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Artificial Intelligence-Driven Methods for Remote Sensing Target and Object Detection II

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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: 25 December 2024 | Viewed by 16244

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Special Issue Editors

Dr. Yanni Dong

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Institute of Geophysics and Geomatics, China University of Geosciences, Wuhan 430074, China
Interests: hyperspectral remote sensing image processing; target detection; dimensionality reduction; classification; metric learning; transfer learning; deep learning; lithologic mapping; geological application of remote sensing
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Dr. Xiaochen Yang

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School of Mathematics and Statistics, University of Glasgow, Glasgow, UK
Interests: distance metric learning; few-shot learning; hyperspectral image analysis; statistical classification
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Qian Du

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Guest Editor

Department of Electrical and Computer Engineering, Mississippi State University, Starkville, MS 39762, USA
Interests: hyperspectral imagery; remote sensing; intelligent processing; machine learning; pattern recognition
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Remote sensing image includes a rich description of the earth’s surface in various modalities (hyperspectral data, high resolution data, multispectral data, synthetic aperture radar (SAR) data, etc.). Remote sensing target detection or object detection aims to determine whether there are targets or objects of interest in the image, playing a decisive role in resource detection, environmental monitoring, urban planning, national security, agriculture, forestry, climate, hydrolog, etc. In recent years, artificial intelligence (AI) has achieved considerable development and been successfully applied for various applications, such as regression, clustering, classification, etc. Although AI-driven approaches can handle the massive quantities of data acquired by remote sensors, they require many high-quality labeled samples to deal with remote sensing big data, leading to fragile results. That is, AI-driven approaches with strong feature extraction abilities have limited performance and are still far from practical demands. Thus, target detection or object detection in the presence of complicated backgrounds with limited labeled samples remains a challenging mission. There is still much room for research on remote sensing target detection and object detection. The main goal of this Special Issue is to address advanced topics related to remote sensing target detection and object detection.

Topics of interests include but are not limited to the following:

New AI-driven methods for remote sensing data, such as GNN, transformer, etc.;
New remote sensing datasets, including hyperspectral, high resolution, SAR datasets, etc.;
Machine learning techniques for remote sensing applications, such as domain adaptation, few-shot learning, manifold learning, and metric learning;
Machine learning-based drone detection and fine-grained detection;
Target detection, object detection, and anomaly detection;
Data-driven applications in remote sensing;
Technique reviews on related topics.

Dr. Yanni Dong
Dr. Xiaochen Yang
Prof. Dr. Qian Du
Guest Editors

Manuscript Submission Information

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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

remote sensing
target detection
artificial intelligence
machine learning
deep learning
object detection
new datasets

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Related Special Issue

Artificial Intelligence-Driven Methods for Remote Sensing Target and Object Detection in Remote Sensing (29 articles)

Published Papers (11 papers)

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Research

22 pages, 5176 KiB

Open AccessArticle

A Reparameterization Feature Redundancy Extract Network for Unmanned Aerial Vehicles Detection

by Shijie Zhang, Xu Yang, Chao Geng and Xinyang Li

Remote Sens. 2024, 16(22), 4226; https://doi.org/10.3390/rs16224226 - 13 Nov 2024

Viewed by 322

Abstract

In unmanned aerial vehicles (UAVs) detection, challenges such as occlusion, complex backgrounds, motion blur, and inference time often lead to false detections and missed detections. General object detection frameworks encounter difficulties in adequately tackling these challenges, leading to substantial information loss during network downsampling, inadequate feature fusion, and being unable to meet real-time requirements. In this paper, we propose a Real-Time Small Object Detection YOLO (RTSOD-YOLO) model to tackle the various challenges faced in UAVs detection. We further enhance the adaptive nature of the Adown module by incorporating an adaptive spatial attention mechanism. This mechanism processes the downsampled feature maps, enabling the model to better focus on key regions. Secondly, to address the issue of insufficient feature fusion, we employ combined serial and parallel triple feature encoding (TFE). This approach fuses scale-sequence features from both shallow features and twice-encoded features, resulting in a new small-scale object detection layer. While enhancing the global context awareness of the existing detection layers, this also enriches the small-scale object detection layer with detailed information. Since rich redundant features often ensure a comprehensive understanding of the input, which is a key characteristic of deep neural networks, we propose a more efficient redundant feature generation module. This module generates more feature maps with fewer parameters. Additionally, we introduce reparameterization techniques to compensate for potential feature loss while further improving the model’s inference speed. Experimental results demonstrate that our proposed RTSOD-YOLO achieves superior detection performance, with

{mAP}_{50}

{mAP}_{50 : 95}

reaching 97.3%/51.7%, which represents improvement of 3%/3.5% over YOLOv8, and 2.6%/0.1% higher than YOLOv10. Additionally, it has the lowest parameter count and FLOPs, making it highly efficient in terms of computational resources. Full article

(This article belongs to the Special Issue Artificial Intelligence-Driven Methods for Remote Sensing Target and Object Detection II)

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26 pages, 28936 KiB

Open AccessArticle

L1RR: Model Pruning Using Dynamic and Self-Adaptive Sparsity for Remote-Sensing Target Detection to Prevent Target Feature Loss

by Qiong Ran, Mengwei Li, Boya Zhao, Zhipeng He and Yuanfeng Wu

Remote Sens. 2024, 16(11), 2026; https://doi.org/10.3390/rs16112026 - 5 Jun 2024

Viewed by 813

Abstract

Limited resources for edge computing platforms in airborne and spaceborne imaging payloads prevent using complex image processing models. Model pruning can eliminate redundant parameters and reduce the computational load, enhancing processing efficiency on edge computing platforms. Current challenges in model pruning for remote-sensing object detection include the risk of losing target features, particularly during sparse training and pruning, and difficulties in maintaining channel correspondence for residual structures, often resulting in retaining redundant features that compromise the balance between model size and accuracy. To address these challenges, we propose the L1 reweighted regularization (L1RR) pruning method. Leveraging dynamic and self-adaptive sparse modules, we optimize L1 sparsity regularization, preserving the model’s target feature information using a feature attention loss mechanism to determine appropriate pruning ratios. Additionally, we propose a residual reconstruction procedure, which removes redundant feature channels from residual structures while maintaining the residual inference structure through output channel recombination and input channel recombination, achieving a balance between model size and accuracy. Validation on two remote-sensing datasets demonstrates significant reductions in parameters and floating point operations (FLOPs) of 77.54% and 65%, respectively, and a 48.5% increase in the inference speed on the Jetson TX2 platform. This framework optimally maintains target features and effectively distinguishes feature channel importance compared to other methods, significantly enhancing feature channel robustness for difficult targets and expanding pruning applicability to less difficult targets. Full article

(This article belongs to the Special Issue Artificial Intelligence-Driven Methods for Remote Sensing Target and Object Detection II)

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23 pages, 21845 KiB

Open AccessArticle

Multi-Scale Object Detection in Remote Sensing Images Based on Feature Interaction and Gaussian Distribution

by Ruixing Yu, Haixing Cai, Boyu Zhang and Tao Feng

Remote Sens. 2024, 16(11), 1988; https://doi.org/10.3390/rs16111988 - 31 May 2024

Cited by 2 | Viewed by 831

Abstract

Remote sensing images are usually obtained from high-altitude observation. The spatial resolution of the images varies greatly and there are scale differences both between and within object classes, resulting in a diversified distribution of object scales. In order to solve these problems, we propose a novel object detection algorithm that maintains adaptability to multi-scale object detection based on feature interaction and Gaussian distribution in remote sensing images. The proposed multi-scale feature interaction model constructs feature interaction modules in the feature layer and spatial domain and combines them to fully utilize the spatial and semantic information of multi-level features. The proposed regression loss algorithm based on Gaussian distribution takes the normalized generalized Jensen–Shannon divergence with Gaussian angle loss as the regression loss function to ensure the scale invariance of the model. The experimental results demonstrate that our method achieves 77.29% mAP on the DOTA-v1.0 dataset and 97.95% mAP on the HRSC2016 dataset, which are, respectively, 1.12% and 1.41% higher than that of the baseline. These experimental results indicate the effectiveness of our method for object detection in remote sensing images. Full article

(This article belongs to the Special Issue Artificial Intelligence-Driven Methods for Remote Sensing Target and Object Detection II)

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22 pages, 30157 KiB

Open AccessArticle

DaliWS: A High-Resolution Dataset with Precise Annotations for Water Segmentation in Synthetic Aperture Radar Images

by Shanshan Zhang, Weibin Li, Rongfang Wang, Chenbin Liang, Xihui Feng and Yanhua Hu

Remote Sens. 2024, 16(4), 720; https://doi.org/10.3390/rs16040720 - 18 Feb 2024

Cited by 2 | Viewed by 1788

Abstract

The frequent occurrence of global flood disasters leads to millions of people falling into poverty each year, which poses immense pressure on governments and hinders social development. Therefore, providing more data support for flood disaster detection is of paramount importance. To facilitate the development of water body detection algorithms, we create the DaliWS dataset for water segmentation, which contains abundant pixel-level annotations, and consists of high spatial resolution SAR images collected from the GaoFen-3 (GF-3) satellite. For comprehensive analysis, extensive experiments are conducted on the DaliWS dataset to explore the performance of the state-of-the-art segmentation models, including FCN, SegNeXt, U-Net, and DeeplabV3+, and investigate the impact of different polarization modes on water segmentation. Additionally, to probe the generalization of our dataset, we further evaluate the models trained with the DaliWS dataset, on publicly available water segmentation datasets. Through detailed analysis of the experimental results, we establish a valuable benchmark and provide usage guidelines for future researchers working with the DaliWS dataset. The experimental results demonstrate the F1 scores of FCN, SegNeXt, U-Net, and DeeplabV3+ on the dual-polarization data of DaliWS dataset reach to 90.361%, 90.192%, 92.110%, and 91.199%, respectively, and these four models trained using the DaliWS dataset exhibit excellent generalization performance on the public dataset, which further confirms the research value of our dataset. Full article

(This article belongs to the Special Issue Artificial Intelligence-Driven Methods for Remote Sensing Target and Object Detection II)

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22 pages, 4810 KiB

Open AccessArticle

Ship Target Detection in Optical Remote Sensing Images Based on E2YOLOX-VFL

by Qichang Zhao, Yiquan Wu and Yubin Yuan

Remote Sens. 2024, 16(2), 340; https://doi.org/10.3390/rs16020340 - 15 Jan 2024

Cited by 4 | Viewed by 1761

Abstract

In this research, E2YOLOX-VFL is proposed as a novel approach to address the challenges of optical image multi-scale ship detection and recognition in complex maritime and land backgrounds. Firstly, the typical anchor-free network YOLOX is utilized as the baseline network for ship detection. Secondly, the Efficient Channel Attention module is incorporated into the YOLOX Backbone network to enhance the model’s capability to extract information from objects of different scales, such as large, medium, and small, thus improving ship detection performance in complex backgrounds. Thirdly, we propose the Efficient Force-IoU (EFIoU) Loss function as a replacement for the Intersection over Union (IoU) Loss, addressing the issue whereby IoU Loss only considers the intersection and union between the ground truth boxes and the predicted boxes, without taking into account the size and position of targets. This also considers the disadvantageous effects of low-quality samples, resulting in inaccuracies in measuring target similarity, and improves the regression performance of the algorithm. Fourthly, the confidence loss function is improved. Specifically, Varifocal Loss is employed instead of CE Loss, effectively handling the positive and negative sample imbalance, challenging samples, and class imbalance, enhancing the overall detection performance of the model. Then, we propose Balanced Gaussian NMS (BG-NMS) to solve the problem of missed detection caused by the occlusion of dense targets. Finally, the E2YOLOX-VFL algorithm is tested on the HRSC2016 dataset, achieving a 9.28% improvement in mAP compared to the baseline YOLOX algorithm. Moreover, the detection performance using BG-NMS is also analyzed, and the experimental results validate the effectiveness of the E2YOLOX-VFL algorithm. Full article

(This article belongs to the Special Issue Artificial Intelligence-Driven Methods for Remote Sensing Target and Object Detection II)

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22 pages, 1779 KiB

Open AccessArticle

Sensing and Deep CNN-Assisted Semi-Blind Detection for Multi-User Massive MIMO Communications

by Fengxia Han, Jin Zeng, Le Zheng, Hongming Zhang and Jianhui Wang

Remote Sens. 2024, 16(2), 247; https://doi.org/10.3390/rs16020247 - 8 Jan 2024

Cited by 1 | Viewed by 1375

Abstract

Attaining precise target detection and channel measurements are critical for guiding beamforming optimization and data demodulation in massive multiple-input multiple-output (MIMO) communication systems with hybrid structures, which requires large pilot overhead as well as substantial computational complexity. With benefits from the powerful detection characteristics of MIMO radar, we aim for designing a novel sensing-assisted semi-blind detection scheme in this paper, where both the inherent low-rankness of signal matrix and the essential knowledge about geometric environments are fully exploited under a designated cooperative manner. Specifically, to efficiently recover the channel factorizations via the formulated low-rank matrix completion problem, a low-complexity iterative algorithm stemming from the alternating steepest descent (ASD) method is adopted to obtain the solutions in case of unknown noise statistics. Moreover, we take one step forward by employing the denoising convolutional neural network (DnCNN) to preprocess the received signals due to its favorable performance of handling Gaussian denoising. The overall paradigm of our proposed scheme consists of three stages, namely (1) target parameter sensing, (2) communication signal denoising and (3) semi-blind detection refinement. Simulation results show that significant estimation gains can be achieved by the proposed scheme with reduced training overhead in a variety of system settings. Full article

(This article belongs to the Special Issue Artificial Intelligence-Driven Methods for Remote Sensing Target and Object Detection II)

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18 pages, 3000 KiB

Open AccessArticle

A Lightweight Man-Overboard Detection and Tracking Model Using Aerial Images for Maritime Search and Rescue

by Yijian Zhang, Qianyi Tao and Yong Yin

Remote Sens. 2024, 16(1), 165; https://doi.org/10.3390/rs16010165 - 30 Dec 2023

Cited by 6 | Viewed by 2373

Abstract

Unmanned rescue systems have become an efficient means of executing maritime search and rescue operations, ensuring the safety of rescue personnel. Unmanned aerial vehicles (UAVs), due to their agility and portability, are well-suited for these missions. In this context, we introduce a lightweight detection model, YOLOv7-FSB, and its integration with ByteTrack for real-time detection and tracking of individuals in maritime distress situations. YOLOv7-FSB is our lightweight detection model, designed to optimize the use of computational resources on UAVs. It comprises several key components: FSNet serves as the backbone network, reducing redundant computations and memory access to enhance the overall efficiency. The SP-ELAN module is introduced to ensure operational speed while improving feature extraction capabilities. We have also enhanced the feature pyramid structure, making it highly effective for locating individuals in distress within aerial images captured by UAVs. By integrating this lightweight model with ByteTrack, we have created a system that improves detection accuracy from 86.9% to 89.2% while maintaining a detection speed similar to YOLOv7-tiny. Additionally, our approach achieves a MOTA of 85.5% and a tracking speed of 82.7 frames per second, meeting the demanding requirements of maritime search and rescue missions. Full article

(This article belongs to the Special Issue Artificial Intelligence-Driven Methods for Remote Sensing Target and Object Detection II)

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19 pages, 29513 KiB

Open AccessArticle

A Multi-Stream Attention-Aware Convolutional Neural Network: Monitoring of Sand and Dust Storms from Ordinary Urban Surveillance Cameras

by Xing Wang, Zhengwei Yang, Huihui Feng, Jiuwei Zhao, Shuaiyi Shi and Lu Cheng

Remote Sens. 2023, 15(21), 5227; https://doi.org/10.3390/rs15215227 - 3 Nov 2023

Cited by 1 | Viewed by 1101

Abstract

Sand and dust storm (SDS) weather has caused several severe hazards in many regions worldwide, e.g., environmental pollution, traffic disruptions, and human casualties. Widespread surveillance cameras show great potential for high spatiotemporal resolution SDS observation. This study explores the possibility of employing the surveillance camera as an alternative SDS monitor. Based on SDS image feature analysis, a Multi-Stream Attention-aware Convolutional Neural Network (MA-CNN), which learns SDS image features at different scales through a multi-stream structure and employs an attention mechanism to enhance the detection performance, is constructed for an accurate SDS observation task. Moreover, a dataset with 13,216 images was built to train and test the MA-CNN. Eighteen algorithms, including nine well-known deep learning models and their variants built on an attention mechanism, were used for comparison. The experimental results showed that the MA-CNN achieved an accuracy performance of 0.857 on the training dataset, while this value changed to 0.945, 0.919, and 0.953 in three different real-world scenarios, which is the optimal performance among the compared algorithms. Therefore, surveillance camera-based monitors can effectively observe the occurrence of SDS disasters and provide valuable supplements to existing SDS observation networks. Full article

(This article belongs to the Special Issue Artificial Intelligence-Driven Methods for Remote Sensing Target and Object Detection II)

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19 pages, 8534 KiB

Open AccessArticle

An Infrared Maritime Small Target Detection Algorithm Based on Semantic, Detail, and Edge Multidimensional Information Fusion

by Jiping Yao, Shanzhu Xiao, Qiuqun Deng, Gongjian Wen, Huamin Tao and Jinming Du

Remote Sens. 2023, 15(20), 4909; https://doi.org/10.3390/rs15204909 - 11 Oct 2023

Cited by 3 | Viewed by 1540

Abstract

The infrared small target detection technology has a wide range of applications in maritime defense warning and maritime border reconnaissance, especially in the maritime and sky scenes for detecting potential terrorist attacks and monitoring maritime borders. However, due to the weak nature of infrared targets and the presence of background interferences such as wave reflections and islands in maritime scenes, targets are easily submerged in the background, making small infrared targets hard to detect. We propose the multidimensional information fusion network(MIFNet) that can learn more information from limited data and achieve more accurate target segmentation. The multidimensional information fusion module calculates semantic information through the attention mechanism and fuses it with detailed information and edge information, enabling the network to achieve more accurate target position detection and avoid detecting one target as multiple ones, especially in high-precision scenes such as maritime target detection, thus effectively improving the accuracy and reliability of detection. Moreover, experiments on our constructed dataset for small infrared targets in maritime scenes demonstrate that our algorithm has advantages over other state-of-the-art algorithms, with an IoU of 79.09%, nIoU of 79.43%, F1 score of 87.88%, and AuC of 95.96%. Full article

(This article belongs to the Special Issue Artificial Intelligence-Driven Methods for Remote Sensing Target and Object Detection II)

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20 pages, 35416 KiB

Open AccessArticle

Hybrid Task Cascade-Based Building Extraction Method in Remote Sensing Imagery

by Runqin Deng, Meng Zhou, Yinni Huang and Wei Tu

Remote Sens. 2023, 15(20), 4907; https://doi.org/10.3390/rs15204907 - 11 Oct 2023

Cited by 2 | Viewed by 1392

Abstract

Instance segmentation has been widely applied in building extraction from remote sensing imagery in recent years, and accurate instance segmentation results are crucial for urban planning, construction and management. However, existing methods for building instance segmentation (BSI) still have room for improvement. To achieve better detection accuracy and superior performance, we introduce a Hybrid Task Cascade (HTC)-based building extraction method, which is more tailored to the characteristics of buildings. As opposed to a cascaded improvement that performs the bounding box and mask branch refinement separately, HTC intertwines them in a joint multilevel process. The experimental results also validate its effectiveness. Our approach achieves better detection accuracy compared to mainstream instance segmentation methods on three different building datasets, yielding outcomes that are more in line with the distinctive characteristics of buildings. Furthermore, we evaluate the effectiveness of each module of the HTC for building extraction and analyze the impact of the detection threshold on the model’s detection accuracy. Finally, we investigate the generalization ability of the proposed model. Full article

(This article belongs to the Special Issue Artificial Intelligence-Driven Methods for Remote Sensing Target and Object Detection II)

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18 pages, 7073 KiB

Open AccessArticle

Exploiting Remote Sensing Imagery for Vehicle Detection and Classification Using an Artificial Intelligence Technique

by Masoud Alajmi, Hayam Alamro, Fuad Al-Mutiri, Mohammed Aljebreen, Kamal M. Othman and Ahmed Sayed

Remote Sens. 2023, 15(18), 4600; https://doi.org/10.3390/rs15184600 - 19 Sep 2023

Cited by 1 | Viewed by 1522

Abstract

Remote sensing imagery involves capturing and examining details about the Earth’s surface from a distance, often using satellites, drones, or other aerial platforms. It offers useful data with which to monitor and understand different phenomena on Earth. Vehicle detection and classification play a crucial role in various applications, including traffic monitoring, urban planning, and environmental analysis. Deep learning, specifically convolutional neural networks (CNNs), has revolutionized vehicle detection in remote sensing. This study designs an improved Chimp optimization algorithm with a DL-based vehicle detection and classification (ICOA-DLVDC) technique on RSI. The presented ICOA-DLVDC technique involves two phases: object detection and classification. For vehicle detection, the ICOA-DLVDC technique applies the EfficientDet model. Next, the detected objects can be classified by using the sparse autoencoder (SAE) model. To optimize the SAE’s hyperparameters effectively, we introduce an ICOA which streamlines the parameter tuning process, accelerating convergence and enhancing the overall performance of the SAE classifier. An extensive set of experiments has been conducted to highlight the improved vehicle classification outcomes of the ICOA-DLVDC technique. The simulation values demonstrated the remarkable performance of the ICOA-DLVDC approach compared to other recent techniques, with a maximum accuracy of 99.70% and 99.50% on the VEDAI dataset and ISPRS Postdam dataset, respectively. Full article

(This article belongs to the Special Issue Artificial Intelligence-Driven Methods for Remote Sensing Target and Object Detection II)

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