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

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HYBRID CONTRASTIVE PROTOTYPICAL NETWORK FOR FEW-SHOT SCENE CLASSIFICATION 30

HYBRID CONTRASTIVE PROTOTYPICAL NETWORK FOR FEW-SHOT SCENE C...

引用

30th IEEE International conference on image processing (ICIP)

作者： Zhu, Junjie Yang, Ke Qiu, Chunping Dai, Mengyuan Guan, Naiyang Yi, Xiaodong Acad Mil Sci Def Innovat Inst Beijing Peoples R China

ISBN: (纸本)9781728198354

Few-shot learning has received widespread attention in remote sensing image scene classification. Many existing methods address this challenge by utilizing meta-learning and metric learning, which focus on developing feature extractors that can quickly adapt to novel few-shot scene classification (FSSC) tasks. However, these methods are often insufficient for real-world datasets with class confusion, where there is high inter-class compactness and intra-class diversity. To overcome this issue, we investigate efficient strategies, i.e., meta-learning-based transferable feature representation and contrastive-based prototypical regularization for learning task-adaptive class boundaries for FSSC. Specifically, we designed a combination of Query-vs-Prototype contrastive loss and Prototype-vs-Prototype contrastive loss to normalize the prototypical representation to be more discriminative in a novel FSSC task. Our proposed model is named the Hybrid Contrastive Prototypical Network (HCP-Net). Experiment results on three popular datasets under two standard benchmarks, i.e., general few-shot classification and few-shot domain generalization, indicate the effectiveness of the proposed method.

关键词： remote sensing Few-shot scene classification Meta-learning Contrastive Learning

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CNN Based Restoration of remote sensing imagery through Denoising Schemes 2

CNN Based Restoration of Remote Sensing Imagery through Deno...

引用

2nd International conference on Recent Trends in Microelectronics, Automation, Computing, and Communications Systems, ICMACC 2024

作者： Narahari, Sujatha Canavoy Krishnan, Sivaneasan Bala Chakrabarti, Prasun Sreenidhi Institute of Science and Technology SIT ECE Department Hyderabad India EEE Department Singapore Baroda University CSE Department Vadodara India

ISBN: (纸本)9798350366570

image restoration through denoising is a technique used to remove noise from noisy images generated by sensor malfunctions, environmental factors, lossy compression algorithms, electrical interference, and so on. To improve the image quality, we present a novel approach using a deep convolutional neural network, which involves of two convolution layers with a ReLU activation function, one convolution layer with no activation function, and a convolution layer with a sigmoid function to limit the mapping value to a range between 0 and 1. To evaluate the customized model architecture, we generated some noisy images using salt and pepper noise and fed them into the neural network as noisy input, with the final output generated after the fourth convolution layer with the sigmoid function. The experimental results are astonishing and outperform most of the deep CNN models over the past decade. © 2024 IEEE.

关键词： Convolutional neural networks

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Road extraction from remote sensing images based on residual dense blocks and mixed attention mechanism 8

Road extraction from remote sensing images based on residual...

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8th International conference on Intelligent Computing and signal processing, ICSP 2023

作者： He, Xiaohui Jiang, Yinjie Zhengzhou University The School of Geo-Science & Technology Zhengzhou China Zhengzhou University School of Computer and Artificial Intelligence Zhengzhou China

ISBN: (纸本)9798350302455

In order to solve the problem of road edge blur in remote sensing image road extraction, a road edge feature information enhancement algorithm based on residual dense blocks and mixed attention mechanism is designed in this paper. At present, semantic segmentation algorithm based on deep learning has been able to extract relatively complete and accurate road network, but the segmentation of road edge is not accurate enough, and the extraction of road edge details is often incomplete. To this end, the ADRU-net road extraction network is designed in this paper, and experimental verification is conducted on data set Zimbabwe-Data-Roads. © 2023 IEEE.

关键词： remote sensing

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Improved Homographic Adaptation for Keypoint Generation in Cross-spectral Registration of Thermal and Optical imagery 29

Improved Homographic Adaptation for Keypoint Generation in C...

引用

conference on image and signal processing for remote sensing XXix

作者： Yagmur, Ismail Can Ates, Hasan F. Ozyegin Univ Dept Comp Sci Istanbul Turkiye Ozyegin Univ Fac Engn Istanbul Turkiye

ISBN: (纸本)9781510666955;9781510666962

Autonomous navigation is an important area of research for aerial vehicles. Visual odometry and simultaneous localization and mapping algorithms are critical for the three-dimensional understanding of the environment. For that purpose, consistent multi-spectral maps of the environment should be generated. Existing pixel-based image registration methods are accurate but too slow to operate in real-time. Recently deep learning is used to develop feature-based data-driven methods for generating interest points and associated descriptors for registering multi-spectral image pairs. These methods are fast and perform better than existing methods for optical images. However, the results are less convincing for thermal image registration. In this work, we propose an improved multi-spectral homographic adaptation technique to generate highly repeatable ground truth interest points that are invariant across viewpoint changes in both spectra. These interest points are used to train the MultiPoint image registration network. Simulation results show that our improved model outperforms existing techniques for feature-based image alignment of optical and thermal images.

关键词： Robot vision Multi-spectral image Registration Homographic Adaptation

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Change Feature Refinement and Cross-Scale Interaction Network for High-Resolution remote sensing image Change Detection 2

Change Feature Refinement and Cross-Scale Interaction Networ...

引用

2nd IEEE International conference on signal, Information and Data processing, ICSIDP 2024

作者： Chen, Yongqi Feng, Shou Tang, Yingjie Su, Nan Zhao, Chunhui Harbin Engineering University College of Information and Comminication Engineering Harbin China

ISBN: (纸本)9798331515669

Change detection represents a fundamental research area within remote sensing technology. Nevertheless, in practical applications, the spatial arrangement of terrestrial features is frequently highly complex. The changing objects vary significantly in size and exhibit irregular, complex shapes. Many mainstream methods are hindered by the intricate spatial scales of these changed objects, culminating in suboptimal detection performance. To overcome these obstacles, we propose a change feature refinement and cross-scale interaction network (FRCI-Net) for effective change detection. To capture more robust change features, FRCI-Net first employs a feature interaction module (FIM) and a differential feature refinement module (DFRM) to accurately capture change regions while enhancing the boundary details of the change features. To further enhance the recognition ability for multi-scale change targets, FRCI-Net leverages a cross-scale interaction module (CIM) to enable attention-driven integration and consolidation of change features across divers scales, thereby improving the network's scale awareness of various change targets. Experiments conducted on two benchmark datasets validate that the proposed method surpasses six advanced methods. © 2024 IEEE.

关键词： Change detection

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Medical remote sensing and Internet of Things for Detection of Epileptic Seizure Using Deep Neural Networks 2

Medical Remote Sensing and Internet of Things for Detection ...

引用

2nd International conference on Distributed Computing and Optimization Techniques, ICDCOT 2024

作者： Vinuja, G. Devi, N. Bharatha Saveetha School of Engineering Simats Department of Cse Thandalam India

ISBN: (纸本)9798350382952

Medical remote sensing and the Internet of Things (IoT) have emerged as powerful tools in the field of disease detection and monitoring. Early detection of infectious diseases is crucial in order to prevent outbreaks and minimize their impact on public health. This paper aims to explore suitable methods and algorithms that can effectively utilize medical remote sensing data and IoT devices to detect and identify infectious diseases at an early stage. In this research, the novel real-time personal health monitoring system WISE (Wearable IoT-cloud-based health monitoring system) is introduced. In the present research, the body area network (BAN) is used to collect the patient's EEG signal. These signals are sent from the patient's location to the cloud via wifi. To turn the EEG input signal into an image, the discrete wavelet transform (DWT) is initially employed. This EEG input image first undergoes data de-noising pre-processing. Wavelet decomposition of EEG data accurately captures and pinpoints transient patterns in both temporal and frequency domains. Classifiers based on multilayer perceptron neural networks (MLPNN) were created and their classification accuracy for EEG signals was tested.. As compared to the existing algorithm the accuracy rate in predicting an abnormal EEG was 98.1 %. The classifier is employed to assess whether a region is typical or anomalous, and it also gauges its effectiveness. © 2024 IEEE.

关键词： Discrete wavelet transforms

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Near Infrared remote sensing of Vegetation Encroachment at Power Transmission Right-of-Way 1

引用

12th International conference on Robotics Vision signal processing and Power Applications

作者： Lim, Pei Yu Bong, David Ting, Kung Chuang Lothai, Florence Francis Joseph, Annie Zulcaffle, Tengku Mohd Afendi Univ Malaysia Sarawak Fac Engn Kota Samarahan 94300 Malaysia i CATS Univ Coll Fac Comp & Software Engn Kuching 93350 Malaysia

ISBN: (数字)9789819990054

ISBN: (纸本)9789819990078;9789819990054;9789819990047

The event of electricity outage could cause huge financial losses for the industry and inconvenience to the consumers. Vegetation encroachment at the power transmission right-of-way is one of the main causes. Transmission line fault could occur when a tree falls into the vicinity of power transmission line. Conventional inspection method such as ground inspection is the simplest approach to counter vegetation encroachment. However, technical personnel is required to travel on site to perform the inspection manually. This process is often time consuming and prone to human error. Airborne Light Detection and Ranging (LiDAR) system and satellite imagery are remote sensing approaches to inspect the power transmission right-of-way. These approaches could reduce reliance on physical site inspection and remove human error. However, large dataset needs to be processed and specialist equipment is needed for this method which also increases the overall cost. In this research, a simple yet cost effective method is used to detect vegetation encroachment by using near aerial infrared (NIR) image processing approach. The process is divided into two parts. First, detect the inconspicuous power transmission line by utilizing Radon Transform (RT) in vertical derivative image and detect the peaks of the Radon Transform. Next, detect the vegetation encroachment in the clearance zone by using green normalized difference vegetation index (GNDVI) algorithm to differentiate between trees and glassy plains. Preliminary experiment results show a satisfactory performance in detecting vegetation encroachment at the power transmission right-of-way.

关键词： Vegetation encroachment Power transmission right-of-way Near infrared (NIR)

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LAND USE CLASSIFICATION VIA MULTI-MODAL COMPLEMENTARY FEATURE FUSION AND CONTEXT INFORMATION ENHANCEMENT FOR OPTICAL AND SAR imageS 31

LAND USE CLASSIFICATION VIA MULTI-MODAL COMPLEMENTARY FEATUR...

引用

2024 International conference on image processing

作者： Fan, Xinyue Zhang, Libao Beijing Normal Univ Sch Artificial Intelligence Beijing 100875 Peoples R China

ISBN: (纸本)9798350349405;9798350349399

Land use classification by fusing optical and synthetic aperture radar (SAR) images has become a research hotspot since it can greatly improve segmentation accuracy. However, due to their different object expression patterns, existing multimodal algorithms have problems such as insufficient utilization of complementary features and inability to solve class imbalance. In this paper, we develop a multi-modal semantic segmentation method based on complementary features fusion and context information enhancement. First, we propose a dual-branch network with no shared weights to extract features of optical images and SAR images respectively. The multi-channel parallel convolution (MCPC) blocks in the network can improve the receptive field of feature extraction. Then, we propose a multi-modal complementary feature fusion (MCFF) module. The two modalities are encouraged to exchange complementary information and suppress redundant information. Finally, for advanced semantic features, we design the context information enhancement (CIE) module to capture multi-scale semantic information and increase feature utilization efficiency to a greater extent. The result of comparative experiments with state-of-the-arts proves the effectiveness of the network.

关键词： remote sensing multi-modal fusion optical images SAR images land use classification

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UV-Mamba: A DCN-Enhanced State Space Model for Urban Village Boundary Identification in High-Resolution remote sensing images

UV-Mamba: A DCN-Enhanced State Space Model for Urban Village...

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2025 IEEE International conference on Acoustics, Speech, and signal processing, ICASSP 2025

作者： Li, Lulin Chen, Ben Zou, Xuechao Xing, Junliang Tao, Pin School of Computer Technology and Applications Qinghai University Xining China School of Computer Science and Technology Beijing Jiaotong University Beijing China Department of Computer Science and Technology Tsinghua University Beijing China

ISBN: (纸本)9798350368741

Due to the diverse geographical environments, intricate landscapes, and high-density settlements, the automatic identification of urban village boundaries using remote sensing images remains a highly challenging task. This paper proposes a novel and efficient neural network model called UV-Mamba for accurate boundary detection in high-resolution remote sensing images. UV-Mamba mitigates the memory loss problem in lengthy sequence modeling, which arises in state space models (SSM) with increasing image size, by incorporating deformable convolutions (DCN). Its architecture utilizes an encoder-decoder framework and includes an encoder with four deformable state space augmentation (DSSA) blocks for efficient multi-level semantic extraction and a decoder to integrate the extracted semantic information. We conducted experiments on two large datasets showing that UV-Mamba achieves state-of-the-art performance. Specifically, our model achieves 73.3% and 78.1% IoU on the Beijing and Xi'an datasets, respectively, representing improvements of 1.2% and 3.4% IoU over the previous best model while also being 6× faster in inference speed and 40× smaller in parameter count. Source code and pre-trained models are available at https://***/Devin-Egber/UV-Mamba. © 2025 IEEE.

关键词： High-Resolution remote sensing images Semantic Segmentation State Space Model (SSM) Urban Village

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remote sensing Small Target Detection Research Based on Improved YOLOv7 2

Remote Sensing Small Target Detection Research Based on Impr...

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2nd International conference on signal processing and Intelligent Computing, SPIC 2024

作者： Guo, Jia Wang, Xiannan Song, Meihua Zhao, Shuang Guo, Cean Zhang, Jian Cheng, Hao Shenyang Ligong University College of Equipment Engineering Shenyang China Northeastern University Key Laboratory of Safe Mining of Deep Metal Mines Ministry of Education Shenyang China

ISBN: (纸本)9798350368888

This study introduces a streamlined improvement algorithm based on YOLOv7 to address the challenges of multiple small targets, complex backgrounds, limited feature points, and environmental influences in remote sensing images. The algorithm incorporates the GSConv module and upsampling to achieve more effective feature fusion. The GSConv module combined with the VoV-GSCSP module achieves Slim-Neck for reducing algorithm complexity and improving accuracy. The Receptive Field Enhancement (RFE) module is introduced after the spatial pyramid pooling layer (SPPCSPC) to enhance the receptive field of small targets. By changing the settings of convolution operations and increasing the receptive field size of each convolutional layer, local and global features in the image are better captured. Information flow is maintained through skip connections. Experimental findings show that the enhanced YOLOv7 algorithm reaches an mAP of 95.5% on the NWPU VHR-10 dataset, marking a 6.4% improvement over the original algorithm's mAP. That improves the accuracy of object detection in remote sensing images. © 2024 IEEE.

关键词： Target tracking

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