This project will study a digital surface elevation model (DSM) and an image semantic segmentation method based on multi-scale weight clustering. Taking the overall partitioning metric as a constraint, the weights are...
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It is expected that unmanned aerial vehicle remotesensing target recognition will have a broad range of applications in fields such as smart cities, traffic monitoring, and disaster monitoring. However, remote sensin...
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ISBN:
(数字)9798331528676
ISBN:
(纸本)9798331528683
It is expected that unmanned aerial vehicle remotesensing target recognition will have a broad range of applications in fields such as smart cities, traffic monitoring, and disaster monitoring. However, remotesensingimages have relatively small targets, and they are also affected by factors such as weather, lighting, and occlusion. This makes target detection and recognition difficult. In order to improve the accuracy of target recognition, this paper proposes a dual modal image depth feature fusion method for recognizing unmanned aerial vehicle remote sensor images. The method includes dual modal image registration, and a dual modal image depth feature fusion method based on the yolov7 model. By constructing a bimodal dataset and using the HOPC operator for image registration, strict registration was achieved with manual annotation assistance. The yolov7-dual model proposed in this article, through pixel level fusion and CAFM attention mechanism, ultimately increased the mAP value from 0.424 to 0.657, significantly exceeding expectations.
The optical neural network system based on the 4f system (4f-ONN) is a feasible solution for on -orbit real-time target detection and recognition on remotesensingimages, as it can directly modulate and encode the tw...
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The optical neural network system based on the 4f system (4f-ONN) is a feasible solution for on -orbit real-time target detection and recognition on remotesensingimages, as it can directly modulate and encode the twodimensional image information. However, traditional 4f systems based on spatial light modulators (SLMs) often encounter misalignment errors during assembly due to the reflective optical path used in SLMs. Additionally, implementing electronic SLM in space -based applications introduces problems such as particle number reversal, significantly reducing the reliability of the system. To address these issues, this paper proposes the adoption of diffractive optical elements (DOEs) to construct the 4f-ONN system. The DOE -based design offers a more compact structure, enhanced reliability, and reduced energy consumption, making it highly suitable for on -orbit imageprocessing applications. Due to the expensive and time-consuming nature of the DOE manufacturing process, a design approach for a 4f system based on DOE was pursued through software simulation in this paper. The simulation phase involved the utilization of electronic neural networks to acquire the physical parameters of the DOE mask, while incorporating the array theorem and Fraunhofer diffraction theorem to accurately calculate the physical dimensions of the DOE. The effectiveness of the adopted DOE -based 4f system was initially validated through experiments on a simple pattern dataset. Subsequently, simulation experiments were conducted on three public datasets, namely Mnist, Fashion-mnist, and QuickDraw16, to confirm the efficacy of the DOE -based 4f system in classification tasks. Lastly, target recognition experiments were performed on the GF-2 dataset, and a corresponding hardware system was developed to demonstrate the potential of the DOE -based 4f system in on -orbit imageprocessing.
In this paper, we monitor the surface deformation of Helan Mountains by using the DInSAR (Differential Interferometric Synthetic Aperture Radar) technology and Sentinel-1 SAR data from December 2019 to December 2021. ...
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Under the rapid development of virtual reality, the display of many products has been transformed from the traditional physical display to a combination of physical and virtual displays, thus enabling users to better ...
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remotesensingimages have the characteristics of complex backgrounds, high resolution, and small targets. Although the existing object detection algorithms can improve the detection accuracy, there are generally prob...
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Some environmental factors like haze or fog de-grades the quality of the image. These factors affect some real time processes such as object detection and recognition, automated vehicles and remotesensing which needs...
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The proceedings contain 82 papers. The topics discussed include: detection of Katokkon Chili maturity using convolutional neural network with transfer learning model DenseNet169;performance one stage layer object dete...
ISBN:
(纸本)9798350382914
The proceedings contain 82 papers. The topics discussed include: detection of Katokkon Chili maturity using convolutional neural network with transfer learning model DenseNet169;performance one stage layer object detection model to count people using various backbones;forecasting fuel supply inventory in remote areas using deep learning approaches;deep learning for text based emotion classification from social media;typing patterns for authenticating and identifying users on touchscreen devices using machine learning;android-based English vocabulary learning media using speech recognition and augmented reality;does the height matter? a case for Wi-Fi based wireless positioning system;and fuzzy C-means cluster pattern analysis and ward model mapping in viewing the growth of infectious and non-infectious diseases children in North Aceh.
In recent years, with the rapid development of aerospace technology, the variety of satellite payloads has increased, and the volume of remotesensing data has become increasingly massive. These changes have also put ...
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ISBN:
(数字)9798350350890
ISBN:
(纸本)9798350350906
In recent years, with the rapid development of aerospace technology, the variety of satellite payloads has increased, and the volume of remotesensing data has become increasingly massive. These changes have also put forward higher requirements for the functionality and performance of storage systems. Starting from the perspective of the content validity of image data, this paper proposes a redundant data elimination method based on change detection. By using a twin fully convolutional neural network to implement change detection for remotesensingimages of the same scene at different times, and based on the change detection results, adopting certain strategies to eliminate unchanged areas, the method maximizes the elimination of redundant information in remotesensing data without losing effective information, thereby reducing the volume of stored data. The experimental results have verified the effectiveness of the proposed method.
The proceedings contain 14 papers. The special focus in this conference is on Context-Aware Systems and Applications. The topics include: User-Based Collaborative Filtering Multi-criteria Recommender System Based on I...
ISBN:
(纸本)9783031588778
The proceedings contain 14 papers. The special focus in this conference is on Context-Aware Systems and Applications. The topics include: User-Based Collaborative Filtering Multi-criteria Recommender System Based on Interaction Between Criteria, Criteria Set with Choquet Integral;application of Machine Learning Techniques to Classify Intention to Pay for Forest Ecosystem Services;Anomaly Detection in Univariate Time Series: HOT SAX vesus LSTM-Based Method;application of Machine Learning Models for Predicting Glucose-Level in the Pure Fluid with Algorithm for Reducing Data Dimension Based on Data Series Extraction;comprehensive Survey On remotesensingimageprocessing Techniques for image Classification;item-Based Energy Clustering Recommendation;General Evaluation of EtherCAT-Based Techniques in Various Industrial Systems: Review and Applications;towards an IoT-Based Unmanned Surface Vehicle Design for Environment Monitoring in Mekong Delta;3D CNN with BERT and Vision Transformer for Video recognition;Identify Tumors on Lung CT images;a Context-Aware Application to Monitor the Air Quality;applying Guided Discovery Learning to Enhance the Achievement of Information Technology Team.
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