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检索条件"任意字段=Conference on Image and Signal Processing for Remote Sensing IX"

共 3605 条记录，以下是271-280 订阅

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Oriented Object Detection in remote sensing image Based on YOLOV5 14

Oriented Object Detection in Remote Sensing Image Based on Y...

引用

14th International conference on signal processing Systems, ICSPS 2022

作者： Pan, Dan Wu, Lifang Beijing University of Technology Faculty of Information Technology Beijing China

ISBN: (纸本)9798350336313

In recent years, remote sensing satellites have developed rapidly and accumulated massive high-resolution image data. Using deep learning to solve remote sensing image object detection has become an important research direction. However, due to shooting from a bird's eye view, the object has an oblique Angle. Because of the perspective problem, remote sensing images have boundary problem when the object is near horizontal. When there are a large number of densely tilted object boxes, using horizontal box detection method will cause box occlusion thus missing or mislabeling. Therefore, it is a challenging task to solve remote sensing image object detection. In this paper, we propose the network structure which based on YOLO-V5 and combined with CSL method. Moreover, the loss function is improved to deal with the overlapping problem of tilted object boxes effectively. As revealed by extensive experiments, the average accuracy of this method on DOTA data sets shows a competitive advantage. At the same time, the size of the network is only 16.3m, which is obviously better than the comparison method. © 2022 IEEE.

关键词： Object detection

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Atmospheric turbulence characterization using a neuromorphic camera 9

Atmospheric turbulence characterization using a neuromorphic...

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conference on image sensing Technologies - Materials, Devices, Systems, and Applications ix

作者： Polnau, Ernst Vorontsov, Mikhail A. Univ Dayton Sch Engn Intelligent Opt Lab 300 Coll Pk Dayton OH 45469 USA

ISBN: (纸本)9781510650596;9781510650589

The capabilities of neuromorphic (event) based sensors for atmospheric turbulence characterization and refractive index structure parameter (c(n)(2)) sensing are investigated. The experimental setup used a system that consisted of a telescope with an attached neuromorphic camera that was imaging features of a corner of an installation on the roof of a building in 7 km distance. Synchronously with recording of the event stream from neuromorphic sensor the refractive index structure parameter was measured with a commercial scintillometer along the same propagation path. A processing technique was developed to compare the distribution-width of events generated by the edges of the imaged corner within a given time-span to the measured strength of turbulence from the scintillometer. Spatio-temporal analysis was applied to show the possibility to detect influence of wind flow in the of recorded event stream data.

关键词： Atmospheric Turbulence neuromorphic camera remote sensing and sensors

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Realization of remote sensing image Classification Algorithm Based on SOPC

Realization of Remote Sensing Image Classification Algorithm...

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IET International Radar conference 2023, IRC 2023

作者： Qi, Guijie Qiao, Tingting Xie, Yizhuang Radar Research Lab School of Information and Electronics Beijing Institute of Technology Beijing100081 China Beijing Key Laboratory of Embedded Real-time Information Processing Technology Beijing100081 China

ISBN: (纸本)9781839539954

Recently, Convolutional Neural Networks (CNNs) have made significant progress in the field of remote sensing image scene classification. However, commonly used CNN models are mainly deployed on GPU, which is bulky and power-hungry, making them unsuitable for customized requirements of on-board embedded applications. To meet the needs of on-board embedded applications, this study selects System On a Programmable Chip (SoPC), known for their low power consumption and compact size, as the processing platform for accelerating CNNs. Employing the knowledge distillation method, this paper uses the classical CNN scene classification algorithms to create lightweight models. These optimized models are then deployed on resource-constrained ZCU104 SoPC hardware platform. Experimental results demonstrate that the optimized lightweight models achieve image classification tasks with over 95% accuracy on SoPC hardware platform. Furthermore, due to minimal impact from quantization errors, the accuracy drop is less than 1%. Compared to GPU (GTX1650), the throughput on SoPC is increased by 96.43%. © The Institution of Engineering & Technology 2023.

关键词： Convolutional neural networks

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5th International conference on 3D Imaging Technologies—Multidimensional signal processing and Deep Learning, 3DIT-MSP and DL 2023

5th International Conference on 3D Imaging Technologies—Mul...

引用

5th International conference on 3D Imaging Technologies—Multidimensional signal processing and Deep Learning, 3DIT-MSP and DL 2023

ISBN: (纸本)9789819751808

The proceedings contain 59 papers. The special focus in this conference is on 3D Imaging Technologies—Multidimensional signal processing and Deep Learning. The topics include: Digital Twin Technology Approach Based on the Hierarchical IDP Tensor Decomposition;modeling Technology for Complex Dynamic Operating Environment of Power Grid Based on Digital Twins;design of a Digital Twin Platform Based on Distributed Computing and Resource Optimization Algorithms;deep Learning Network Optimization Combining 3D Imaging and Multidimensional signal processing;time Series Prediction Application of Deep Learning in Multidimensional signal processing;improving Abstractive Summarization with Graph Sequence Model;advancing Semantic Segmentation and Interpretation of 3D images Through Integrated Deep Learning and Natural Language processing Techniques;children’s Toy Product Design Based on Augmented Reality Technology;based on the Neural Network Classification of Human Behavior Research;development of an Independent Adversarial Sample Detection Model, Based on image Features;Visualization and Analysis of CNN Adversarial Training;video Violence Detection Method Based on Multi-Feature and Graph Convolutional Network;Enhancing Realized Volatility Prediction: An Exploration into LightGBM Baseline Models;terminal Anomaly Discovery Technology Based on Service Behavior Deviation;An Efficient CRNN Model with the Multi-scale Feature Fusion for Text Recognition from Chinese Medical Reports;advancing Precision Drug Screening: Integrating Imaging Technology and Artificial Intelligence for Novel Models;a Short Video Retrieval Method Based on Spatio-Temporal Feature Fusion;spatial Data Transformation and Fusion Expression Based on Geometric Algebra;EM2-YOLO: Lightweight remote sensing image Detection;Tower-Type Detection of UAV Aerial image Based on YOLOV5 Network Model.

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Semantic Segmentation Algorithm for remote sensing images Based on Attention Mechanism

Semantic Segmentation Algorithm for Remote Sensing Images Ba...

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2023 IEEE International conference on signal processing, Communications and Computing, ICSPCC 2023

作者： Jiang, Jionghui Feng, Xi'An Huang, Hui School of Marine Science and Technology Northwestern Polytechnical University Xi'an China College of Artificial Intelligence Wenzhou University Wenzhou China

ISBN: (纸本)9798350316728

This paper propose to combine the attention mechanism with the U-Net model to improve the performance and accuracy of semantic segmentation tasks. The attention mechanism can better focus on task-relevant regions and features, thereby enhancing the model's perceptual and expressive capabilities. By introducing attention modules into the U-Net semantic segmentation model, fine adjustments and attention to image features are achieved, thereby improving the effectiveness of semantic segmentation. Experimental results also demonstrate that the algorithm proposed in this paper greatly improves the accuracy of semantic segmentation for remote sensing images. © 2023 IEEE.

关键词： Semantic Segmentation

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Progressive Refinement Learning Based on Feature Cross Perception for Residential Areas Semantic Segmentation 48

Progressive Refinement Learning Based on Feature Cross Perce...

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48th IEEE International conference on Acoustics, Speech and signal processing, ICASSP 2023

作者： Lyu, Xinran Zhang, Libao Beijing Normal University School of Artificial Intelligence Beijing100875 China

ISBN: (纸本)9781728163277

Due to the pixel-level accurate annotation of remote sensing images consumes a lot of labor costs, weak annotation semantic segmentation has become a hotspot in recent years. However, due to the lack of label accuracy, these methods often have insufficient expression ability. In this paper, we proposed a semantic segmentation method for residential areas by progressive refinement learning. This method mainly consists of two parts. In the first part, we constructed a classification network and proposed an initial pixel-level label calculation method based on multi-layer category feature awareness. In the second part, we proposed to construct feature cross perceptron module in the structure of the multi-level codec to achieve image pair semantic co-segmentation. In addition, we used confidence maps to modify the loss function to achieve more accurate results. Comprehensive evaluations with GeoEye-1 dataset and the comparison with 7 methods validate the superiority of the proposed model. © 2023 IEEE.

关键词： feature cross perception progressive refinement learning remote sensing weak annotation

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Improved Prototypical Networks for remote sensing Scene Classification

Improved Prototypical Networks for Remote Sensing Scene Clas...

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2023 International conference on image, signal processing, and Pattern Recognition, ISPP 2023

作者： Zhou, Yujin Zhang, Xiang Li, Jie Wang, Guoqing Xie, Yizhuang Beijing Key Laboratory of Embedded Real-Time Information Processing Technology Beijing Institute of Technology Beijing100081 China The Shanghai Aerospace Electronic Technology Institute Shanghai201109 China

ISBN: (纸本)9781510666351

In practical applications, remote sensing (RS) scene classification faces data shift problems, including novel class and data discrepancy problems. Due to these problems, it is difficult to obtain representative and discriminative features. Therefore, we propose improved prototypical networks (IPN) based on few-shot learning to solve data shift problems in RS scene classification. First, a novel and effective scheme is proposed, which is to introduce Vision Transformer (ViT) pre-trained on a large-scale dataset as the feature extractor of the prototypical networks. Based on the meta-task training framework, IPN can adapt well to RS scene classification tasks and obtain representative features. In addition, a novel loss function called self-distillation-based prototype loss is designed to obtain discriminative features by introducing inter-sample self-distillation and inter-layer self-distillation methods. Extensive experiments are conducted on several public RS scene datasets. Compared with the existing methods, the proposed method achieves an improvement of 4.18%-21.42%. Results demonstrate that the proposed method can effectively solve the data shift problems in RS scene classification. © 2023 SPIE.

关键词： Distillation

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A NOVEL SELF-SUPERVISED CROSS-MODAL image RETRIEVAL METHOD IN remote sensing 29

A NOVEL SELF-SUPERVISED CROSS-MODAL IMAGE RETRIEVAL METHOD I...

引用

IEEE International conference on image processing (ICIP)

作者： Sumbul, Gencer Mueller, Markus Demir, Beguem Tech Univ Berlin Fac Elect Engn & Comp Sci Berlin Germany

ISBN: (数字)9781665496209

ISBN: (纸本)9781665496209

Due to the availability of multi-modal remote sensing (RS) image archives, one of the most important research topics is the development of cross-modal RS image retrieval (CM-RSIR) methods that search semantically similar images across different modalities. Existing CM-RSIR methods require the availability of a high quality and quantity of annotated training images. The collection of a sufficient number of reliable labeled images is time consuming, complex and costly in operational scenarios, and can significantly affect the final accuracy of CM-RSIR. In this paper, we introduce a novel self-supervised CM-RSIR method that aims to: i) model mutual-information between different modalities in a self-supervised manner;ii) retain the distributions of modal-specific feature spaces similar to each other;and iii) define the most similar images within each modality without requiring any annotated training image. To this end, we propose a novel objective including three loss functions that simultaneously: i) maximize mutual information of different modalities for inter-modal similarity preservation;ii) minimize the angular distance of multi-modal image tuples for the elimination of inter-modal discrepancies;and iii) increase cosine similarity of the most similar images within each modality for the characterization of intra-modal similarities. Experimental results show the effectiveness of the proposed method compared to state-of-the-art methods. The code of the proposed method is publicly available at https://***- ***/rsim/SS-CM-RSIR.

关键词： Cross-modal image retrieval deep learning self-supervised learning remote sensing

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DEEP LEARNING OF RADIOMETRICAL AND GEOMETRICAL SAR DISTORSIONS FOR image MODALITY TRANSLATIONS 29

DEEP LEARNING OF RADIOMETRICAL AND GEOMETRICAL SAR DISTORSIO...

引用

IEEE International conference on image processing (ICIP)

作者： Bralet, Antoine Atto, Abdourrahmane M. Chanussot, Jocelyn Trouve, Emmanuel Univ Savoie Mont Blanc LISTIC F-74940 Annecy France Univ Grenoble Alpes CNRS Grenoble INP GIPSA Lab F-38000 Grenoble France

ISBN: (数字)9781665496209

ISBN: (纸本)9781665496209

Multimodal approaches for Earth Observations suffer from both the lack of interpretability of SAR images and the high sensitivity to meteorological conditions of optical images. Translation methods were implemented to solve them for specific tasks and areas. But these implementations lack of generalizability as they do not include samples with challenging characteristics. Firstly, this paper sums up the main problems that a general SAR to optical image translator should overcome. Then, a SAR Distorted image to optical translator Network (SARDINet) alternating knowledgeable channel-wise spatial convolutions and cross-channel convolutions is implemented. It aims at solving a problem of major concern in remote sensing: translating layover disturbed SAR images into disturbance-free optical ones. SARDINet is trained through a classical and an adversarial framework and compared to cGAN and cycleGAN from the literature. Experimental results prove that adversarial approaches are more qualitative but worsen quantitative results.

关键词： remote sensing Deep learning Translator Multimodal SAR-Optical

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Building Extraction from remote sensing images with Conditional Generative Adversarial Networks 7

Building Extraction from Remote Sensing Images with Conditio...

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7th International conference on signal and image processing, ICSIP 2022

作者： Chen, Hongshun Sun, Wenjing Beijing Normal University Zhuhai School of Information Technology Zhuhai China

ISBN: (数字)9781665495639

ISBN: (纸本)9781665495639

Automated building extraction from remote sensing images is one of the most challenging problems. In order to automatically extract buildings from remote sensing images by Conditional Generative Adversarial Networks(CGAN), the pix2pix model was adopted and tested on the aerial imagery dataset of WHU Building Dataset. Results show that the pix2pix model performances well and achieves a high precision. © 2022 IEEE.

关键词： Generative adversarial networks

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