Filters








18 Hits in 6.1 sec

Hyperspectral Image Classification – Traditional to Deep Models: A Survey for Future Prospects [article]

Muhammad Ahmad, Sidrah Shabbir, Swalpa Kumar Roy, Danfeng Hong, Xin Wu, Jing Yao, Adil Mehmood Khan, Manuel Mazzara, Salvatore Distefano, Jocelyn Chanussot
2021 arXiv   pre-print
Hyperspectral Imaging (HSI) has been extensively utilized in many real-life applications because it benefits from the detailed spectral information contained in each pixel.  ...  Moreover, we will consider the fact that DL requires a large number of labeled training examples whereas acquiring such a number for HSIC is challenging in terms of time and cost.  ...  ACKNOWLEDGMENT The authors thanks to Ganesan Narayanasamy who is leading IBM OpenPOWER/POWER enablement and ecosystem worldwide for his support to get the IBM AC922 system's access.  ... 
arXiv:2101.06116v2 fatcat:2duwvojkybgufo4kf6sbc6hdva

Deep Learning Meets Hyperspectral Image Analysis: A Multidisciplinary Review

Alberto Signoroni, Mattia Savardi, Annalisa Baronio, Sergio Benini
2019 Journal of Imaging  
Deep learning approaches certainly offer a great variety of opportunities for solving classical imaging tasks and also for approaching new stimulating problems in the spatial–spectral domain.  ...  Modern hyperspectral imaging systems produce huge datasets potentially conveying a great abundance of information; such a resource, however, poses many challenges in the analysis and interpretation of  ...  A three-dimensional CNN-based approach can be exploited to extract combined features directly from the hyperspectral images to be used in classification, as done in [126] for plant disease identification  ... 
doi:10.3390/jimaging5050052 pmid:34460490 fatcat:ledlmt42bfdtdhe7tvj2dl2rwm

A Review of Plant Phenotypic Image Recognition Technology Based on Deep Learning

Jianbin Xiong, Dezheng Yu, Shuangyin Liu, Lei Shu, Xiaochan Wang, Zhaoke Liu
2021 Electronics  
Plant phenotypic image recognition (PPIR) is an important branch of smart agriculture. In recent years, deep learning has achieved significant breakthroughs in image recognition.  ...  Consequently, PPIR technology that is based on deep learning is becoming increasingly popular.  ...  In addition, data preprocessing steps, such as dimensionality reduction, clustering, and segmentation, can also be the key to a successful decision [29] .  ... 
doi:10.3390/electronics10010081 fatcat:7o7nm5lg25d2tjo26c4w3umz4q

DeepHealth: Review and challenges of artificial intelligence in health informatics [article]

Gloria Hyunjung Kwak, Pan Hui
2020 arXiv   pre-print
This article presents a comprehensive review of research applying artificial intelligence in health informatics, focusing on the last seven years in the fields of medical imaging, electronic health records  ...  The demand for it in health informatics is also increasing, and we can expect to see the potential benefits of its applications in healthcare.  ...  Reference [161] combined 2D U-Net architecture with GRU to perform 3D segmentation, and [158] applied it several times in multiple directions to incorporate bidirectional information from neighbors  ... 
arXiv:1909.00384v2 fatcat:sy7pm2c2uvdd3pal2russn4xri

Feature Extraction for Hyperspectral Imagery: The Evolution from Shallow to Deep [article]

Behnood Rasti, Danfeng Hong, Renlong Hang, Pedram Ghamisi, Xudong Kang, Jocelyn Chanussot, Jon Atli Benediktsson
2020 arXiv   pre-print
) for accurate analysis of hyperspectral images.  ...  This article outlines the advances in feature extraction approaches for hyperspectral imagery by providing a technical overview of the state-of-the-art techniques, providing useful entry points for researchers  ...  Melba Crawford for providing the Indian Pines 2010 Data and the National Center for Airborne Laser Mapping (NCALM), the University of Houston, and the IEEE GRSS Fusion Committee for providing the Houston  ... 
arXiv:2003.02822v2 fatcat:2l37q46y6ndqjooo6pkcqezmzi

Diving Deep into Deep Learning:History, Evolution, Types and Applications

2020 VOLUME-8 ISSUE-10, AUGUST 2019, REGULAR ISSUE  
Due to its practicability, deep learning is finding its applications in various AI solutions such as computer vision, natural language processing, intelligent video analytics, analyzing hyperspectral imagery  ...  Although Machine Learning (ML) has become synonymous for Artificial Intelligence (AI); recently, Deep Learning (DL) is being used in place of machine learning persistently.  ...  ACKNOWLEDGMENT The authors would like to thank REVA University for providing the necessary facility to carry out the research work.  ... 
doi:10.35940/ijitee.a4865.019320 fatcat:orn2asvoxfaxvlc5iv7kec4nm4

Deep Learning Classifiers for Hyperspectral Imaging: A Review

M. E. Paoletti, J. M. Haut, J. Plaza, A. Plaza
2022 Zenodo  
Hyperspectral imaging (HSI) is a hot topic in remote sensing data analysis due to the vast amount of information comprised by this kind of images, which allows for a better characterization and exploitation  ...  In order to solve these limitations, several DL-based architectures have been recently developed, exhibiting great potential in HSI data interpretation.  ...  Acknowledgements This work has been suported by: The authors would like to gratefully thank the Associate Editor and the two Anonymous Reviewers for their outstanding comments and suggestions, which greatly  ... 
doi:10.5281/zenodo.6413841 fatcat:j43c6xvhnneczhn4ne6slwqspe

Table of Contents

2020 IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium  
Erdem Kabadayı, Koç University, Turkey TU2-R5: HYPERSPECTRAL IMAGE CLASSIFICATION II TU2-R5.1: TWO-STEP ENSEMBLE BASED CLASS NOISE CLEANINGMETHOD FOR ............................................830 HYPERSPECTRAL  ...  Emery, University of Colorado, United States TU2-R5.9: ADAPTIVE NEIGHBORHOOD STRATEGY BASED GENERATIVE ADVERSARIAL ............................... 862 NETWORK FOR HYPERSPECTRAL IMAGE CLASSIFICATION Hongbo  ...  FR1-R18: NETWORK BASED CLASSIFIER  ... 
doi:10.1109/igarss39084.2020.9323828 fatcat:6aittajt35gufeaugcmemu5cya

A State-of-the-Art Survey on Deep Learning Theory and Architectures

Md Zahangir Alom, Tarek M. Taha, Chris Yakopcic, Stefan Westberg, Paheding Sidike, Mst Shamima Nasrin, Mahmudul Hasan, Brian C. Van Essen, Abdul A. S. Awwal, Vijayan K. Asari
2019 Electronics  
Experimental results show state-of-the-art performance using deep learning when compared to traditional machine learning approaches in the fields of image processing, computer vision, speech recognition  ...  In recent years, deep learning has garnered tremendous success in a variety of application domains.  ...  Acknowledgments: We would like to thank all authors mentioned in the reference of this paper from whom we have learned a lot and thus made this review paper possible.  ... 
doi:10.3390/electronics8030292 fatcat:2i64q7g6kjbjvfalvzwgiggnyq

Data Analysis Methods for Software Systems

Jolita Bernatavičienė
2021 Vilnius University Proceedings  
DAMSS-2021 is the 12th international conference on data analysis methods for software systems, organized in Druskininkai, Lithuania. The same place and the same time every year.  ...  This means that the topics of the conference are actual for business, too.  ...  The proposed Geometric MDS allows the implementation of parallel computing for the dimensionality reduction process of large-scale data using multithreaded multi-core processors or parallel coprocessors  ... 
doi:10.15388/damss.12.2021 fatcat:iefv6bz3drcrfpcwxoaqmu3gra

A Comprehensive Survey of Machine Learning Applied to Radar Signal Processing [article]

Ping Lang, Xiongjun Fu, Marco Martorella, Jian Dong, Rui Qin, Xianpeng Meng, Min Xie
2020 arXiv   pre-print
The main applications of ML-based RSP are then analysed and structured based on the application field.  ...  Traditional radar signal processing (RSP) methods have shown some limitations when meeting such requirements, particularly in matters of target classification.  ...  After dimensionality reduction with MLP, a bidirectional LSTM learned the multi-aspect features to achieve target recognition. This method achieved accuracy rate of 99.9% on MSATR data.  ... 
arXiv:2009.13702v1 fatcat:m6am73324zdwba736sn3vmph3i

Deep Learning Methods for Solving Linear Inverse Problems: Research Directions and Paradigms [article]

Yanna Bai, Wei Chen, Jie Chen, Weisi Guo
2020 arXiv   pre-print
In this paper, we present a comprehensive survey of the recent progress in the development of deep learning for solving various linear inverse problems.  ...  Nowadays, the rapid development of deep learning provides a fresh perspective for solving the linear inverse problem, which has various well-designed network architectures results in state-of-the-art performance  ...  With the learned dictionary A, the high-dimensional signal performs dimensionality reduction to remove redundant information generated in the sampling process.  ... 
arXiv:2007.13290v2 fatcat:kqoerts77nftbl32fctx3za2me

Deep Learning and Earth Observation to Support the Sustainable Development Goals [article]

Claudio Persello, Jan Dirk Wegner, Ronny Hänsch, Devis Tuia, Pedram Ghamisi, Mila Koeva, Gustau Camps-Valls
2021 arXiv   pre-print
in Earth observation.  ...  This paper reviews current deep learning approaches for Earth observation data, along with their application towards monitoring and achieving the SDGs most impacted by the rapid development of deep learning  ...  Hyperspectral images Hyperspectral images (HSIs) have intensively contributed to SDGs, in particular, SDG 2 [60] , SDG 6 [61] , SDG 14 [62] , and SDG 15 [63] .  ... 
arXiv:2112.11367v1 fatcat:7eve5dr45vcublfqyzzrccuvxa

A survey on deep multimodal learning for computer vision: advances, trends, applications, and datasets

Khaled Bayoudh, Raja Knani, Fayçal Hamdaoui, Abdellatif Mtibaa
2021 The Visual Computer  
Extracting relevant patterns from this kind of data is still a motivating goal for researchers in deep learning.  ...  In particular, we summarize six perspectives from the current literature on deep multimodal learning, namely: multimodal data representation, multimodal fusion (i.e., both traditional and deep learning-based  ...  In [51] , Ding et al. proposed a new late fusion policy using CNNs for multimodal facial feature extraction and SAEs for dimensional reduction.  ... 
doi:10.1007/s00371-021-02166-7 pmid:34131356 pmcid:PMC8192112 fatcat:jojwyc6slnevzk7eaiutlmlgfe

2021 Index IEEE Transactions on Instrumentation and Measurement Vol. 70

2021 IEEE Transactions on Instrumentation and Measurement  
The Author Index contains the primary entry for each item, listed under the first author's name.  ...  Article numbers are based on specified topic areas and corresponding codes associated with the publication.  ...  Bradley, L.J., +, TIM 2021 1504609 Dimensionality reduction Fault Diagnosis Using Improved Discrimination Locality Preserving Projec-tions Integrated With Sparse Autoencoder.  ... 
doi:10.1109/tim.2022.3156705 fatcat:dmqderzenrcopoyipv3v4vh4ry
« Previous Showing results 1 — 15 out of 18 results