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A block-based inter-band predictor using multilayer propagation neural network for hyperspectral image compression
[article]
2019
arXiv
pre-print
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In this paper, a block-based inter-band predictor (BIP) with multilayer propagation neural network model (MLPNN) is presented by a completely new framework. ...
This predictor can combine with diversity entropy coding methods. Hyperspectral (HS) images are composed by a series high similarity spectral bands. ...
A block-based inter-band predictor using multilayer propagation neural network for hyperspectral image compression Rui Dusselaar and Manoranjan Paul, Senior Member, IEEE A Four-pixel values are shown against ...
arXiv:1902.04191v1
fatcat:xbort7aarbepxd23z2p42si77m
2020 Index IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Vol. 13
2020
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
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., +, JSTARS 2020 847-858 A Deep Learning Method With Merged LSTM Neural Networks for SSHA Prediction. ...
Deep Prototypical Networks With Hybrid Residual Attention for Hyperspectral Image Classification. ...
A New Deep-Learning-Based Approach for Earthquake-Triggered Landslide Detection From Single-Temporal RapidEye Satellite Imagery. Yi, Y., +, JSTARS 2020 ...
doi:10.1109/jstars.2021.3050695
fatcat:ycd5qt66xrgqfewcr6ygsqcl2y
2020 Index IEEE Transactions on Image Processing Vol. 29
2020
IEEE Transactions on Image Processing
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., +, TIP 2020 3558-3568 Context-Adaptive Neural Network-Based Prediction for Image Compression. ...
., +, TIP 2020 1843-1855
End-to-End Blind Image Quality Prediction With Cascaded Deep Neural
Network. ...
doi:10.1109/tip.2020.3046056
fatcat:24m6k2elprf2nfmucbjzhvzk3m
Prediction of bruise volume propagation of pear during the storage using soft computing methods
2019
Food Science & Nutrition
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In this study, the potential of artificial neural network (ANN), adaptive neuro-fuzzy inference system (ANFIS), and multiple regression (MR) techniques to predict bruise volume propagation of pears during ...
Multilayer perceptron (MLP) artificial neural network with three layers that includes an input layer (three neurons), two hidden layers (two and nine neurons), and one output layer was used. ...
"DarGazi" variety of Pear fruit is very sensitive to bruising from mechanical impact and compression. Detailed information about estimation models of bruise volume propagation for pear is limited. ...
doi:10.1002/fsn3.1365
pmid:32148797
pmcid:PMC7020290
fatcat:isakbjywxzd6nlstgaqgetq6y4
2019 Index IEEE Transactions on Geoscience and Remote Sensing Vol. 57
2019
IEEE Transactions on Geoscience and Remote Sensing
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., Refo-cusing and Zoom-In Polar Format Algorithm for Curvilinear Spotlight SAR Imaging on Arbitrary Region of Interest; TGRS Oct. 2019 7995-8010 Hu, T., see Kang, Z., TGRS Jan. 2019 181-193 Hu, T., ...
Atrous Convolution Neural Network for Hyperspectral Image Denoising. ...
., +, TGRS Nov. 2019 8333-8349
FaultNet3D: Predicting Fault Probabilities, Strikes, and Dips With a Single
Convolutional Neural Network. ...
doi:10.1109/tgrs.2020.2967201
fatcat:kpfxoidv5bgcfo36zfsnxe4aj4
Table of Contents
2020
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
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-I Chang 2485 Adaptive Residual Convolutional Neural Network for Hyperspectral Image Classification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ...
Zhou 3521 Deep Prototypical Networks With Hybrid Residual Attention for Hyperspectral Image Classification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ...
Qian 5682 Ideal Regularized Discriminative Multiple Kernel Subspace Alignment for Domain Adaptation in Hyperspectral Image Classification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ...
doi:10.1109/jstars.2020.3046663
fatcat:zqzyhnzacjfdjeejvzokfy4qze
2019 Index IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Vol. 12
2019
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
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., +, JSTARS June 2019 1898-1904 HyperPNN: Hyperspectral Pansharpening via Spectrally Predictive Convolutional Neural Networks. ...
., +, JSTARS Oct. 2019 4024-4037 HyperPNN: Hyperspectral Pansharpening via Spectrally Predictive Convolutional Neural Networks. ...
doi:10.1109/jstars.2020.2973794
fatcat:sncrozq3fjg4bgjf4lnkslbz3u
Comprehensive review of hyperspectral image compression algorithms
2020
Optical Engineering: The Journal of SPIE
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Rapid advancement in the development of hyperspectral image analysis techniques has led to specialized hyperspectral missions. ...
This survey focuses on different hyperspectral image compression algorithms that have been classified into two broad categories based on eight internal and six external parameters. ...
In 2019, another method based on neural network was proposed in block-based interband predictor 84 using multilayer propagation neural network (BIP-MLPNN). ...
doi:10.1117/1.oe.59.9.090902
fatcat:7tn2yfduzreanbmufpmu5cyzpu
IEEE Access Special Section Editorial: Advanced Data Mining Methods for Social Computing
2020
IEEE Access
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The article by Wang et al., ''Stock volatility prediction by hybrid neural network,'' proposes a hybrid time-series predictive neural network (HTPNN) that combines the effect of the news. ...
The scalable part is a neural network that can jointly encode, compress, and fuse various types of contexts. ...
doi:10.1109/access.2020.3043060
fatcat:qbqk5f4ojvadlazhk2mc343sra
2020 Index IEEE Transactions on Neural Networks and Learning Systems Vol. 31
2020
IEEE Transactions on Neural Networks and Learning Systems
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The Author Index contains the primary entry for each item, listed under the first author's name. ...
., +, TNNLS April 2020 1145-1154 Multilayer perceptrons Compressing Deep Neural Networks With Sparse Matrix Factorization. ...
., +, TNNLS May 2020 1638-1652 Evolutionary Compression of Deep Neural Networks for Biomedical Image Segmentation. ...
doi:10.1109/tnnls.2020.3045307
fatcat:34qoykdtarewhdscxqj5jvovqy
Data-driven geophysics: from dictionary learning to deep learning
[article]
2020
arXiv
pre-print
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"Data-driven" techniques may overcome these issues with increasingly available geophysical data. ...
We present a coding tutorial and a summary of tips for beginners and interested geophysical readers to rapidly explore deep learning. ...
Acknowledgments The work was supported in part by the National Key Research and Development Program
Data Availability Statement Data were not used, nor created for this research. ...
arXiv:2007.06183v2
fatcat:ow45ejo7izbkpmssedwd74rbym
Deep learning and machine vision for food processing: A survey
2021
Current Research in Food Science
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At the same time, image processing is an important component of machine vision. ...
Image processing can take advantage of machine learning and deep learning models to effectively identify the type and quality of food. ...
Back Propagation (BP) network is a multilayer feed-forward network trained by the error back-propagation algorithm and is one of the most widely used neural network models. ...
doi:10.1016/j.crfs.2021.03.009
pmid:33937871
pmcid:PMC8079277
fatcat:cqzvzbwwjrdulnve6shf7o2agu
Deep Learning and Machine Vision for Food Processing: A Survey
[article]
2021
arXiv
pre-print
Preserved Fulltext
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The file type is application/pdf.
At the same time, image processing is an important component of machine vision. ...
Image processing can take advantage of machine learning and deep learning models to effectively identify the type and quality of food. ...
Back Propagation (BP) network is a multi-layer feed-forward network trained by the error back-propagation algorithm and is one of the most widely used neural network models. ...
arXiv:2103.16106v1
fatcat:jr3pw7a6inf2tlpef3fk3p2xma
2020 Index IEEE Transactions on Industrial Informatics Vol. 16
2020
IEEE Transactions on Industrial Informatics
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Forests-Based Model for Ultra-Short-Term Prediction of PV Characteristics; TII Jan. 2020 202-214 Imran, A., see Hussain, B., TII Aug. 2020 4986-4996 Imran, M., see Fu, S., TII Sept. 2020 6013-6022 ...
Cai, H., TII Jan. 2020 587-594 Jiang, L., see Xia, Z., TII Jan. 2020 629-638 Jiang, Q., Yan, S., Yan, X., Yi, H., and Gao, F., Data-Driven Two-Dimensional Deep Correlated Representation Learning for ...
., +, TII June 2020 3974-3986 GPU Acceleration of Clustered DPCM for Lossless Compression of Hyperspectral Images. ...
doi:10.1109/tii.2021.3053362
fatcat:blfvdtsc3fdstnk6qoaazskd3i
Multi-grid cellular genetic algorithm for optimizing variable ordering of ROBDDs
2012
2012 IEEE Congress on Evolutionary Computation
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The approach systematically produces better results than the used basic genetic algorithm and better or similar results with other heuristic methods. ...
This paper presents a cellular genetic algorithm for optimizing the variable order in Reduced Ordered Binary Decision Diagrams. The evolution process is inspired by a basic genetic algorithm. ...
Seiya Takenouchi, Hisashi Aomori, Tsuyoshi Otake, Mamoru Tanaka, Ichiro Matsuda and Susumu Itoh, Lossless Image Coding by Cellular Neural Networks with Backward Error Propagation Learning Monday, IJCNN ...
doi:10.1109/cec.2012.6256590
dblp:conf/cec/RotaruB12
fatcat:4ly3nrktw5habc6lf5err7d5py
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