UAV Remote Sensing Image Research Articles

Farmland Extraction from UAV Remote Sensing Images Based on Improved SegFormer Model

Farmland Extraction from UAV Remote Sensing Images Based on Improved SegFormer Model

LUFFD-YOLO: A Lightweight Model for UAV Remote Sensing Forest Fire Detection Based on Attention Mechanism and Multi-Level Feature Fusion

The timely and precise detection of forest fires is critical for halting the spread of wildfires and minimizing ecological and economic damage. However, the large variation in target size and the complexity of the background in UAV remote sensing images increase the difficulty of real-time forest fire detection. To address this challenge, this study proposes a lightweight YOLO model for UAV remote sensing forest fire detection (LUFFD-YOLO) based on attention mechanism and multi-level feature fusion techniques: (1) GhostNetV2 was employed to enhance the conventional convolution in YOLOv8n for decreasing the number of parameters in the model; (2) a plug-and-play enhanced small-object forest fire detection C2f (ESDC2f) structure was proposed to enhance the detection capability for small forest fires; (3) an innovative hierarchical feature-integrated C2f (HFIC2f) structure was proposed to improve the model’s ability to extract information from complex backgrounds and the capability of feature fusion. The LUFFD-YOLO model surpasses the YOLOv8n, achieving a 5.1% enhancement in mAP and a 13% reduction in parameter count and obtaining desirable generalization on different datasets, indicating a good balance between high accuracy and model efficiency. This work would provide significant technical support for real-time forest fire detection using UAV remote-sensing images.

Hybrid CNN and Transformer Network for Semantic Segmentation of UAV Remote Sensing Images

Hybrid CNN and Transformer Network for Semantic Segmentation of UAV Remote Sensing Images

Empowering Physical Attacks With Jacobian Matrix Regularization Against ViT-Based Detectors in UAV Remote Sensing Images

Empowering Physical Attacks With Jacobian Matrix Regularization Against ViT-Based Detectors in UAV Remote Sensing Images

Clusterformer for Pine Tree Disease Identification Based on UAV Remote Sensing Image Segmentation

Clusterformer for Pine Tree Disease Identification Based on UAV Remote Sensing Image Segmentation

Efficient Wheat Lodging Detection Using UAV Remote Sensing Images and an Innovative Multi-Branch Classification Framework

Wheat lodging has a significant impact on yields and quality, necessitating the accurate acquisition of lodging information for effective disaster assessment and damage evaluation. This study presents a novel approach for wheat lodging detection in large and heterogeneous fields using UAV remote sensing images. A comprehensive dataset spanning an area of 2.3117 km2 was meticulously collected and labeled, constituting a valuable resource for this study. Through a comprehensive comparison of algorithmic models, remote sensing data types, and model frameworks, this study demonstrates that the Deeplabv3+ model outperforms various other models, including U-net, Bisenetv2, FastSCN, RTFormer, Bisenetv2, and HRNet, achieving a noteworthy F1 score of 90.22% for detecting wheat lodging. Intriguingly, by leveraging RGB image data alone, the current model achieves high-accuracy rates in wheat lodging detection compared to models trained with multispectral datasets at the same resolution. Moreover, we introduce an innovative multi-branch binary classification framework that surpasses the traditional single-branch multi-classification framework. The proposed framework yielded an outstanding F1 score of 90.30% for detecting wheat lodging and an accuracy of 86.94% for area extraction of wheat lodging, surpassing the single-branch multi-classification framework by an improvement of 7.22%. Significantly, the present comprehensive experimental results showcase the capacity of UAVs and deep learning to detect wheat lodging in expansive areas, demonstrating high efficiency and cost-effectiveness under heterogeneous field conditions. This study offers valuable insights for leveraging UAV remote sensing technology to identify post-disaster damage areas and assess the extent of the damage.

Research on Target Recognition of UAV Remote Sensing Image Based on Improved Mask R-CNN Model

Research on Target Recognition of UAV Remote Sensing Image Based on Improved Mask R-CNN Model

Adversarial Patch Attack on Multi-Scale Object Detection for UAV Remote Sensing Images

Although deep learning has received extensive attention and achieved excellent performance in various scenarios, it suffers from adversarial examples to some extent. In particular, physical attack poses a greater threat than digital attack. However, existing research has paid less attention to the physical attack of object detection in UAV remote sensing images (RSIs). In this work, we carefully analyze the universal adversarial patch attack for multi-scale objects in the field of remote sensing. There are two challenges faced by an adversarial attack in RSIs. On one hand, the number of objects in remote sensing images is more than that of natural images. Therefore, it is difficult for an adversarial patch to show an adversarial effect on all objects when attacking a detector of RSIs. On the other hand, the wide height range of the photography platform causes the size of objects to vary a great deal, which presents challenges for the generation of universal adversarial perturbation for multi-scale objects. To this end, we propose an adversarial attack method of object detection for remote sensing data. One of the key ideas of the proposed method is the novel optimization of the adversarial patch. We aim to attack as many objects as possible by formulating a joint optimization problem. Furthermore, we raise the scale factor to generate a universal adversarial patch that adapts to multi-scale objects, which ensures that the adversarial patch is valid for multi-scale objects in the real world. Extensive experiments demonstrate the superiority of our method against state-of-the-art methods on YOLO-v3 and YOLO-v5. In addition, we also validate the effectiveness of our method in real-world applications.

A Novel Method of Small Object Detection in UAV Remote Sensing Images Based on Feature Alignment of Candidate Regions

To solve the problem of low detection accuracy of small objects in UAV optical remote sensing images due to low contrast, dense distribution, and weak features, this paper proposes a small object detection method based on feature alignment of candidate regions is proposed for remote sensing images. Firstly, AFA-FPN (Attention-based Feature Alignment FPN) defines the corresponding relationship between feature mappings, solves the misregistration of features between adjacent levels, and improves the recognition ability of small objects by aligning and fusing shallow spatial features and deep semantic features. Secondly, the PHDA (Polarization Hybrid Domain Attention) module captures local areas containing small object features through parallel channel domain attention and spatial domain attention. It assigns a larger weight to these areas to alleviate the interference of background noise. Then, the rotation branch uses RROI to rotate the horizontal frame obtained by RPN, which avoids missing detection of small objects with dense distribution and arbitrary direction. Next, the rotation branch uses RROI to rotate the horizontal box obtained by RPN. It solves the problem of missing detection of small objects with dense distribution and arbitrary direction and prevents feature mismatch between the object and candidate regions. Finally, the loss function is improved to better reflect the difference between the predicted value and the ground truth. Experiments are conducted on a self-made dataset. The experimental results show that the mAP of the proposed method reaches 82.04% and the detection speed reaches 24.3 FPS, which is significantly higher than that of the state-of-the-art methods. Meanwhile, the ablation experiment verifies the rationality of each module.

Lightweight Hot-Spot Fault Detection Model of Photovoltaic Panels in UAV Remote-Sensing Image.

Photovoltaic panels exposed to harsh environments such as mountains and deserts (e.g., the Gobi desert) for a long time are prone to hot-spot failures, which can affect power generation efficiency and even cause fires. The existing hot-spot fault detection methods of photovoltaic panels cannot adequately complete the real-time detection task; hence, a detection model considering both detection accuracy and speed is proposed. In this paper, the feature extraction part of YOLOv5 is replaced by the more lightweight Focus structure and the basic unit of ShuffleNetv2, and then the original feature fusion method is simplified. Considering that there is no publicly available infrared photovoltaic panel image dataset, this paper generates an infrared photovoltaic image dataset through frame extraction processing and manual annotation of a publicly available video. Consequently, the number of parameters of the model was 3.71 M, mAP was 98.1%, and detection speed was 49 f/s. A comprehensive comparison of the accuracy, detection speed, and model parameters of each model showed that the indicators of the new model are superior to other detection models; thus, the new model is more suitable to be deployed on the UAV platform for real-time photovoltaic panel hot-spot fault detection.

Transmission Line Vibration Damper Detection Using Deep Neural Networks Based on UAV Remote Sensing Image.

Vibration dampers can greatly eliminate the galloping phenomenon of overhead transmission wires caused by wind. The detection of vibration dampers based on visual technology is an important issue. The current vibration damper detection work is mainly carried out manually. In view of the above situation, this article proposes a vibration damper detection model named DamperYOLO based on the one-stage framework in object detection. DamperYOLO first uses a Canny operator to smooth the overexposed points of the input image and extract edge features, then selectees ResNet101 as the backbone of the framework to improve the detection speed, and finally injects edge features into backbone through an attention mechanism. At the same time, an FPN-based feature fusion network is used to provide feature maps of multiple resolutions. In addition, we built a vibration damper detection dataset named DamperDetSet based on UAV cruise images. Multiple sets of experiments on self-built DamperDetSet dataset prove that our model reaches state-of-the-art level in terms of accuracy and test speed and meets the standard of real-time output of high-accuracy test results.

Classification of Forest Types using UAV Remote Sensing Images Based on Improved Ant Colony Algorithm

Classification of Forest Types using UAV Remote Sensing Images Based on Improved Ant Colony Algorithm

Research on Estimation of Paddy Field Area Index Based on UAV Remote Sensing Images

In order to verify the superiority and effectiveness of extracting rice information based on UAV images. This paper takes the rice plot as the research object, and uses the portable UAV Mavic Pro for aerial photography. Preprocess the acquired UAV images to generate orthophotos with a resolution of 3.95cm/pix. Using object-oriented thinking, visual evaluation and ESP tools are combined to quickly select the optimal segmentation scale to be 300, and support is applied. Vector machine, random forest, and nearest neighbor supervised classification methods have carried out ground object classification and rapid extraction of rice area. The classification results and area accuracy are evaluated by visual classification results. The method with the highest overall accuracy is the nearest neighbor classification method. At this time, the user accuracy of rice classification is 95%, and the area consistency accuracy is 99%. The results show that UAV remote sensing and automatic classification can quickly obtain high resolution images and extract rice planting area in plain rice planting area, make up for the lack of ground survey data when Nongshan is blocked, and provide samples and verification basis for the calculation of large-scale rice planting area, yield and other information.

无人机遥感图像数据可视化系统的设计与实现

无人机遥感图像数据可视化系统的设计与实现

Research on Method of Farmland Obstacle Boundary Extraction in UAV Remote Sensing Images.

Aimed at the problem of obstacle detection in farmland, the research proposed to adopt the method of farmland information acquisition based on unmanned aerial vehicle landmark image, and improved the method of extracting obstacle boundary based on standard correlation coefficient template matching and assessed the influence of different image resolutions on the precision of obstacle extraction. Analyzing the RGB image of farmland acquired by unmanned aerial vehicle remote sensing technology, this research got the following results. Firstly, we applied a method automatically registering coordinates, and the average deviations on the X and Y direction were 4.6 cm and 12.0 cm respectively, while the average deviations manually by ArcGIS were 4.6 cm and 5.7 cm. Secondly, with an improvement on the step of the traditional correlation coefficient template matching, we reduced the time of template matching from 12.2 s to 4.6 s. The average deviation between edge length of obstacles calculated by corner points extracted by the algorithm and that by actual measurement was 4.0 cm. Lastly, by compressing the original image on a different ratio, when the pixel reached 735 × 2174 (the image resolution reached 6 cm), the obstacle boundary was extracted based on correlation coefficient template matching, the average deviations of boundary points I of six obstacles on the X and Y were respectively 0.87 and 0.95 cm, and the whole process of detection took about 3.1 s. To sum up, it can be concluded that the algorithm of automatically registered coordinates and of automatically extracted obstacle boundary, which were designed in this research, can be applied to the establishment of a basic information collection system for navigation in future study. The best image pixel of obstacle boundary detection proposed after integrating the detection precision and detection time can be the theoretical basis for deciding the unmanned aerial vehicle remote sensing image resolution.

UAV Remote Sensing Image Rural Building Detection Based on Convolutional Neural Network

In order to improve the accuracy of intelligent detection of rural buildings, this paper uses massive remote sensing data of UAV to train convolutional neural network and improves CaffeNet model. Experimental results are as the followings: With reference to the traditional CaffeNet, the improved a-CaffeNet has an accuracy rate of 97.30%. The test time and the training time are shortened by 30.4% and 11.84% respectively. According to the experiments, this method is more suitable for remote sensing image of rural buildings detection.

Haze removal method based on a variation function and colour attenuation prior for UAV remote-sensing images

ABSTRACTAiming at the problem of low visibility of images obtained by UAV in hazy weather, this paper proposes an image dehazing algorithm based on variation function and colour attention prior. A large number of experiments have proved that the sky or other bright regions could affect the estimation of atmospheric light and transmittance. In the experiment, our proposed algorithm divides the images into sky and dark regions and uses the pixels of the dark region to solve the atmospheric light value. According to the region where the pixels are located, the transmittances of the pixels in the sky and non-sky regions are separately estimated and adjusted. The experiment’s results show that the restored image visibility, information entropy and colour saturation are significantly improved, and the algorithm’s computational efficiency is high.

Research on Boundary Recognition and Extraction Method of Field Operation Area based on UAV Remote Sensing Images

Precision agriculture is a mode of modern agricultural production and management on the basis of information technology, and it is an important way to achieve low consumption, high efficiency, fine quality and safety in agriculture. Moreover, accurate recognition and extraction of field operation area (FOA) boundary is a crucial basic data to implement precision agriculture. Due to the irregular shape, different planting ways and inconsistent size of FOA, it is difficult to recognize and extract the boundary. Therefore, the boundary of FOA based on UAV remote sensing images was classified in this paper, and a priori rule was proposed according to the boundary characteristics. Combined with the classical algorithm of image processing, a boundary recognition and extraction system was developed by LabVIEW to obtain the effective boundary of FOA. At last, the boundary recognition method and the accuracy and real-time performance of the extraction system were tested, and the result shows the method and system can accurately recognize and extract the actual boundary of FOA, and they are adapted to three different types of boundaries and image resolutions. The system has good real-time performance, and the single-frame image processing time does not exceed 100ms when the image resolution is lower than 1920⁎1280.

Counting Tree Number in Subtropical Forest Districts based on UAV Remote Sensing Images

Counting Tree Number in Subtropical Forest Districts based on UAV Remote Sensing Images

Geometry correction Algorithm for UAV Remote Sensing Image Based on Improved Neural Network

Aiming at the disadvantage of current geometry correction algorithm for UAV remote sensing image, a new algorithm is proposed. Adaptive genetic algorithm (AGA) and RBF neural network are introduced into this algorithm. And combined with the geometry correction principle for UAV remote sensing image, the algorithm and solving steps of AGA-RBF are presented in order to realize geometry correction for UAV remote sensing. The correction accuracy and operational efficiency is improved through optimizing the structure and connection weight of RBF neural network separately with AGA and LMS algorithm. Finally, experiments show that AGA-RBF algorithm has the advantages of high correction accuracy, high running rate and strong generalization ability.