Roy et al., 2020 - Google Patents
FuSENet: fused squeeze‐and‐excitation network for spectral‐spatial hyperspectral image classificationRoy et al., 2020
View HTML- Document ID
- 2320510199838744097
- Author
- Roy S
- Dubey S
- Chatterjee S
- Baran Chaudhuri B
- Publication year
- Publication venue
- IET Image Processing
External Links
Snippet
Deep learning‐based approaches have become very prominent in recent years due to its outstanding performance as compared to the hand‐extracted feature‐based methods. Convolutional neural network (CNN) is a type of deep learning architecture to deal with the …
- 230000004927 fusion 0 abstract description 27
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/62—Methods or arrangements for recognition using electronic means
- G06K9/6267—Classification techniques
- G06K9/6268—Classification techniques relating to the classification paradigm, e.g. parametric or non-parametric approaches
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/36—Image preprocessing, i.e. processing the image information without deciding about the identity of the image
- G06K9/46—Extraction of features or characteristics of the image
- G06K9/4604—Detecting partial patterns, e.g. edges or contours, or configurations, e.g. loops, corners, strokes, intersections
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/62—Methods or arrangements for recognition using electronic means
- G06K9/6217—Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
- G06K9/6261—Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation partitioning the feature space
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/36—Image preprocessing, i.e. processing the image information without deciding about the identity of the image
- G06K9/46—Extraction of features or characteristics of the image
- G06K9/4671—Extracting features based on salient regional features, e.g. Scale Invariant Feature Transform [SIFT] keypoints
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/62—Methods or arrangements for recognition using electronic means
- G06K9/6217—Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
- G06K9/6232—Extracting features by transforming the feature space, e.g. multidimensional scaling; Mappings, e.g. subspace methods
- G06K9/6247—Extracting features by transforming the feature space, e.g. multidimensional scaling; Mappings, e.g. subspace methods based on an approximation criterion, e.g. principal component analysis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/36—Image preprocessing, i.e. processing the image information without deciding about the identity of the image
- G06K9/46—Extraction of features or characteristics of the image
- G06K9/52—Extraction of features or characteristics of the image by deriving mathematical or geometrical properties from the whole image
- G06K9/527—Scale-space domain transformation, e.g. with wavelet analysis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/62—Methods or arrangements for recognition using electronic means
- G06K9/6201—Matching; Proximity measures
- G06K9/6202—Comparing pixel values or logical combinations thereof, or feature values having positional relevance, e.g. template matching
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computer systems based on biological models
- G06N3/02—Computer systems based on biological models using neural network models
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/30—Information retrieval; Database structures therefor; File system structures therefor
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/00624—Recognising scenes, i.e. recognition of a whole field of perception; recognising scene-specific objects
- G06K9/0063—Recognising patterns in remote scenes, e.g. aerial images, vegetation versus urban areas
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N99/00—Subject matter not provided for in other groups of this subclass
- G06N99/005—Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T11/00—2D [Two Dimensional] image generation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/10—Image acquisition modality
- G06T2207/10024—Color image
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F3/00—Input arrangements for transferring data to be processed into a form capable of being handled by the computer; Output arrangements for transferring data from processing unit to output unit, e.g. interface arrangements
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K2209/00—Indexing scheme relating to methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Roy et al. | FuSENet: fused squeeze‐and‐excitation network for spectral‐spatial hyperspectral image classification | |
Li et al. | Deep learning-based classification methods for remote sensing images in urban built-up areas | |
Wang et al. | Spectral–spatial multi-feature-based deep learning for hyperspectral remote sensing image classification | |
Jin et al. | Object-oriented method combined with deep convolutional neural networks for land-use-type classification of remote sensing images | |
Liu et al. | Multiscale Dense Cross‐Attention Mechanism with Covariance Pooling for Hyperspectral Image Scene Classification | |
Srivastava et al. | CNN-based salient features in HSI image semantic target prediction | |
Shamsolmoali et al. | Convolutional neural network in network (CNNiN): hyperspectral image classification and dimensionality reduction | |
Ahmad et al. | Graph‐based spatial–spectral feature learning for hyperspectral image classification | |
Qing et al. | Spatial‐spectral classification of hyperspectral images: a deep learning framework with Markov Random fields based modelling | |
Lei et al. | A non-local capsule neural network for hyperspectral remote sensing image classification | |
Imani et al. | Morphology‐based structure‐preserving projection for spectral–spatial feature extraction and classification of hyperspectral data | |
Rajendran et al. | Hyperspectral image classification model using squeeze and excitation network with deep learning | |
Diakite et al. | Hyperspectral image classification using 3D 2D CNN | |
Beirami et al. | Spatial-spectral classification of hyperspectral images based on multiple fractal-based features | |
Hong et al. | Object-oriented multiscale deep features for hyperspectral image classification | |
Sharifi et al. | A new deep learning approach for classification of hyperspectral images: Feature and decision level fusion of spectral and spatial features in multiscale CNN | |
Li et al. | A fuzzy spectral clustering algorithm for hyperspectral image classification | |
Tang et al. | An end‐to‐end steel surface defect detection approach via Swin transformer | |
Jiang et al. | A fully convolutional network with channel and spatial attention for hyperspectral image classification | |
Cao et al. | Semi-supervised feature learning for disjoint hyperspectral imagery classification | |
Cai et al. | Transformer-based contrastive prototypical clustering for multimodal remote sensing data | |
Liu et al. | Scene classification for remote sensing images with self‐attention augmented CNN | |
Zhang et al. | Tree-shaped multiobjective evolutionary CNN for hyperspectral image classification | |
Hamouda et al. | Hyperspectral imaging classification based on convolutional neural networks by adaptive sizes of windows and filters | |
Le et al. | A deep belief network for classifying remotely-sensed hyperspectral data |