[go: up one dir, main page]
More Web Proxy on the site http://driver.im/

Mu et al., 2021 - Google Patents

Hyperspectral image spectral–spatial classification method based on deep adaptive feature fusion

Mu et al., 2021

View HTML
Document ID
12466448058085622866
Author
Mu C
Liu Y
Liu Y
Publication year
Publication venue
Remote Sensing

External Links

Snippet

Convolutional neural networks (CNNs) have been widely used in hyperspectral image (HSI) classification. Many algorithms focus on the deep extraction of a single kind of feature to improve classification. There have been few studies on the deep extraction of two or more …
Continue reading at www.mdpi.com (HTML) (other versions)

Classifications

    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06KRECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/62Methods or arrangements for recognition using electronic means
    • G06K9/6217Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/30Information retrieval; Database structures therefor; File system structures therefor
    • G06F17/30286Information retrieval; Database structures therefor; File system structures therefor in structured data stores
    • G06F17/30587Details of specialised database models
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06QDATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00Systems or methods specially adapted for a specific business sector, e.g. utilities or tourism
    • G06Q50/10Services
    • G06Q50/22Health care, e.g. hospitals; Social work
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06QDATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00Systems or methods specially adapted for a specific business sector, e.g. utilities or tourism
    • G06Q50/01Social networking
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06KRECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/36Image preprocessing, i.e. processing the image information without deciding about the identity of the image
    • G06K9/46Extraction of features or characteristics of the image
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/20Handling natural language data
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F19/00Digital computing or data processing equipment or methods, specially adapted for specific applications
    • G06F19/10Bioinformatics, i.e. methods or systems for genetic or protein-related data processing in computational molecular biology
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/10Complex mathematical operations
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F19/00Digital computing or data processing equipment or methods, specially adapted for specific applications
    • G06F19/30Medical informatics, i.e. computer-based analysis or dissemination of patient or disease data
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06QDATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00Administration; Management
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06QDATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00Commerce, e.g. shopping or e-commerce
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N99/00Subject matter not provided for in other groups of this subclass
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL

Similar Documents

Publication Publication Date Title
Zhang et al. HTD-Net: A deep convolutional neural network for target detection in hyperspectral imagery
Liu et al. Bidirectional-convolutional LSTM based spectral-spatial feature learning for hyperspectral image classification
Li et al. Deep belief network for spectral–spatial classification of hyperspectral remote sensor data
Mu et al. A multi-scale and multi-level spectral-spatial feature fusion network for hyperspectral image classification
Liu et al. An investigation of a multidimensional CNN combined with an attention mechanism model to resolve small-sample problems in hyperspectral image classification
Zhai et al. Kernel sparse subspace clustering with a spatial max pooling operation for hyperspectral remote sensing data interpretation
Wu et al. Residual group channel and space attention network for hyperspectral image classification
Wu et al. Three-dimensional ResNeXt network using feature fusion and label smoothing for hyperspectral image classification
Wu et al. Precise crop classification of hyperspectral images using multi-branch feature fusion and dilation-based MLP
Meng et al. Deep residual involution network for hyperspectral image classification
Mu et al. Hyperspectral image spectral–spatial classification method based on deep adaptive feature fusion
Jiang et al. Laplacian regularized spatial-aware collaborative graph for discriminant analysis of hyperspectral imagery
Shi et al. Hierarchical multi-view semi-supervised learning for very high-resolution remote sensing image classification
Ding et al. Integrating hybrid pyramid feature fusion and coordinate attention for effective small sample hyperspectral image classification
Wu et al. A cross-channel dense connection and multi-scale dual aggregated attention network for hyperspectral image classification
Zhou et al. Double additive margin softmax loss for face recognition
Zhang et al. Deep spectral spatial inverted residual network for hyperspectral image classification
Shi et al. Hyperspectral image classification based on a 3D octave convolution and 3D multiscale spatial attention network
Sun et al. Fusing spatial attention with spectral-channel attention mechanism for hyperspectral image classification via encoder–decoder networks
Fang et al. Sparse feature learning of hyperspectral imagery via multiobjective-based extreme learning machine
Li et al. Graph-based deep multitask few-shot learning for hyperspectral image classification
Pei et al. Small sample hyperspectral image classification method based on dual-channel spectral enhancement network
Li et al. Squconvnet: Deep sequencer convolutional network for hyperspectral image classification
Cui et al. MADANet: A lightweight hyperspectral image classification network with multiscale feature aggregation and a dual attention mechanism
Lei et al. Multiscale feature aggregation capsule neural network for hyperspectral remote sensing image classification