Zhang et al., 2018 - Google Patents
Road extraction by deep residual u-netZhang et al., 2018
View PDF- Document ID
- 4813981674871504278
- Author
- Zhang Z
- Liu Q
- Wang Y
- Publication year
- Publication venue
- IEEE Geoscience and Remote Sensing Letters
External Links
Snippet
Road extraction from aerial images has been a hot research topic in the field of remote sensing image analysis. In this letter, a semantic segmentation neural network, which combines the strengths of residual learning and U-Net, is proposed for road area extraction …
- 238000000605 extraction 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
- G06K9/4604—Detecting partial patterns, e.g. edges or contours, or configurations, e.g. loops, corners, strokes, intersections
-
- 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
- G06F17/30781—Information retrieval; Database structures therefor; File system structures therefor of video data
- G06F17/30784—Information retrieval; Database structures therefor; File system structures therefor of video data using features automatically derived from the video content, e.g. descriptors, fingerprints, signatures, genre
- G06F17/30799—Information retrieval; Database structures therefor; File system structures therefor of video data using features automatically derived from the video content, e.g. descriptors, fingerprints, signatures, genre using low-level visual features of the video content
-
- 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
- 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
- G06F17/30247—Information retrieval; Database structures therefor; File system structures therefor in image databases based on features automatically derived from the image data
-
- 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
- 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
- G06K9/32—Aligning or centering of the image pick-up or image-field
- G06K9/3233—Determination of region of interest
-
- 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
-
- 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
- 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
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
-
- 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 |
---|---|---|
Zhang et al. | Road extraction by deep residual u-net | |
Hausler et al. | Patch-netvlad: Multi-scale fusion of locally-global descriptors for place recognition | |
Zhou et al. | Split depth-wise separable graph-convolution network for road extraction in complex environments from high-resolution remote-sensing images | |
Sun et al. | DMA-Net: DeepLab with multi-scale attention for pavement crack segmentation | |
Dong et al. | DenseU-net-based semantic segmentation of small objects in urban remote sensing images | |
Ding et al. | DCU-Net: a dual-channel U-shaped network for image splicing forgery detection | |
Zhao et al. | Hi-Fi: Hierarchical feature integration for skeleton detection | |
Zheng et al. | FarSeg++: Foreground-aware relation network for geospatial object segmentation in high spatial resolution remote sensing imagery | |
El Biach et al. | Encoder-decoder based convolutional neural networks for image forgery detection | |
Jing et al. | Occlusion-aware bi-directional guided network for light field salient object detection | |
Li et al. | Building footprint generation through convolutional neural networks with attraction field representation | |
Yang et al. | Road detection via deep residual dense u-net | |
Yang et al. | DuARE: Automatic road extraction with aerial images and trajectory data at Baidu maps | |
CN117576567B (en) | Remote sensing image change detection method using multi-level difference characteristic self-adaptive fusion | |
Tran et al. | Pp-linknet: Improving semantic segmentation of high resolution satellite imagery with multi-stage training | |
Menon et al. | Pedestrian counting using Yolo V3 | |
Xu et al. | Pixel-level pavement crack detection using enhanced high-resolution semantic network | |
Rani et al. | ShortYOLO-CSP: a decisive incremental improvement for real-time vehicle detection | |
Lei et al. | Pedestrian lane detection for assistive navigation of vision-impaired people: survey and experimental evaluation | |
Zao et al. | Topology-guided road graph extraction from remote sensing images | |
Hu et al. | Generalized image recognition algorithm for sign inventory | |
Patel et al. | Semantic segmentation of cracks on masonry surfaces using deep-learning techniques | |
Chen et al. | MoviNet: a novel network for cross-modal map extraction by vision transformer and CNN | |
Feng et al. | Improved deep fully convolutional network with superpixel-based conditional random fields for building extraction | |
Norelyaqine et al. | Architecture of Deep Convolutional Encoder‐Decoder Networks for Building Footprint Semantic Segmentation |