Multi-source remotesensingimages have the characteristics of large differences in texture and gray level. Mismatch and low recognition accuracy are easy to occur in the process of identifying targets. Thus, in this ...
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Multi-source remotesensingimages have the characteristics of large differences in texture and gray level. Mismatch and low recognition accuracy are easy to occur in the process of identifying targets. Thus, in this paper, the target recognition algorithm of multi-source remotesensingimage based on IoT vision is investigated. The infrared sensor and SAR radars are set in the visual perception layer of the iVIOT. The visual perception layer transmits the collected remotesensingimage information to the application layer through the wireless networks. The data processing module in the application layer uses the normalized central moment idea to extract the features of multi-source remotesensingimage. Contourlet two-level decomposition is performed on the image after feature extraction to realize multi-scale and multi-directional feature fusion. A two-step method of primary fineness is used to match the fused features and the random sampling consensus algorithm is used to eliminate false matches for obtaining the correct match pairs. After the image feature matching is completed, the BVM target detection operator is used to complete the target recognition of multi-source remotesensingimage. Experimental results show that the use of the IoT to visually recognizing the desired remotesensingimage target has low communication overhead, and the recognition reaches 99% accuracy.
Spatial data mining is an important approach for collecting useful data from big datasets, especially remotely sensed images. This study tackles issues in environmental monitoring and management using sophisticated im...
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Spatial data mining is an important approach for collecting useful data from big datasets, especially remotely sensed images. This study tackles issues in environmental monitoring and management using sophisticated imageprocessing. The Horse Herd Optimization-based VGG19 (HHO-VGG19) is proposed to improve land cover classification, recognition of objects, detection of changes, and detection of anomalies. The study used the BCDD dataset, which was scaled to 512 x 512 pixels, then applied Z-score normalization and extracted features using Principal Component Analysis (PCA). The VGG19 architecture was enhanced by utilizing Horse Herd Optimization to enhance image classification efficiency. The HHO-VGG19 model surpasses conventional techniques, with F1-score of 92%, a recall of 94%, an accuracy of 98.5%, and a 30-second execution time reduction. The findings indicate the efficiency of integrating sophisticated imageprocessing with spatial data mining, giving an effective tool for remotesensingimageprocessing in environmental uses including tracking ecosystems and handling of natural resources.
Event-based sensors (EBS) consist of a pixelated focal plane array in which each pixel is an independent asynchronous change detector. The analog asynchronous array is read by a synchronous digital readout and written...
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ISBN:
(纸本)9781510673991;9781510673984
Event-based sensors (EBS) consist of a pixelated focal plane array in which each pixel is an independent asynchronous change detector. The analog asynchronous array is read by a synchronous digital readout and written to disk. As a result, EBS pixels consume minimal power and bandwidth unless the scene changes. Furthermore, the change detectors have a very large dynamic range (similar to 120 dB) and rapid response time (similar to 20 us). A framing camera with comparable speed requires similar to 3 orders of magnitude more power and similar to 2 orders of magnitude higher bandwidth. These features make EBS an appealing technology for proliferation detection applications. remotesensing deployed in the field requires low power, low bandwidth, and low complexity algorithms. EBS inherently allows for low power and low bandwidth, but a drawback of event-based sensors is the lack of mature image analysis algorithms. While analysis of conventional imagers draws from decades of imageprocessing algorithms, EBS data is a fundamentally different format;a series of x, y, asynchronous time, and polarization change (increase/decrease) as opposed to x, y, and intensity at a regularly sampled framerate. To leverage the advantages of EBS over conventional imagers, our team has worked to develop and refine imageprocessing algorithms that use EBS data directly. We will discuss these efforts, including frequency and phase detection. We will also discuss the field applications of these algorithms such as degraded visual environments (e.g., fog) and defeating laser dazzling attempts.
remote network teaching has gained significant importance in recent times, with video images serving as a crucial medium for delivering educational content. Ensuring accurate face recognition in these video images is ...
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remote network teaching has gained significant importance in recent times, with video images serving as a crucial medium for delivering educational content. Ensuring accurate face recognition in these video images is a key challenge. To address this, we present a face recognition algorithm based on an improved frame difference method. The algorithm focuses on enhancing the accuracy of face recognition specifically in remote network teaching video images. By leveraging a generative adversarial network method, we enhance image resolution as a preprocessing step. Subsequently, our proposed image target detection algorithm effectively identifies the face region through foreground and background segmentation. We employ an improved local three-value pattern for face feature extraction, concentrating on the face target region. These features are then input into an integrated neural network face recognition model. Experimental results demonstrate the algorithm's efficacy in enhancing clarity processing, facial object detection, and feature extraction for remote teaching video images. Notably, the proposed method achieves an average gradient of details below 0.1 and attains a facial feature matching degree of 0.98, establishing the high accuracy of facial recognition results in remote teaching video images.
With the ongoing development of deep learning techniques in recent years, the convolutional neural networks (CNNs) have shown remarkable performance breakthrough in remotesensingimage scene classification. However, ...
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ISBN:
(数字)9783031127007
ISBN:
(纸本)9783031126994;9783031127007
With the ongoing development of deep learning techniques in recent years, the convolutional neural networks (CNNs) have shown remarkable performance breakthrough in remotesensingimage scene classification. However, the performance of these deep models largely depends on the number of available training samples or labeled images. Although the knowledge transferring and pre-training techniques can handle such situation, these may become ineffective due to domain difference. On the other side, the existing data augmentation approaches often produce training samples with too low diversity to help in performance improvement. In order to address these issues, in this work, we propose PReLim as a novel modeling paradigm for remotesensing scene classification under limited labeled samples scenario. PReLim is based on the notion of local and global filtering of scene fragment mixture, which overcomes both the sample diversity and the domain difference issue. Experimental analyses with the benchmark UCMerced and SIRI-WHU datasets demonstrate the effectiveness of PReLim in achieving the state-of-the-art accuracy using limited number of training samples.
To improve the application efficiency of RGB remotesensingimages in agricultural land resource surveys, a cultivated land segmentation algorithm based on kernel space non-uniform regularization classification and im...
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The escalating use of Unmanned Aerial Vehicles (UAVs) as remotesensing platforms has garnered considerable attention, proving invaluable for ground object recognition. While satellite remotesensingimages face limit...
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ISBN:
(纸本)9783031821554;9783031821561
The escalating use of Unmanned Aerial Vehicles (UAVs) as remotesensing platforms has garnered considerable attention, proving invaluable for ground object recognition. While satellite remotesensingimages face limitations in resolution and weather susceptibility, UAV remotesensing, employing low-speed unmanned aircraft, offers enhanced object resolution and agility. The advent of advanced machine learning techniques has propelled significant strides in image analysis, particularly in semantic segmentation for UAV remotesensingimages. This paper evaluates the effectiveness and efficiency of SegFormer, a semantic segmentation framework, for the semantic segmentation of UAV images. SegFormer variants, ranging from real-time (B0) to high-performance (B5) models, are assessed using the UAVid dataset tailored for semantic segmentation tasks. The research details the architecture and training procedures specific to SegFormer in the context of UAV semantic segmentation. Experimental results showcase the model's performance on benchmark dataset, highlighting its ability to accurately delineate objects and land cover features in diverse UAV scenarios, leading to both high efficiency and performance.
Hyperspectral object detection (HOD) aims to identify and locate multiple objects in a scene using hyperspectral images (HSIs). While much research has focused on hyperspectral target detection (HTD) at the pixel leve...
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ISBN:
(纸本)9798400707032
Hyperspectral object detection (HOD) aims to identify and locate multiple objects in a scene using hyperspectral images (HSIs). While much research has focused on hyperspectral target detection (HTD) at the pixel level, HOD remains underexplored. Traditional HTD methods rely heavily on prior spectral information of the target and simple pixel neighborhood relationships, leading to accuracy issues when targets are occluded. Inspired by advances in RGB image detection, we propose a compact and efficient cloud-robust hyperspectral object detection network (CR-HODNet) using 3D convolution to extract spatial and spectral features jointly. We further enhance these features with channel and spatial attention mechanisms and address cloud occlusion challenges using transformer-based multi-head attention. Our method is validated on real airborne hyperspectral images with synthetic cloud occlusion, showing robust performance in challenging scenarios.
Recent advances in unsupervised learning have demonstrated the ability of large vision models to achieve promising results on downstream tasks by pre-training on large amount of unlabelled data. Such pre-training tech...
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ISBN:
(纸本)9798350353006
Recent advances in unsupervised learning have demonstrated the ability of large vision models to achieve promising results on downstream tasks by pre-training on large amount of unlabelled data. Such pre-training techniques have also been explored recently in the remotesensing domain due to the availability of large amount of unlabelled data. Different from standard natural image datasets, remotesensing data is acquired from various sensor technologies and exhibit diverse range of scale variations as well as modalities. Existing satellite image pre-training methods either ignore the scale information present in the remotesensingimagery or restrict themselves to use only a single type of data modality. In this paper, we re-visit transformers pre-training and leverage multi-scale information that is effectively utilized with multiple modalities. Our proposed approach, named SatMAE++, performs multiscale pre-training and utilizes convolution based upsampling blocks to reconstruct the image at higher scales making it extensible to include more scales. Compared to existing works, the proposed SatMAE++ with multi-scale pre-training is equally effective for both optical as well as multi-spectral imagery. Extensive experiments on six datasets reveal the merits of proposed contributions, leading to state-of-the-art performance on all datasets. SatMAE++ achieves mean average precision (mAP) gain of 2.5% for multi-label classification task on BigEarthNet dataset. Our code and pre-trained models are available at https://***/techmn/satmae_pp.
Tailing ponds are used to store tailings or industrial waste discharged after beneficiation. Identifying these ponds in advance can help prevent pollution incidents and reduce their harmful impacts on ecosystems. Tail...
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Tailing ponds are used to store tailings or industrial waste discharged after beneficiation. Identifying these ponds in advance can help prevent pollution incidents and reduce their harmful impacts on ecosystems. Tailing ponds are traditionally identified via manual inspection, which is time-consuming and labor-intensive. Therefore, tailing pond identification based on computer vision is of practical significance for environmental protection and safety. In the context of identifying tailings ponds in remotesensing, a significant challenge arises due to high-resolution images, which capture extensive feature details-such as shape, location, and texture-complicated by the mixing of tailings with other waste materials. This results in substantial intra-class variance and limited inter-class variance, making accurate recognition more difficult. Therefore, to monitor tailing ponds, this study utilized an improved version of DeepLabv3+, which is a widely recognized deep learning model for semantic segmentation. We introduced the multi-scale attention modules, ResNeSt and SENet, into the DeepLabv3+ encoder. The split-attention module in ResNeSt captures multi-scale information when processing multiple sets of feature maps, while the SENet module focuses on channel attention, improving the model's ability to distinguish tailings ponds from other materials in images. Additionally, the tailing pond semantic segmentation dataset NX-TPSet was established based on the Gauge-Fractional-6 image. The ablation experiments show that the recognition accuracy (intersection and integration ratio, IOU) of the RST-DeepLabV3+ model was improved by 1.19% to 93.48% over DeepLabV3+.The multi-attention module enables the model to integrate multi-scale features more effectively, which not only improves segmentation accuracy but also directly contributes to more reliable and efficient monitoring of tailings ponds. The proposed approach achieves top performance on two benchmark datasets, NX-TPSe
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