[go: up one dir, main page]

NanLan et al., 2022 - Google Patents

Hyperspectral data classification algorithm considering spatial texture features

NanLan et al., 2022

View PDF @Full View
Document ID
11976524828886755355
Author
NanLan W
Xiaoyong Z
Publication year
Publication venue
Mobile Information Systems

External Links

Snippet

As a cutting‐edge technology, hyperspectral remote sensing has been widely applied in many fields, including agricultural production, mineral identification, target detection, disaster warning, military reconnaissance, and urban planning. The collected hyperspectral …
Continue reading at onlinelibrary.wiley.com (PDF) (other versions)

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
    • G06K9/6217—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
    • G06K9/62—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
    • G06K9/00624—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
    • G06K9/00657—Recognising patterns in remote scenes, e.g. aerial images, vegetation versus urban areas of vegetation
    • 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
    • G06K9/4652—Extraction of features or characteristics of the image related to colour
    • 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
    • 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
    • G06K9/00771—Recognising scenes under surveillance, e.g. with Markovian modelling of scene activity
    • 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
    • G06K9/20—Image acquisition
    • 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
    • G06F17/30244—Information retrieval; Database structures therefor; File system structures therefor in image databases
    • 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
    • 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
    • G06T2207/10032—Satellite or aerial image; Remote sensing
    • 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
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • 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
    • 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

Similar Documents

Publication Publication Date Title
Zhang et al. Hyperspectral and LiDAR data classification based on structural optimization transmission
Uddin et al. Information-theoretic feature selection with segmentation-based folded principal component analysis (PCA) for hyperspectral image classification
Kaul et al. Support vector machine versus convolutional neural network for hyperspectral image classification: A systematic review
Gómez-Chova et al. Multimodal classification of remote sensing images: A review and future directions
Zhang et al. Hyperspectral remote sensing image subpixel target detection based on supervised metric learning
Han et al. Combining 3D‐CNN and Squeeze‐and‐Excitation Networks for Remote Sensing Sea Ice Image Classification
Liu et al. Multiscale Dense Cross‐Attention Mechanism with Covariance Pooling for Hyperspectral Image Scene Classification
Yu et al. NestNet: A multiscale convolutional neural network for remote sensing image change detection
Tehrani et al. Multi-regional landslide detection using combined unsupervised and supervised machine learning
Klaric et al. GeoCDX: An automated change detection and exploitation system for high-resolution satellite imagery
Diakite et al. Hyperspectral image classification using 3D 2D CNN
Jia et al. Spatial-spectral-combined sparse representation-based classification for hyperspectral imagery
Ouchra et al. Machine learning for satellite image classification: A comprehensive review
Imani et al. Morphology‐based structure‐preserving projection for spectral–spatial feature extraction and classification of hyperspectral data
Paul et al. Canopy averaged chlorophyll content prediction of pear trees using convolutional autoencoder on hyperspectral data
Xia et al. Land resource use classification using deep learning in ecological remote sensing images
Yaman et al. Image processing and machine learning‐based classification method for hyperspectral images
Khesali et al. Semi automatic road extraction by fusion of high resolution optical and radar images
Rajamani et al. Automatic building footprint extraction and road detection from hyperspectral imagery
Mary et al. Deep learning model for the image fusion and accurate classification of remote sensing images
Xie et al. Land use classification method of remote sensing images for urban and rural planning monitoring using deep learning
Zhan et al. DGPF-RENet: A low data dependence network with low training iterations for hyperspectral image classification
Natya et al. Land cover classification schemes using remote sensing images: a recent survey
NanLan et al. Hyperspectral data classification algorithm considering spatial texture features
Wang et al. Lithological information extraction and classification in hyperspectral remote sensing data using Backpropagation Neural Network