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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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Road Extraction from U2Net High Resolution remote sensing images with Attention Constraints

Road Extraction from U2Net High Resolution Remote Sensing Im...

引用

signal processing and Intelligent Computing (SPIC), International conference on

作者： Yongqi Zhang Lin Gao School of Information Science and Engineering Shenyang Ligong University Shenyang China

ISBN: (数字)9798350368888

ISBN: (纸本)9798350368895

In order to solve the problem that Convolutional Neural Networks(CNN) have poor modeling ability for global information and may degrade performance when processing target objects at different locations or scales, this paper proposes an attention-constrained U2Net (CBU2Net) high-resolution remote sensing image path extraction method. Based on the original U2Net model, spatial and channel attention mechanisms are introduced. Network model was tested on the Massachusetts road data set, and the accuracy rate, recall rate, Intersection over Union(IoU) and F1-score reached 96.72%, 98.71%, 96.55% and 98.25%, respectively. Compared with the mainstream methods U2Net and U-Net, IoU increased by 0.04% and 0.34%, and F1-score increased by 0.03% and 0.18%, respectively. The experimental outcomes demonstrate that the introduced approach exhibits commendable efficacy, successfully enhancing the precision of road segmentation.

关键词： Attention mechanisms image resolution Roads Redundancy signal processing Feature extraction Robustness Data mining Convolutional neural networks remote sensing

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Prompting DirectSAM for Semantic Contour Extraction in remote sensing images

Prompting DirectSAM for Semantic Contour Extraction in Remot...

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International conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Shiyu Miao Delong Chen Fan Liu Chuanyi Zhang Yanhui Gu Shengjie Guo Jun Zhou College of Computer Science and Software Engineering Hohai University Nanjing China Hong Kong University of Science and Technology Hong Kong SAR School of Computer and Electronic Information Nanjing Normal University Nanjing China School of Information and Communication Technology Griffith University Queensland Australia

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

The Direct Segment Anything Model (DirectSAM) excels in class-agnostic contour extraction. In this paper, we explore its use by applying it to optical remote sensing imagery, where semantic contour extraction—such as identifying buildings, road networks, and coastlines-holds significant practical value. Those applications are currently handled via training specialized small models separately on small datasets in each domain. We introduce a foundation model derived from DirectSAM, termed DirectSAM-RS, which not only inherits the strong segmentation capability acquired from natural images, but also benefits from a large-scale dataset we created for remote sensing semantic contour extraction. This dataset comprises over 34k image-text-contour triplets, making it at least 30 times larger than individual dataset. DirectSAM-RS integrates a prompter module: a text encoder and cross-attention layers attached to the DirectSAM architecture, which allows flexible conditioning on target class labels or referring expressions. We evaluate the DirectSAM-RS in both zero-shot and fine-tuning setting, and demonstrate that it achieves state-of-the-art performance across several downstream benchmarks.

关键词： Training image segmentation Roads Semantics Optical fiber networks Optical imaging Optical sensors Speech processing Optical signal processing remote sensing

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A MODEL COMPRESSION METHOD BASED ON MULTI-TEACHER DISTILLATION FOR SAR SCENE CLASSIFICATION

A MODEL COMPRESSION METHOD BASED ON MULTI-TEACHER DISTILLATI...

引用

IET International Radar conference 2023, IRC 2023

作者： Li, Qihai Zhang, Jiawei Zhou, Tian Qi, Baogui Chen, He Chen, Liang Beijing Key Laboratory of Embedded Real-time Information Processing Technology Beijing Institute of Technology Beijing100081 China Yangtze Delta Region Academy of Beijing Institute of Technology Zhejiang Jiaxing314019 China Shanghai Institute of Satellite Engineering Shanghai200240 China

ISBN: (纸本)9781839539954

In recent years, with the advancement of deep learning, convolutional neural networks tailored for processing image content have gained increasing attention within the remote sensing imagery domain. However, deep convolutional neural networks that exhibit exceptional performance often encompass a substantial number of parameters, thereby necessitating heightened computational resources and time for network training and prediction. Given the constraints posed by resource limitations in the realm of remote sensing, deploying such high-performance networks onto these devices becomes a challenging endeavor. To address this set of challenges, this paper introduces a methodology grounded in target differential probability for multi-teacher knowledge distillation. This approach effectively reduces the model's complexity while preserving the network models' commendable performance. By conducting a statistical analysis of the model's output probability distribution and computing the differential probabilities between teacher and student networks, we regulate the output proportions of teachers within the multi-teacher model. As a result, a high-performing student network is cultivated. In aggregate, experimental validation on two distinct datasets substantiates the efficacy of this approach in facilitating efficient knowledge transfer, consequently yielding heightened performance outcomes. © The Institution of Engineering & Technology 2023.

关键词： Convolutional neural networks

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Enhancing Lung Cancer Detection using VGG-16: Leveraging Deep Convolutional Neural Networks for Accurate Medical image Analysis 2

Enhancing Lung Cancer Detection using VGG-16: Leveraging Dee...

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

作者： Rajaram, Renugadevi Midhunchakkaravarthy Selvam, Janani Vajravelu, Ashok Information Technology Lincoln University College Selangor Darul Ehsan Malaysia Lincoln University College Selangor Darul Faculty of Engineering Ehsan Malaysia Universiti Tun Hussein Onn Malaysia Faculty of Electrical and Electronic Engineering Johor Malaysia

ISBN: (纸本)9798350366570

Lung cancer is one of the deadliest diseases a person can face. The course of medical treatment depends largely on the cancer's type and location. Early detection of cancer cells could save countless lives. To achieve this, an automated system is needed to identify malignant conditions as quickly as possible. Although many techniques have been proposed by researchers over the years, prediction accuracy remains a significant challenge. This paper introduces a method based on artificial neural networks to detect abnormal lung tissue growth. To enhance accuracy, a tool with a higher probability of detection is utilized. Medical professionals diagnose patients using histological images of biopsied tissue from potentially infected lungs. However, diagnosing various types of lung cancer is time-consuming and prone to error. Convolutional Neural Networks (CNNs) have improved the speed and accuracy of identifying and classifying different types of lung cancer, leading to better patient outcomes by helping identify the most effective treatment options. For this study, we used the 'Histopathological images' dataset from Kaggle, which includes lung adenocarcinomas, lung squamous cell carcinomas, and normal lung tissues. The key stages of this approach involve model selection, evaluation, pre-processing, and data collection. Metrics such as accuracy, precision, recall, and Fl-score were used to assess the proposed method. The CNN model achieved accuracy rates of 97.5% for training and 98.5% for validation. © 2024 IEEE.

关键词： Lung cancer

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TriFormer: Triple Branch Heterogeneous Transformers for UHD image Restoration

TriFormer: Triple Branch Heterogeneous Transformers for UHD ...

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International conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Xinchen Ma Yue Lu School of Communication and Electronic Engineering East China Normal University Shanghai China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Ultra-high-definition (UHD) image restoration is becoming a critical research area due to the increasing demand for high-quality visual content in various applications, including autonomous driving, remote sensing, digital entertainment, etc. However, UHD image restoration tasks present notable challenges, including the significant computational burden posed by the large image size scale, the difficulty in preserving fine details and textures, and the increased susceptibility to noise and artifacts during the restoration process. In this paper, we propose a novel triple-branched optimized heterogeneous transformer for UHD image restoration, named TriFormer. Specifically, our method embeds a high-resolution CNN branch for high-frequency features, a low-resolution pixel-wise attention branch for low-frequency features, and a channel-wise attention branch for feature fusion. We perform experiments across two UHD image restoration tasks: enhancing low-light images and deblurring. Results demonstrate that our model outperforms state-of-the-art approaches both quantitatively and qualitatively. The code will be made available at https://***/Chloe-mxxxxc/TRIFORMER.

关键词： Visualization Technological innovation Noise Graphics processing units Entertainment industry Computer architecture Transformers image restoration Speech processing remote sensing

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Evaluating the Effect of Stage Removal on the Performance of Phase Gradient Autofocus (PGA) Algorithm

Evaluating the Effect of Stage Removal on the Performance of...

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2023 International Telecommunications conference, ITC-Egypt 2023

作者： Elbohy, Ahmed Azouz, Ahmed Mashaly, Ahmed S. Helmy, Ashraf Khaled Military Technical College Electrical Engineering Branch Cairo Egypt National Authoritty for Remote Sensing & Space Sciences Cairo Egypt

ISBN: (纸本)9798350326062

Autofocus is a crucial step for Synthetic Aperture Radar (SAR) image formation. The choice of autofocus strategy is highly dependent on the compromise between SAR processing speediness and image quality demanded for trustworthy target analysis applications. The Phase Gradient Autofocus (PGA) algorithm for SAR phase error correction was released at the end of the 1980s. Since then, it has been evinced as a vigorous technique that can afford outstanding results over an assortment of SAR data not only on scene content but also for the hierarchy of the degrading phase error. Undeniably, PGA is adapted to restore defocused SAR images to their designed quality. The accomplishment of the algorithm is entrenched on four elemental signal processing steps, which comprise circular shifting, windowing, estimation of phase gradient, and lastly iterative correction. The question has been highlighted, which of these four elements is verily decisive to the efficacy of the algorithm. Breaking into the algorithm to dispense with one of these four steps reduces the computational cost and processing time while keeping algorithm performance and not affecting the image quality. The Breaking into the algorithm and the deep understanding of these four steps, the role of each part, and the importance of its implementation according to its arrangement within the algorithm is an attempt to reduce the computational complexity by reducing the steps of PGA which, may allow the creation of a customized algorithm to take the advantages of the outstanding PGA and overcome its slow implementation, This enables us to reduce the computational cost and processing time while keeping algorithm performance and not affecting the image quality using the algorithm in real time and without human intervention. Decomposition will be executed in three phases by masking one of the basic algorithm components in a cascaded manner to assess the effect on image quality, then elect which step may be eliminated while m

关键词： Synthetic aperture radar

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MFENet: Multi-Feature Extraction Net for remote sensing Semantic Segmentation 7

MFENet: Multi-Feature Extraction Net for Remote Sensing Sema...

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7th International conference on Intelligent Computing and signal processing, ICSP 2022

作者： Zhang, Chao Lu, Xingshun Ye, Qihong Wang, Chao Yang, Chuansheng Wang, Quanqing Zhejiang Ocean University School of Information Engineering Zhoushan China Zhejiang Ocean University Key Laboratory of Oceanographic Big Data Mining and Application of Zhejiang Province Zhoushan China

ISBN: (纸本)9781665478571

In this paper, we tackle the remote sensing semantic segmentation task by capturing feature information across multiple scales, all channels, and global locations. Different from previous works that simply use U-net to extract multi-scale features, we further improve U-net and propose a Multi-Feature Extraction Network (MFE-Unet). Specifically, we propose the MFE module, which uses both dilated convolution module and two attention modules. Dilated convolution is used to enhance U-net's ability to represent multi-scale information. The two attention modules refer to the channel attention module and the pixel attention module. Channel attention maps all channels centrally, assigns weights uniformly, and adaptively adjusts the importance of each channel's information. Pixel attention treats features at each location as the same individual, and similar features will be associated together to further improve feature representation. We conducted multiple sets of experiments on the "AI+"remote sensing image dataset. Experiments show that our network is sufficient against several advanced models. © 2022 IEEE.

关键词： Convolution

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Unsupervised Change Detection in Multi-Modal SAR images using CycleGAN 28

Unsupervised Change Detection in Multi-Modal SAR Images usin...

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conference on image and signal processing for remote sensing XXVIII

作者： Bergamasco, Luca Bovolo, Francesca Fdn Bruno Kessler Via Sommarive 18 Trento Italy

ISBN: (纸本)9781510655386;9781510655379

Many Change Detection (CD) methods exploit the bi-temporal multi-modal data derived by multiple sensors to find the changes effectively. State-of-the-Art CD methods define features with a common domain between the multi-modal data by normalizing input images or ad hoc feature extraction/selection methods. Deep Learning (DL) CD methods automatically learn features with a common domain during the training or adapt the features derived by multi-modal data. However, CD methods focusing on multi-sensor multi-frequency SAR data are still poorly investigated. We propose a DL CD method that exploits a Cycle Generative Adversarial Network (CycleGAN) to automatically learn and extract multi-scale feature maps in a domain common to the input multi-frequency multi-sensor SAR data. The feature maps are learned, during unsupervised training, by generators that aim to transform the input data domain into the target one while preserving the semantic information and aligning the feature domain. We process the multi-sensor multi-frequency SAR data with the trained generators to produce bi-temporal multi-scale feature maps that are compared to enhance changes. A standard-deviation-based feature selection is applied to keep only the most informative comparisons and reject the ones with poor change information. The multi-scale comparisons are used for a detail preserving CD. Preliminary experimental results conducted on bi-temporal SAR data acquired by Cosmo-SkyMed and SAOCOM on the urban area of Milan, Italy, in January 2020 and August 2021 provided promising results.

关键词： Unsupervised Change Detection CycleGAN SAR images multi-modal data remote sensing multi-sensor images

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Boundary Detector Network with Squeeze-and-Excitation Network for Semantic Segmentation of remote sensing images 2

Boundary Detector Network with Squeeze-and-Excitation Networ...

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2nd International conference on signal processing, Computer Networks and Communications, SPCNC 2023

作者： Cheng, Haiyan Yang, Ruiqi Di, Guangzhi College of big data and intelligent engineering Southwest Forestry University China

ISBN: (纸本)9798400716430

With the rapid development of deep learning, remote sensing image interpretation using semantic segmentation has been widely used in various fields. Deep convolutional Neural network (DCNN) is an important tool for semantic segmentation. However, increasing the complexity of the network and deepening the neural network not only can improve the accuracy of the network, but also lead to the overfitting problem. Based on this point, this paper used the squeeze-and-excitation (SE) modules to optimize the network. SE modules are superimposed after each layer of the ResNet-101 network. This method not only improves network performance but does not lose small target information too much. In this experiment, RGB and nDSM multi-channels of high resolution remote sensing (RS) images with multi-semantic information are used. Experimental results show that the accuracy of the proposed method in the training set and verification set can reach 98.11% and 98.45%, and the model is in continuous convergence. OA, Precision, Recall, F1 and MIOU can reach 85.73%, 84.64%, 85.76%, 85.67%, and 75.47% respectively. © 2023 ACM.

关键词： Semantic Segmentation

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Research on SAR image and Optical image Fusion Classification Based on Deep Learning

Research on SAR Image and Optical Image Fusion Classificatio...

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signal, Information and Data processing (ICSIDP), IEEE International conference on

作者： H. Wu C. H. Li G. L. Tong School of Aerospace EngineeringLand Satellite Remote Sensing Application Center MNR Beijing China Satellite Planning Department Land Satellite Remote Sensing Application Center MNR Beijing China Technical Quality Management Department The Fourth Topographic Surveying Team MNR Harbin China

ISBN: (数字)9798331515669

ISBN: (纸本)9798331515676

In order to better integrate and classify multi-source image features, we constructed a multi-source remote sensing data feature extraction and fusion classification framework U-DSDNet based on a combination of dual channel convolutional neural networks and self attention. Build two independent channel networks to extract feature information. Introducing self attention mechanism to fuse multi-source image feature information, improving classification accuracy and stability. Meanwhile, we use Sentinel series satellite data to demonstrate the classification accuracy of U-DSDNet. Through comparative analysis with UNet, DeeplabV3+, and PSPNet, the experimental results show that in the SEN-OPT-SAR dataset, the overall accuracy of the U-DSDNet model is 3.8%, 0.2%, and 1.6% higher than the three models, respectively, verifying the superiority and effectiveness of the U-DSDNet model in land classification tasks.

关键词： Analytical models Accuracy Attention mechanisms Satellites Semantic segmentation Feature extraction Data models Stability analysis Radar polarimetry remote sensing

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