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

Boston et al., 2024 - Google Patents

U-Net Convolutional Neural Network for Mapping Natural Vegetation and Forest Types from Landsat Imagery in Southeastern Australia

Boston et al., 2024

View PDF
Document ID
4873730024891013752
Author
Boston T
Van Dijk A
Thackway R
Publication year
Publication venue
Journal of Imaging

External Links

Snippet

Accurate and comparable annual mapping is critical to understanding changing vegetation distribution and informing land use planning and management. A U-Net convolutional neural network (CNN) model was used to map natural vegetation and forest types based on …
Continue reading at www.mdpi.com (PDF) (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
    • G06K9/00624Recognising scenes, i.e. recognition of a whole field of perception; recognising scene-specific objects
    • G06K9/0063Recognising patterns in remote scenes, e.g. aerial images, vegetation versus urban areas
    • G06K9/00657Recognising patterns in remote scenes, e.g. aerial images, vegetation versus urban areas of vegetation
    • 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
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00Indexing scheme for image analysis or image enhancement
    • G06T2207/10Image acquisition modality
    • G06T2207/10032Satellite or aerial image; Remote sensing
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00Indexing scheme for image analysis or image enhancement
    • G06T2207/30Subject of image; Context of image processing
    • G06T2207/30181Earth observation
    • 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
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00Indexing scheme for image analysis or image enhancement
    • G06T2207/20Special algorithmic details
    • G06T2207/20112Image segmentation details
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00Indexing scheme for image analysis or image enhancement
    • G06T2207/10Image acquisition modality
    • G06T2207/10024Color image
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00Indexing scheme for image analysis or image enhancement
    • G06T2207/30Subject of image; Context of image processing
    • G06T2207/30004Biomedical image processing
    • 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
    • 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
    • G06K9/00127Acquiring and recognising microscopic objects, e.g. biological cells and cellular parts
    • G06K9/0014Pre-processing, e.g. image segmentation ; Feature extraction
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00Image analysis
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T17/00Three dimensional [3D] modelling, e.g. data description of 3D objects
    • G06T17/05Geographic models
    • 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
    • G06F17/30241Information retrieval; Database structures therefor; File system structures therefor in geographical information databases
    • 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

Similar Documents

Publication Publication Date Title
Yin et al. Mapping agricultural land abandonment from spatial and temporal segmentation of Landsat time series
Lv et al. Novel adaptive histogram trend similarity approach for land cover change detection by using bitemporal very-high-resolution remote sensing images
Ahmed et al. Characterizing stand-level forest canopy cover and height using Landsat time series, samples of airborne LiDAR, and the Random Forest algorithm
Lucas et al. The earth observation data for habitat monitoring (EODHaM) system
Lu et al. Land use/cover classification in the Brazilian Amazon using satellite images
George et al. Forest tree species discrimination in western Himalaya using EO-1 Hyperion
Onojeghuo et al. Optimising the use of hyperspectral and LiDAR data for mapping reedbed habitats
Zhao et al. A systematic review of individual tree crown detection and delineation with convolutional neural networks (CNN)
Yang et al. Mapping understory plant communities in deciduous forests from Sentinel-2 time series
Tang et al. Definition and measurement of tree cover: A comparative analysis of field-, lidar-and landsat-based tree cover estimations in the Sierra national forests, USA
Fonseca et al. Pattern recognition and remote sensing techniques applied to land use and land cover mapping in the Brazilian Savannah
Locher-Krause et al. Expanding temporal resolution in landscape transformations: Insights from a landsat-based case study in Southern Chile
Boston et al. U-Net Convolutional Neural Network for Mapping Natural Vegetation and Forest Types from Landsat Imagery in Southeastern Australia
Shiu et al. Mapping paddy rice agriculture in a highly fragmented area using a geographic information system object-based post classification process
Tapsall et al. Analysis of RapidEye imagery for annual landcover mapping as an aid to European Union (EU) common agricultural policy
Yan et al. High-resolution mapping of paddy rice fields from unmanned airborne vehicle images using enhanced-TransUnet
Hashim et al. Land use land cover analysis with pixel-based classification approach
López et al. Land cover classification of VHR airborne images for citrus grove identification
Jhonnerie et al. Comparison of random forest algorithm which implemented on object and pixel based classification for mangrove land cover mapping
Dibs et al. Estimation and Mapping the Rubber Trees Growth Distribution using Multi Sensor Imagery With Remote Sensing and GIS Analysis
Saini et al. Automatic mapping of deciduous and evergreen forest by using machine learning and satellite imagery
Moumni et al. Argan Tree (Argania spinosa (L.) Skeels) Mapping Based on Multisensor Fusion of Satellite Imagery in Essaouira Province, Morocco
Sarti et al. Trees outside forest in Italian agroforestry landscapes: detection and mapping using sentinel-2 imagery
Boston et al. Convolutional Neural Network Outperforms Random Forests in Mapping Natural Vegetation and Forest Types from Landsat Imagery in Southeastern Australia
Wahyuni Forest change analysis using OBIA approach and supervised classification a case study: Kolaka District, South East Sulawesi