检索结果-内蒙古大学图书馆

Panchromatic and multispectral image fusion for remote sensing and earth observation: Concepts, taxonomy, literature review, evaluation methodologies and challenges ahead

引用

INFORMATION FUSION 2023年 93卷 227-242页

作者： Zhang, Kai Zhang, Feng Wan, Wenbo Yu, Hui Sun, Jiande Del Ser, Javier Elyan, Eyad Hussain, Amir Shandong Normal Univ Sch Informat Sci & Engn Jinan Peoples R China Univ Portsmouth Sch Creat Technol Portsmouth England Basque Res & Technol Alliance BRTA TECNALIA Derio 48160 Spain Univ Basque Country UPV EHU Bilbao 48013 Spain Robert Gordon Univ Sch Comp Aberdeen Scotland Edinburgh Napier Univ Sch Comp Edinburgh Scotland

Panchromatic and multispectral image fusion, termed pan-sharpening, is to merge the spatial and spectral information of the source images into a fused one, which has a higher spatial and spectral resolution and is more reliable for downstream tasks compared with any of the source images. It has been widely applied to image interpretation and pre-processing of various applications. A large number of methods have been proposed to achieve better fusion results by considering the spatial and spectral relationships among panchromatic and multispectral images. In recent years, the fast development of artificial intelligence (AI) and deep learning (DL) has significantly enhanced the development of pan-sharpening techniques. However, this field lacks a comprehensive overview of recent advances boosted by the rise of AI and DL. This paper provides a comprehensive review of a variety of pan-sharpening methods that adopt four different paradigms, i.e., component substitution, multiresolution analysis, degradation model, and deep neural networks. As an important aspect of pan-sharpening, the evaluation of the fused image is also outlined to present various assessment methods in terms of reduced-resolution and full-resolution quality measurement. Then, we conclude this paper by discussing the existing limitations, difficulties, and challenges of pan-sharpening techniques, datasets, and quality assessment. In addition, the survey summarizes the development trends in these areas, which provide useful methodological practices for researchers and professionals. Finally, the developments in pan-sharpening are summarized in the conclusion part. The aim of the survey is to serve as a referential starting point for newcomers and a common point of agreement around the research directions to be followed in this exciting area.

关键词： image fusion Pan-sharpening image quality evaluation Multispectral image Panchromatic image

来源：评论

学校读者我要写书评

暂无评论

neural networks FOR OPTIMAL INITIAL GUESS SELECTION IN NONLINEAR MICROWAVE SUBSURFACE IMAGING

NEURAL NETWORKS FOR OPTIMAL INITIAL GUESS SELECTION IN NONLI...

引用

IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

作者： Ambrosanio, Michele Franceschini, Stefano Autorino, Maria Maddalena Pascazio, Vito Univ Napoli Parthenope Dipartimento Ingn Ctr Direz Napoli I-80143 Naples Italy

ISBN: (数字)9781665427920

ISBN: (纸本)9781665427920

Subsurface non-destructive exploration is of interest for several applications which span from archeology and civil engineering to safety and security. Among the available remote sensing imaging modalities, ground penetrating radar (GPR) seems very attractive for the detection and characterisation of buried targets. This technology exploits electromagnetic signals in the microwave band to perform the exploration in a non-invasive way. Unfortunately, conventional GPR images require an expert user to be interpreted and do not provide quantitative information about the buried objets. In this framework, this paper explores the use of artificial neural networks for quantitative multiple-input-multiple-output tomographic ground penetrating radar to improve the reliability of the quantitative tomographic recovery, speeding up the nonlinear inversion and providing a user-friendly image which is easy to be interpreted even for a non-expert user.

关键词： Ground penetrating radar (GPR) subsurface signal processing MIMO radar inverse scattering neural networks deep learning microwave imaging

来源：评论

学校读者我要写书评

暂无评论

Transfer learning-based radar imaging with deep convolutional neural networks for distributed frequency modulated continuous waveform multiple-input multiple-output radars

引用

IET RADAR SONAR AND NAVIGATION 2021年第10期15卷 1209-1220页

作者： Seo, Jiho Yang, Yunji Hong, Yong-gi Park, Jaehyun Pukyong Natl Univ Dept Elect Engn Div Smart Robot Convergence & Applicat Engn Pusan 48515 South Korea

Deep-learning-based radar imaging is developed with distributed frequency modulated continuous waveform multiple-input multiple-output (FMCW MIMO) radars in which a deep-learning approach based on the convolutional neural network (CNN) is proposed to achieve radar images robust to adverse circumstances. Differently from the existing deep-learning methods applied to radar object recognition, the deramped radar signal is exploited as the input of the proposed deep CNN (DCNN) without any processing related to the spectrogram transform and the subspace decomposition. To effectively train the proposed DCNN, the received signal is reformulated in terms of the reflection gain values in the (azimuth, range) patches in the image region of interest such that the output vector of the DCNN is composed of the reflection gain values in the associated patches. Furthermore, to overcome the limitations on the amount of training data and training time, the transfer learning approach is effectively applied to the distributed FMCW MIMO radar imaging. The proposed radar imaging is assessed with synthetic simulation data. Specifically, by transferring the pretrained DCNN model for a given reference radar to other distributed radars, the distributed radars can save about 52.4 % in training time compared with a DCNN having the same architecture but without transfer learning.

关键词： radar signal processing Computer vision and image processing techniques distributed radars given reference radar transfer learning-based radar learning (artificial intelligence) deramped radar signal radar imaging distributed FMCW MIMO radar imaging deep-learning-based radar imaging Signal processing and detection distributed frequency radar images Optical, image and video signal processing training time radar object recognition reflection gain values existing deep-learning methods Radar equipment, systems and applications image region transfer learning approach output vector object recognition convolutional neural network neural nets frequency modulation DCNN deep CNN convolution MIMO communication continuous waveform multiple-input multiple-output radars CW radar deep convolutional neural networks FM radar radar antennas MIMO radar Other topics in statistics

来源：评论

学校读者我要写书评

暂无评论

Cervical spine fracture detection using deep learning algorithm

Cervical spine fracture detection using deep learning algori...

引用

International Conference on image and Signal processing and their applications (ISPA)

作者： Mohamed Nadjib Meadi Abd El Mouméne Zerari Hadjer Benbrahim Department of Computer Science LESIA Laboratory University of Mohamed Khider Biskra Algeria

ISBN: (数字)9798350309249

ISBN: (纸本)9798350309256

artificial intelligence (AI) has revolutionized various scientific fields, including healthcare. In particular, deep learning (DL) algorithms have shown remarkable potential in medical imaging, enabling accurate disease detection and diagnosis. Among the prevalent injuries, cervical spine fractures require rapid identification for timely treatment. Recent advancements in AI and DL techniques have opened up new possibilities for automated recognition and classification of cervical spine fractures using medical imaging data. In this study, we investigate the capabilities of deep learning algorithms, specifically the convolutional neural network (CNN) architecture, for automated detection and classification of cervical spine fractures. The outcomes of our research have the potential to significantly enhance early detection and treatment of cervical spine fractures, leading to improved patient outcomes.

关键词： Deep learning neural networks Signal processing algorithms Medical services Signal processing Classification algorithms Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Sargassum detection and path estimation using neural networks 7

Sargassum detection and path estimation using neural network...

引用

PROCEEDINGS OF SPIE Optics, Photonics and Digital Technologies for Imaging applications vii

作者： Lopez-Portillo, Jose A. Casasola-Rodriguez, Ivan G. Escalante-Ramirez, Boris Olveres, Jimena Arriaga, Jaime Appendini, Christian Univ Nacl Autonoma Mexico Ingn Mexico City DF Mexico Univ Nacl Autonoma Mexico Ciencia & Ingn Comp Mexico City DF Mexico Univ Nacl Autonoma Mexico Fac Ingn Mexico City DF Mexico Delft Univ Technol Fac Civil Engn & Geosci Delft Netherlands Univ Nacl Autonoma Mexico Inst Ingn Lab Ingn & Proc Costeros Sisal Mexico Univ Nacl Autonoma Mexico Ctr Estudios Comp Avanzada Mexico City DF Mexico

ISBN: (纸本)9781510651531;9781510651524

Sargassum has affected the Mexican Caribbean coasts since 2015 in atypical amounts, causing economic and ecological problems. Removal once it reaches the coast is complex since it is not easily separated from the sand, damaging dune vegetation, heavy transport compacts the sand and further deteriorates the coastline. Therefore, it is important to detect and estimate the sargassum mats path to optimize the collection efforts in the water. There have been some improvements in systems that rely on satellite images to determine areas and possible paths of sargassum, but these methods do not solve the problems near the coastline where the big mats observed in deep sea end up segregating in little mats which often do not show up in the satellite images. Besides, the temporal scales of nearshore sargassum dynamics are characterized by finer temporal resolution. This paper focuses on cameras located near the coast of Puerto Morelos reef lagoon where images are recorded of both beach and near-coastal sea. First, we apply preprocessing techniques based on time that allows us to discriminate the moving sargassum mats from the static sea bottom, then, using classic image processing techniques and neural networks we detect, trace, and estimate the path of the mat towards the place of arrival on the beach. We compared classic algorithms with neural networks. Some of the algorithms we tested are k-means and random forest for segmentation and dense optical flow to follow and estimate the path. This new methodology allows to supervise in real time the demeanor of sargassum close to shore without complex technical support.

关键词： Sargassum detect estimate trace coast cameras

来源：评论

学校读者我要写书评

暂无评论

Live Demonstration: Face Recognition at The Edge Using Fast On-Chip Deep Learning Neuromorphic Chip 5

Live Demonstration: Face Recognition at The Edge Using Fast ...

引用

5th IEEE International Conference on artificial Intelligence Circuits and Systems, AICAS 2023

作者： Zhong, Zhengqing Wang, Tengxiao Wang, Haibing Zhou, Zhihua He, Junxian Tang, Fang Zhou, Xichuan Yu, Shuang-Ming Liu, Liyuan Wu, Nanjian Tian, Min Shi, Cong Chongqing University School of Microelectronics and Communication Engineering Chongqing400044 China Chongqing Xianfeng Company Institute of Electronic Technology Chongqing400044 China Chinese Academy of Sciences Institute of Semiconductors Beijing100083 China

ISBN: (纸本)9798350332674

Spiking neural networks (SNNs) and neuromorphic systems have attracted ever increasing interests recently, due to their high computational and energy efficiencies originated from closely imitating the functional mechanism of cerebral cortex, which adopts sparse spikes for information processing. In this work, we present a low-cost real-time face recognition system for potential edge-side intelligent applications. This system is mainly built upon our prior reported MorphBungee neuromorphic chip, which is capable of fast on-chip deep learning for fully-connected (FC) SNN of up to 4 layers, 1K spiking neurons and 256K synapses, under a low power consumption of about 100 mW. Our face recognition system achieves 20-fps and 30-fps image frame rates for real-life human face learning and inference, respectively, and obtains a high face recognition accuracy of 100% among 6 persons. It demonstrates that our face recognition system with the neuromorphic chip is suitable for resource-limited real-time intelligent edge applications. © 2023 IEEE.

关键词： neural networks

来源：评论

学校读者我要写书评

暂无评论

Survey on Generative Adversarial Behavior in artificial neural Tasks

Iraqi Journal for Computer Science and Mathematics

引用

Iraqi Journal for Computer Science and Mathematics 2022年第2期3卷 83-94页

作者： Qamar, Roheen Bajao, Naomi Suwarno, Iswanto Jokhio, Fareed Ahmed Department of Computer Science Quaid-e-Awam University of Engineering Science and Technology Nawabshah Pakistan Cebu Technological University Philippines Universitas Muhammadiyah Yogyakarta Indonesia

Generative opposing networking is a technique for learning deep representations in the absence of a large amount of annotated training data. This competitive technique employs two networks to generate background signals. Generative adversarial networks (GANs) use learned representations for a variety of applications, including image synthesis, semantic imaging, style transfer, super magnification, and segmentation. images can be utilized in many ways. GANs are a unique class that has recently received considerable interest because of the popularity of deep generative models. GANs implicitly distribute complex and high-resolution images, sounds, and data. However, given inadvertently built network architecture, objective function usage, and optimization algorithm selection, significant difficulties, such as mode collapse, inconsistencies, and instability, develop while training GANs. This study conducts a thorough examination of the developments in GANs design and optimization strategies presented to address GANs’ difficulties. We provide intriguing study possibilities in this rapidly evolving area. GANs are a popular study topic because of their ability to generate synthetic data and the benefits of representations that can be understood regardless of the application. While various reviews for GANs in the image processing arena have been undertaken to date, none have focused on the review of GANs in multi-disciplinary domains. Thus, this study investigates the utilization of GANs in interdisciplinary application fields and their implementation issues by thoroughly searching for research articles connected to GAN. © 2022 Autoctonía. Revista de Ciencias Sociales e Historia. All rights reserved.

关键词： Generative adversarial networks

来源：评论

学校读者我要写书评

暂无评论

Integrated Photonic-Electronic Platform for Real-time Analog Data processing in LiDARs

Integrated Photonic-Electronic Platform for Real-time Analog...

引用

Emerging Topics in artificial Intelligence (ETAI)

作者： Salmani, Mahsa Saha, Sreenil Eshaghi, Armaghan Huawei Technol Canada Markham ON L3R 5A4 Canada

ISBN: (纸本)9781510653931;9781510653924

Light, Detection and Ranging (LiDAR), has been emerging as a powerful tool for applications with accurate and reliable perception requirements, e.g., autonomous driving which needs a combination of long-range and high spatial resolution together with a real-time performance. processing the raw LiDAR data, which is a large-dimensional unstructured 3D point cloud, is computationally costly due to the nature of the algorithms used for processing the point clouds. In particular, the neural networks employed for LiDAR data processing comprise several layers, for each of which multiplications of matrices with large sizes need to be performed. In this case, Graphics processing Units (GPUs) cannot be used as real-time standalone devices for hardware acceleration because they have high execution time due to their dependency on a Central processing Unit (CPU) for data offloading and scheduling the execution of the algorithms used to process point clouds. To address the aforementioned challenges, we propose an efficient co-design of an analog neural network (ANN) and a hybrid CMOS-Photonics platform for LiDAR systems. The proposed architecture exploits the high bandwidth and low latency of optical computation to significantly improve the computational efficiency. In particular, in our proposed architecture, a CMOS control chip integrated with a photonic broadcast-and-weight architecture is interfaced with LiDAR to perform real-time data processing and high-dimensional matrix multiplications. Moreover, by processing the raw LiDAR data in the analog domain, the proposed hybrid electro-optic computing platform minimizes the number of data converters in LiDAR systems.

关键词： LiDAR optical neural network image classification segmentation

来源：评论

学校读者我要写书评

暂无评论

Research on Network Attack Vulnerability Prediction Based on neural Network Model

Research on Network Attack Vulnerability Prediction Based on...

引用

image processing and Computer applications (ICIPCA), IEEE International Conference on

作者： Meirong Lin Yulu Qu Information Engineering College Hainan Vocational University of Science and Technology Haikou China

The identification and prediction of simulated network attacks using artificial neural networks have achieved the prediction of simulated network attacks and transformed them into clear information for security management personnel, striving for defense time and preparation for counterattacks. This article is based on Python and utilizes artificial neural network models and ANN algorithms to preprocess data, adjust model parameters, perform fitting and recognition, present text, and complete feature extraction, data cleaning, and model training of simulated network attack data. The aim is to train ANN artificial neural network models to identify and predict simulated network attack data.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Pyramid Tokens-to-Token Vision Transformer for Thyroid Pathology image Classification 11

Pyramid Tokens-to-Token Vision Transformer for Thyroid Patho...

引用

11th International Conference on image processing Theory, Tools and applications (IPTA)

作者： Yin, Peng Yu, Bo Jiang, Chengwei Chen, Hechang Jilin Univ Sch Artificial Intelligence Changchun Peoples R China Jilin Univ China Japan Union Hosp Dept Pathol Changchun Peoples R China

ISBN: (纸本)9781665469647

Histopathological image contains rich phenotypic information, which is beneficial to classifying tumor subtypes and predicting the development of diseases. The vast size of pathological slides makes it impossible to directly train whole slide images (WSI) on convolutional neural networks (CNNs). Most of the previous weakly supervision works divide highresolution WSIs into small image patches and separately input them into the CNN to classify them as tumors or normal areas. The first difficulty is that although the method based on the CNN framework achieves a high accuracy rate, it increases the model parameters and computational complexity. The second difficulty is balancing the relationship between accuracy and model computation. It makes the model maintain and improve the classification accuracy as much as possible based on the lightweight. In this paper, we propose a new lightweight architecture called Pyramid Tokens-to-Token VIsion Transformer (PyT2T-ViT) with multiple instance learning based on Vision Transformer. We introduce the feature extractor of the model with Token-to-Token ViT (T2TViT) to reduce the model parameters. The performance of the model is improved by combining the image pyramid of multiple receptive fields so that it can take into account the local and global features of the cell structure at a single scale. We applied the method to our collection of 560 thyroid pathology images from the same institution, model parameters and computation were greatly reduced. The classification effect is significantly better than the CNN-based method.

关键词： Whole slide image multiple instance learning thyroid cancer transformer attention image pyramid

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：