Xiang et al., 2024 - Google Patents
Automated forest inventory: Analysis of high-density airborne LiDAR point clouds with 3D deep learningXiang et al., 2024
View HTML- Document ID
- 763754649993970591
- Author
- Xiang B
- Wielgosz M
- Kontogianni T
- Peters T
- Puliti S
- Astrup R
- Schindler K
- Publication year
- Publication venue
- Remote Sensing of Environment
External Links
Snippet
Detailed forest inventories are critical for sustainable and flexible management of forest resources, to conserve various ecosystem services. Modern airborne laser scanners deliver high-density point clouds with great potential for fine-scale forest inventory and analysis, but …
- 238000013135 deep learning 0 title abstract description 28
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
- G06K9/36—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
-
- 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
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/20—Special algorithmic details
- G06T2207/20112—Image segmentation details
-
- 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
-
- 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
- G06K9/34—Segmentation of touching or overlapping patterns in the image field
- G06K9/342—Cutting or merging image elements, e.g. region growing, watershed, clustering-based techniques
-
- 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
- G06K9/32—Aligning or centering of the image pick-up or image-field
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/30—Subject of image; Context of image processing
- G06T2207/30004—Biomedical image processing
-
- 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
- G06K9/68—Methods or arrangements for recognition using electronic means using sequential comparisons of the image signals with a plurality of references in which the sequence of the image signals or the references is relevant, e.g. addressable memory
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/30—Subject of image; Context of image processing
- G06T2207/30181—Earth observation
-
- 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
-
- 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
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/40—Analysis of texture
-
- 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
- G06T17/00—Three dimensional [3D] modelling, e.g. data description of 3D objects
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Xiang et al. | Automated forest inventory: Analysis of high-density airborne LiDAR point clouds with 3D deep learning | |
Li et al. | Deep learning-based object detection techniques for remote sensing images: A survey | |
US10607362B2 (en) | Remote determination of containers in geographical region | |
Sun et al. | Individual tree crown segmentation and crown width extraction from a heightmap derived from aerial laser scanning data using a deep learning framework | |
Zhou et al. | Automated residential building detection from airborne LiDAR data with deep neural networks | |
Lee et al. | Adaptive clustering of airborne LiDAR data to segment individual tree crowns in managed pine forests | |
EP3614308A1 (en) | Joint deep learning for land cover and land use classification | |
Koch et al. | Segmentation of forest to tree objects | |
Mayr et al. | Object‐based classification of terrestrial laser scanning point clouds for landslide monitoring | |
Matveev et al. | Def: Deep estimation of sharp geometric features in 3d shapes | |
Polewski et al. | Learning a constrained conditional random field for enhanced segmentation of fallen trees in ALS point clouds | |
Hao et al. | A hierarchical region-merging algorithm for 3-D segmentation of individual trees using UAV-LiDAR point clouds | |
Shao et al. | Efficient co-registration of UAV and ground LiDAR forest point clouds based on canopy shapes | |
Jasiewicz et al. | GeoPAT: A toolbox for pattern-based information retrieval from large geospatial databases | |
Xiao et al. | 3D urban object change detection from aerial and terrestrial point clouds: A review | |
Kostensalo et al. | Recreating structurally realistic tree maps with airborne laser scanning and ground measurements | |
EP3553700A2 (en) | Remote determination of containers in geographical region | |
Robb et al. | A semi-automated method for mapping glacial geomorphology tested at Breiðamerkurjökull, Iceland | |
Chandra et al. | Building detection methods from remotely sensed images | |
Canedo et al. | Uncovering archaeological sites in airborne LiDAR data with data-centric artificial intelligence | |
Lizarazo et al. | Segmentation of remotely sensed imagery: moving from sharp objects to fuzzy regions | |
Li et al. | Measuring detailed urban vegetation with multisource high-resolution remote sensing imagery for environmental design and planning | |
Xu et al. | Find the centroid: A vision‐based approach for optimal object grasping | |
Eshetae | Tree species classification using uav-rgb images and machine learning algorithms in a mixed temperate forest: a case study of Haagse Bos, Netherlands | |
Liu et al. | Identification of Damaged Building Regions from High-Resolution Images Using Superpixel-Based Gradient and Autocorrelation Analysis |