Zuo et al., 2025 - Google Patents
A cross-stage features fusion network for building extraction from remote sensing imagesZuo et al., 2025
View PDF- Document ID
- 13991997675756490477
- Author
- Zuo X
- Shao Z
- Wang J
- Huang X
- Wang Y
- Publication year
- Publication venue
- Geo-Spatial Information Science
External Links
Snippet
The deep learning-based building extraction methods produce different feature maps at different stages of the network, which contain different information features. The detailed information of the feature maps decreases along the depth of the network, and insufficiently …
- 238000000605 extraction 0 title abstract description 73
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
- 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
-
- 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
- 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
- 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
- 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
- G06F17/30861—Retrieval from the Internet, e.g. browsers
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06Q—DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Systems or methods specially adapted for a specific business sector, e.g. utilities or tourism
- G06Q50/01—Social networking
-
- 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
- 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
- G06T2207/30—Subject of image; Context of 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
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T11/00—2D [Two Dimensional] image generation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06Q—DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06Q—DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q40/00—Finance; Insurance; Tax strategies; Processing of corporate or income taxes
-
- 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
- G06T17/05—Geographic models
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06Q—DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q30/00—Commerce, e.g. shopping or e-commerce
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Li et al. | A2-FPN for semantic segmentation of fine-resolution remotely sensed images | |
| Tao et al. | MSNet: Multispectral semantic segmentation network for remote sensing images | |
| Chen et al. | Continuous cross-resolution remote sensing image change detection | |
| Zuo et al. | A cross-stage features fusion network for building extraction from remote sensing images | |
| Abdi et al. | A multi-feature fusion using deep transfer learning for earthquake building damage detection | |
| Yang et al. | Semantic segmentation for remote sensing images based on an AD-HRNet model | |
| Dabove et al. | Revolutionizing urban mapping: deep learning and data fusion strategies for accurate building footprint segmentation | |
| Wang et al. | Instance segmentation of point cloud captured by RGB-D sensor based on deep learning | |
| Lee et al. | A review on recent deep learning-based semantic segmentation for urban greenness measurement | |
| Li et al. | CoupleUNet: Swin Transformer coupling CNNs makes strong contextual encoders for VHR image road extraction | |
| Lu et al. | Global road extraction using a pseudo-label guided framework: from benchmark dataset to cross-region semi-supervised learning | |
| Mei et al. | ADMNet: adaptive deformable convolution large model combining multi-level progressive fusion for Building Change Detection | |
| Wang et al. | Semantic segmentation of urban land classes using a multi-scale dataset | |
| Zhao et al. | Height estimation from single aerial imagery using contrastive learning based multi-scale refinement network | |
| Shafique et al. | BCD-Net: building change detection based on fully scale connected U-Net and subpixel convolution | |
| Guan et al. | Multi-level representation learning via ConvNeXt-based network for unaligned cross-view matching | |
| Yang et al. | Remote sensing object detection based on a combination of a CNN and the Swin transformer | |
| Yiming et al. | A shape-aware enhancement Vision Transformer for building extraction from remote sensing imagery | |
| Putty et al. | Semantic Segmentation of Remotely Sensed Images for Land-use and Land-cover Classification: A Comprehensive Review | |
| Zhuang et al. | Multi-class remote sensing change detection based on model fusion | |
| Wenxin et al. | ADAC: an active domain adaptive network with progressive learning strategy for cloud detection of remote sensing imagery | |
| Zhang et al. | M2Caps: Learning multi-modal capsules of optical and SAR images for land cover classification | |
| Liu et al. | High-resolution building extraction based on the edge-aware network CEEAU_Net | |
| Wang et al. | A boundary enhancement loss function for semantic segmentation of land cover | |
| Cai et al. | CSANet: a channel-spatial attention network for remote sensing image change detection |