[go: up one dir, main page]

Ji et al., 2020 - Google Patents

Seabed sediment classification using multibeam backscatter data based on the selecting optimal random forest model

Ji et al., 2020

Document ID
129580078511472405
Author
Ji X
Yang B
Tang Q
Publication year
Publication venue
Applied Acoustics

External Links

Snippet

Seabed sediment classification using acoustic remote sensing technique is an attractive approach due to its high coverage capabilities and limited costs compared to taking samples of the seafloor. This paper focuses on backscatter intensity correction, sonar image quality …
Continue reading at www.sciencedirect.com (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
    • G06K9/36Image 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
    • 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
    • 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
    • G06K9/6267Classification techniques
    • G06K9/6268Classification techniques relating to the classification paradigm, e.g. parametric or non-parametric approaches
    • 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
    • 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
    • G06K9/20Image acquisition
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00Computer systems based on biological models
    • G06N3/02Computer systems based on biological models using neural network models
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F3/00Input 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
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00Image analysis
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N99/00Subject matter not provided for in other groups of this subclass
    • GPHYSICS
    • G01MEASURING; TESTING
    • G01SRADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S7/00Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
    • G01S7/02Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06KRECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K2209/00Indexing scheme relating to methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • GPHYSICS
    • G01MEASURING; TESTING
    • G01VGEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS
    • G01V1/00Seismology; Seismic or acoustic prospecting or detecting
    • GPHYSICS
    • G01MEASURING; TESTING
    • G01SRADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S15/00Systems using the reflection or reradiation of acoustic waves, e.g. sonar systems

Similar Documents

Publication Publication Date Title
Ji et al. Seabed sediment classification using multibeam backscatter data based on the selecting optimal random forest model
Neupane et al. A review on deep learning-based approaches for automatic sonar target recognition
Niu et al. Deep-learning source localization using multi-frequency magnitude-only data
Che Hasan et al. Integrating multibeam backscatter angular response, mosaic and bathymetry data for benthic habitat mapping
Wilson et al. Multiscale terrain analysis of multibeam bathymetry data for habitat mapping on the continental slope
Ji et al. Acoustic seabed classification based on multibeam echosounder backscatter data using the PSO-BP-AdaBoost algorithm: A case study from Jiaozhou Bay, China
Lucieer et al. Do marine substrates ‘look’and ‘sound’the same? Supervised classification of multibeam acoustic data using autonomous underwater vehicle images
Hożyń A review of underwater mine detection and classification in sonar imagery
Hasan et al. Evaluation of four supervised learning methods for benthic habitat mapping using backscatter from multi-beam sonar
Luo et al. Sediment classification of small-size seabed acoustic images using convolutional neural networks
Misiuk et al. Benthic habitat mapping: A review of three decades of mapping biological patterns on the seafloor
Huang et al. Predictive modelling of seabed sediment parameters using multibeam acoustic data: a case study on the Carnarvon Shelf, Western Australia
Zhu et al. Active learning for recognition of shipwreck target in side-scan sonar image
Yassir et al. Acoustic fish species identification using deep learning and machine learning algorithms: A systematic review
Donini et al. A deep learning architecture for semantic segmentation of radar sounder data
Zhao et al. Automatic detection and segmentation on gas plumes from multibeam water column images
Lawson et al. Decision forests for machine learning classification of large, noisy seafloor feature sets
Minelli et al. Semi-automated data processing and semi-supervised machine learning for the detection and classification of water-column fish schools and gas seeps with a multibeam echosounder
Gournia et al. Automatic detection of trawl-marks in sidescan sonar images through spatial domain filtering, employing haar-like features and morphological operations
Ghavidel et al. Sonar data classification by using few-shot learning and concept extraction
Tang et al. Seabed mixed sediment classification with multi-beam echo sounder backscatter data in Jiaozhou Bay
Mbani et al. Implementation of an automated workflow for image-based seafloor classification with examples from manganese-nodule covered seabed areas in the Central Pacific Ocean
Almaimani Classifying GPR images using convolutional neural networks
Ahmed et al. Improving seabed classification from Multi-Beam Echo Sounder (MBES) backscatter data with visual data mining
Donini et al. Unsupervised semantic segmentation of radar sounder data using contrastive learning