scenes with different distributional shift. Ten different types of OOD detection methods were employed, and their performance was evaluated and compared using quantitative metrics. Numerous experiments were conducted to evaluate the overall performance of these state-of-the-art OOD detection methods under different test benchmarks. The comparative results show that the virtual-logit matching methods without additional training outperform the other types of methods on our benchmarks, suggesting that additional training methods are unnecessary for remote sensing image scene classification applications. Furthermore, we provide insights into OOD detection models and performance enhancement in real world. To the best of our knowledge, this study is the first evaluation and analysis of methods for detecting out-of-distribution data in remote sensing. We hope that this research will serve as a fundamental resource for future studies on out-of-distribution detection in remote sensing. Full article

(This article belongs to the Special Issue Advanced Artificial Intelligence for Remote Sensing: Methodology and Applications)

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17 pages, 5075 KiB

Open AccessArticle

CNN-BiLSTM: A Novel Deep Learning Model for Near-Real-Time Daily Wildfire Spread Prediction

by Mohammad Marjani, Masoud Mahdianpari and Fariba Mohammadimanesh

Remote Sens. 2024, 16(8), 1467; https://doi.org/10.3390/rs16081467 - 20 Apr 2024

Viewed by 722

Abstract

Wildfires significantly threaten ecosystems and human lives, necessitating effective prediction models for the management of this destructive phenomenon. This study integrates Convolutional Neural Network (CNN) and Bidirectional Long Short-Term Memory (BiLSTM) modules to develop a novel deep learning model called CNN-BiLSTM for near-real-time wildfire spread prediction to capture spatial and temporal patterns. This study uses the Visible Infrared Imaging Radiometer Suite (VIIRS) active fire product and a wide range of environmental variables, including topography, land cover, temperature, NDVI, wind informaiton, precipitation, soil moisture, and runoff to train the CNN-BiLSTM model. A comprehensive exploration of parameter configurations and settings was conducted to optimize the model’s performance. The evaluation results and their comparison with benchmark models, such as a Long Short-Term Memory (LSTM) and CNN-LSTM models, demonstrate the effectiveness of the CNN-BiLSTM model with IoU of F1 Score of 0.58 and 0.73 for validation and training sets, respectively. This innovative approach offers a promising avenue for enhancing wildfire management efforts through its capacity for near-real-time prediction, marking a significant step forward in mitigating the impact of wildfires. Full article

(This article belongs to the Special Issue Advanced Artificial Intelligence for Remote Sensing: Methodology and Applications)

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19 pages, 8487 KiB

Open AccessArticle

MRFA-Net: Multi-Scale Receptive Feature Aggregation Network for Cloud and Shadow Detection

by Jianxiang Wang, Yuanlu Li, Xiaoting Fan, Xin Zhou and Mingxuan Wu

Remote Sens. 2024, 16(8), 1456; https://doi.org/10.3390/rs16081456 - 20 Apr 2024

Viewed by 313

Abstract

The effective segmentation of clouds and cloud shadows is crucial for surface feature extraction, climate monitoring, and atmospheric correction, but it remains a critical challenge in remote sensing image processing. Cloud features are intricate, with varied distributions and unclear boundaries, making accurate extraction difficult, with only a few networks addressing this challenge. To tackle these issues, we introduce a multi-scale receptive field aggregation network (MRFA-Net). The MRFA-Net comprises an MRFA-Encoder and MRFA-Decoder. Within the encoder, the net includes the asymmetric feature extractor module (AFEM) and multi-scale attention, which capture diverse local features and enhance contextual semantic understanding, respectively. The MRFA-Decoder includes the multi-path decoder module (MDM) for blending features and the global feature refinement module (GFRM) for optimizing information via learnable matrix decomposition. Experimental results demonstrate that our model excelled in generalization and segmentation performance when addressing various complex backgrounds and different category detections, exhibiting advantages in terms of parameter efficiency and computational complexity, with the MRFA-Net achieving a mean intersection over union (MIoU) of 94.12% on our custom Cloud and Shadow dataset, and 87.54% on the open-source HRC_WHU dataset, outperforming other models by at least 0.53% and 0.62%. The proposed model demonstrates applicability in practical scenarios where features are difficult to distinguish. Full article

(This article belongs to the Special Issue Advanced Artificial Intelligence for Remote Sensing: Methodology and Applications)

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18 pages, 9421 KiB

Open AccessArticle

Remote Sensing Images Secure Distribution Scheme Based on Deep Information Hiding

by Peng Luo, Jia Liu, Jingting Xu, Qian Dang and Dejun Mu

Remote Sens. 2024, 16(8), 1331; https://doi.org/10.3390/rs16081331 - 10 Apr 2024

Viewed by 395

Abstract

To ensure the security of highly sensitive remote sensing images (RSIs) during their distribution, it is essential to implement effective content security protection methods. Generally, secure distribution schemes for remote sensing images often employ cryptographic techniques. However, sending encrypted data exposes communication behavior, which poses significant security risks to the distribution of remote sensing images. Therefore, this paper introduces deep information hiding to achieve the secure distribution of remote sensing images, which can serve as an effective alternative in certain specific scenarios. Specifically, the Deep Information Hiding for RSI Distribution (hereinafter referred to as DIH4RSID) based on an encoder–decoder network architecture with Parallel Attention Mechanism (PAM) by adversarial training is proposed. Our model is constructed with four main components: a preprocessing network (PN), an embedding network (EN), a revealing network (RN), and a discriminating network (DN). The PN module is primarily based on Inception to capture more details of RSIs and targets of different scales. The PAM module obtains features in two spatial directions to realize feature enhancement and context information integration. The experimental results indicate that our proposed algorithm achieves relatively higher visual quality and secure level compared to related methods. Additionally, after extracting the concealed content from hidden images, the average classification accuracy is unaffected. Full article

(This article belongs to the Special Issue Advanced Artificial Intelligence for Remote Sensing: Methodology and Applications)

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17 pages, 4146 KiB

Open AccessArticle

CDEST: Class Distinguishability-Enhanced Self-Training Method for Adopting Pre-Trained Models to Downstream Remote Sensing Image Semantic Segmentation

by Ming Zhang, Xin Gu, Ji Qi, Zhenshi Zhang, Hemeng Yang, Jun Xu, Chengli Peng and Haifeng Li

Remote Sens. 2024, 16(7), 1293; https://doi.org/10.3390/rs16071293 - 06 Apr 2024

Viewed by 486

Abstract

The self-supervised learning (SSL) technique, driven by massive unlabeled data, is expected to be a promising solution for semantic segmentation of remote sensing images (RSIs) with limited labeled data, revolutionizing transfer learning. Traditional ‘local-to-local’ transfer from small, local datasets to another target dataset plays an ever-shrinking role due to RSIs’ diverse distribution shifts. Instead, SSL promotes a ‘global-to-local’ transfer paradigm, in which generalized models pre-trained on arbitrarily large unlabeled datasets are fine-tuned to the target dataset to overcome data distribution shifts. However, the SSL pre-trained models may contain both useful and useless features for the downstream semantic segmentation task, due to the gap between the SSL tasks and the downstream task. To adapt such pre-trained models to semantic segmentation tasks, traditional supervised fine-tuning methods that use only a small number of labeled samples may drop out useful features due to overfitting. The main reason behind this is that supervised fine-tuning aims to map a few training samples from the high-dimensional, sparse image space to the low-dimensional, compact semantic space defined by the downstream labels, resulting in a degradation of the distinguishability. To address the above issues, we propose a class distinguishability-enhanced self-training (CDEST) method to support global-to-local transfer. First, the self-training module in CDEST introduces a semi-supervised learning mechanism to fully utilize the large amount of unlabeled data in the downstream task to increase the size and diversity of the training data, thus alleviating the problem of biased overfitting of the model. Second, the supervised and semi-supervised contrastive learning modules of CDEST can explicitly enhance the class distinguishability of features, helping to preserve the useful features learned from pre-training while adapting to downstream tasks. We evaluate the proposed CDEST method on four RSI semantic segmentation datasets, and our method achieves optimal experimental results on all four datasets compared to supervised fine-tuning as well as three semi-supervised fine-tuning methods. Full article

(This article belongs to the Special Issue Advanced Artificial Intelligence for Remote Sensing: Methodology and Applications)

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23 pages, 4205 KiB

Open AccessArticle

Exploring Uncertainty-Based Self-Prompt for Test-Time Adaptation Semantic Segmentation in Remote Sensing Images

by Ziquan Wang, Yongsheng Zhang, Zhenchao Zhang, Zhipeng Jiang, Ying Yu, Lei Li and Lei Zhang

Remote Sens. 2024, 16(7), 1239; https://doi.org/10.3390/rs16071239 - 31 Mar 2024

Viewed by 484

Abstract

Test-time adaptation (TTA) has been proven to effectively improve the adaptability of deep learning semantic segmentation models facing continuous changeable scenes. However, most of the existing TTA algorithms lack an explicit exploration of domain gaps, especially those based on visual domain prompts. To address these issues, this paper proposes a self-prompt strategy based on uncertainty, guiding the model to continuously focus on regions with high uncertainty (i.e., regions with a larger domain gap). Specifically, we still use the Mean-Teacher architecture with the predicted entropy from the teacher network serving as the input to the prompt module. The prompt module processes uncertain maps and guides the student network to focus on regions with higher entropy, enabling continuous adaptation to new scenes. This is a self-prompting strategy that requires no prior knowledge and is tested on widely used benchmarks. In terms of the average performance, our method outperformed the baseline algorithm in TTA and continual TTA settings of Cityscapes-to-ACDC by 3.3% and 3.9%, respectively. Our method also outperformed the baseline algorithm by 4.1% and 3.1% on the more difficult Cityscapes-to-(Foggy and Rainy) Cityscapes setting, which also surpasses six other current TTA methods. Full article

(This article belongs to the Special Issue Advanced Artificial Intelligence for Remote Sensing: Methodology and Applications)

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16 pages, 11666 KiB

Open AccessArticle

A Lightweight Building Extraction Approach for Contour Recovery in Complex Urban Environments

by Jiaxin He, Yong Cheng, Wei Wang, Zhoupeng Ren, Ce Zhang and Wenjie Zhang

Remote Sens. 2024, 16(5), 740; https://doi.org/10.3390/rs16050740 - 20 Feb 2024

Viewed by 644

Abstract

High-spatial-resolution urban buildings play a crucial role in urban planning, emergency response, and disaster management. However, challenges such as missing building contours due to occlusion problems (occlusion between buildings of different heights and buildings obscured by trees), uneven contour extraction due to mixing of building edges with other feature elements (roads, vehicles, and trees), and slow training speed in high-resolution image data hinder efficient and accurate building extraction. To address these issues, we propose a semantic segmentation model composed of a lightweight backbone, coordinate attention module, and pooling fusion module, which achieves lightweight building extraction and adaptive recovery of spatial contours. Comparative experiments were conducted on datasets featuring typical urban building instances in China and the Mapchallenge dataset, comparing our method with several classical and mainstream semantic segmentation algorithms. The results demonstrate the effectiveness of our approach, achieving excellent mean intersection over union (mIoU) and frames per second (FPS) scores on both datasets (China dataset: 85.11% and 110.67 FPS; Mapchallenge dataset: 90.27% and 117.68 FPS). Quantitative evaluations indicate that our model not only significantly improves computational speed but also ensures high accuracy in the extraction of urban buildings from high-resolution imagery. Specifically, on a typical urban building dataset from China, our model shows an accuracy improvement of 0.64% and a speed increase of 70.03 FPS compared to the baseline model. On the Mapchallenge dataset, our model achieves an accuracy improvement of 0.54% and a speed increase of 42.39 FPS compared to the baseline model. Our research indicates that lightweight networks show significant potential in urban building extraction tasks. In the future, the segmentation accuracy and prediction speed can be further balanced on the basis of adjusting the deep learning model or introducing remote sensing indices, which can be applied to research scenarios such as greenfield extraction or multi-class target extraction. Full article

(This article belongs to the Special Issue Advanced Artificial Intelligence for Remote Sensing: Methodology and Applications)

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23 pages, 18943 KiB

Open AccessArticle

An Approach to Large-Scale Cement Plant Detection Using Multisource Remote Sensing Imagery

by Tianzhu Li, Caihong Ma, Yongze Lv, Ruilin Liao, Jin Yang and Jianbo Liu

Remote Sens. 2024, 16(4), 729; https://doi.org/10.3390/rs16040729 - 19 Feb 2024

Viewed by 780

Abstract

The cement industry, as one of the primary contributors to global greenhouse gas emissions, accounts for 7% of the world’s carbon dioxide emissions. There is an urgent need to establish a rapid method for detecting cement plants to facilitate effective monitoring. In this study, a comprehensive method based on YOLOv5-IEG and the Thermal Signature Detection module using Google Earth optical imagery and SDGSAT-1 thermal infrared imagery was proposed to detect large-scale cement plant information, including geographic location and operational status. The improved algorithm demonstrated an increase of 4.8% in accuracy and a 7.7% improvement in [email protected]:95. In a specific empirical investigation in China, we successfully detected 781 large-scale cement plants with an accuracy of 90.8%. Specifically, of the 55 cement plants in Shandong Province, we identified 46 as operational and nine as non-operational. The successful application of advanced models and remote sensing technology in efficiently and accurately tracking the operational status of cement plants provides crucial support for environmental protection and sustainable development. Full article

(This article belongs to the Special Issue Advanced Artificial Intelligence for Remote Sensing: Methodology and Applications)

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17 pages, 18157 KiB

Open AccessArticle

Deep Learning-Based Real-Time Detection of Surface Landmines Using Optical Imaging

by Emanuele Vivoli, Marco Bertini and Lorenzo Capineri

Remote Sens. 2024, 16(4), 677; https://doi.org/10.3390/rs16040677 - 14 Feb 2024

Viewed by 1195

Abstract

This paper presents a pioneering study in the application of real-time surface landmine detection using a combination of robotics and deep learning. We introduce a novel system integrated within a demining robot, capable of detecting landmines in real time with high recall. Utilizing YOLOv8 models, we leverage both optical imaging and artificial intelligence to identify two common types of surface landmines: PFM-1 (butterfly) and PMA-2 (starfish with tripwire). Our system runs at 2 FPS on a mobile device missing at most 1.6% of targets. It demonstrates significant advancements in operational speed and autonomy, surpassing conventional methods while being compatible with other approaches like UAV. In addition to the proposed system, we release two datasets with remarkable differences in landmine and background colors, built to train and test the model performances. Full article

(This article belongs to the Special Issue Advanced Artificial Intelligence for Remote Sensing: Methodology and Applications)

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23 pages, 5562 KiB

Open AccessArticle

SMFF-YOLO: A Scale-Adaptive YOLO Algorithm with Multi-Level Feature Fusion for Object Detection in UAV Scenes

by Yuming Wang, Hua Zou, Ming Yin and Xining Zhang

Remote Sens. 2023, 15(18), 4580; https://doi.org/10.3390/rs15184580 - 18 Sep 2023

Cited by 4 | Viewed by 2291

Abstract

Object detection in images captured by unmanned aerial vehicles (UAVs) holds great potential in various domains, including civilian applications, urban planning, and disaster response. However, it faces several challenges, such as multi-scale variations, dense scenes, complex backgrounds, and tiny-sized objects. In this paper, we present a novel scale-adaptive YOLO framework called SMFF-YOLO, which addresses these challenges through a multi-level feature fusion approach. To improve the detection accuracy of small objects, our framework incorporates the ELAN-SW object detection prediction head. This newly designed head effectively utilizes both global contextual information and local features, enhancing the detection accuracy of tiny objects. Additionally, the proposed bidirectional feature fusion pyramid (BFFP) module tackles the issue of scale variations in object sizes by aggregating multi-scale features. To handle complex backgrounds, we introduce the adaptive atrous spatial pyramid pooling (AASPP) module, which enables adaptive feature fusion and alleviates the negative impact of cluttered scenes. Moreover, we adopt the Wise-IoU(WIoU) bounding box regression loss to enhance the competitiveness of different quality anchor boxes, which offers the framework a more informed gradient allocation strategy. We validate the effectiveness of SMFF-YOLO using the VisDrone and UAVDT datasets. Experimental results demonstrate that our model achieves higher detection accuracy, with AP50 reaching 54.3% for VisDrone and 42.4% for UAVDT datasets. Visual comparative experiments with other YOLO-based methods further illustrate the robustness and adaptability of our approach. Full article

(This article belongs to the Special Issue Advanced Artificial Intelligence for Remote Sensing: Methodology and Applications)

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20 pages, 3522 KiB

Open AccessArticle

Detection Method of Infected Wood on Digital Orthophoto Map–Digital Surface Model Fusion Network

by Guangbiao Wang, Hongbo Zhao, Qing Chang, Shuchang Lyu, Binghao Liu, Chunlei Wang and Wenquan Feng

Remote Sens. 2023, 15(17), 4295; https://doi.org/10.3390/rs15174295 - 31 Aug 2023

Cited by 3 | Viewed by 917

Abstract

Pine wilt disease (PWD) is a worldwide affliction that poses a significant menace to forest ecosystems. The swift and precise identification of pine trees under infection holds paramount significance in the proficient administration of this ailment. The progression of remote sensing and deep learning methodologies has propelled the utilization of target detection and recognition techniques reliant on remote sensing imagery, emerging as the prevailing strategy for pinpointing affected trees. Although the existing object detection algorithms have achieved remarkable success, virtually all methods solely rely on a Digital Orthophoto Map (DOM), which is not suitable for diseased trees detection, leading to a large false detection rate in the detection of easily confused targets, such as bare land, houses, brown herbs and so on. In order to improve the ability of detecting diseased trees and preventing the spread of the epidemic, we construct a large-scale PWD detection dataset with both DOM and Digital Surface Model (DSM) images and propose a novel detection framework, DDNet, which makes full use of the spectral features and geomorphological spatial features of remote sensing targets. The experimental results show that the proposed joint network achieves an AP50 2.4% higher than the traditional deep learning network. Full article

(This article belongs to the Special Issue Advanced Artificial Intelligence for Remote Sensing: Methodology and Applications)

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20 pages, 7667 KiB

Open AccessArticle

TPH-YOLOv5-Air: Airport Confusing Object Detection via Adaptively Spatial Feature Fusion

by Qiang Wang, Wenquan Feng, Lifan Yao, Chen Zhuang, Binghao Liu and Lijiang Chen

Remote Sens. 2023, 15(15), 3883; https://doi.org/10.3390/rs15153883 - 05 Aug 2023

Cited by 2 | Viewed by 1466

Abstract

Airport detection in remote sensing scenes is a crucial area of research, playing a key role in aircraft blind landing procedures. However, airport detection in remote sensing scenes still faces challenges such as class confusion, poor detection performance on multi-scale objects, and limited dataset availability. To address these issues, this paper proposes a novel airport detection network (TPH-YOLOv5-Air) based on adaptive spatial feature fusion (ASFF). Firstly, we construct an Airport Confusing Object Dataset (ACD) specifically tailored for remote sensing scenarios containing 9501 instances of airport confusion objects. Secondly, building upon the foundation of TPH-YOLOv5++, we adopt the ASFF structure, which not only enhances the feature extraction efficiency but also enriches feature representation. Moreover, an adaptive spatial feature fusion (ASFF) strategy based on adaptive parameter adjustment module (APAM) is proposed, which improves the feature scale invariance and enhances the detection of airports. Finally, experimental results based on the ACD dataset demonstrate that TPH-YOLOv5-Air achieves a mean average precision (mAP) of 49.4%, outperforming TPH-YOLOv5++ by 2% and the original YOLOv5 network by 3.6%. This study contributes to the advancement of airport detection in remote sensing scenes and demonstrates the practical application potential of TPH-YOLOv5-Air in this domain. Visualization and analysis further validate the effectiveness and interpretability of TPH-YOLOv5-Air. The ACD dataset is publicly available. Full article

(This article belongs to the Special Issue Advanced Artificial Intelligence for Remote Sensing: Methodology and Applications)

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