Wang et al., 2023 - Google Patents
Sub-pixel mapping of spectral imagery based on deviation information measurementWang et al., 2023
- Document ID
- 13760070438851362370
- Author
- Wang P
- Zhang Y
- Wang L
- Zhang L
- Leung H
- Publication year
- Publication venue
- IEEE Transactions on Instrumentation and Measurement
External Links
Snippet
Sub-pixel mapping (SPM) technology can analyze mixed pixels in spectral image and realize the transformation from abundance images to a fine sub-pixel classification image. Since the SPM belongs to an ill-posed issue, deviation information (DI) inevitably is in …
- 230000003595 spectral 0 title abstract description 34
Classifications
-
- 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
- G06F17/30943—Information retrieval; Database structures therefor; File system structures therefor details of database functions independent of the retrieved data type
- G06F17/30994—Browsing or visualization
-
- 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
- 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
- G06F17/30286—Information retrieval; Database structures therefor; File system structures therefor in structured data stores
-
- 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
- G06T17/00—Three dimensional [3D] modelling, e.g. data description of 3D objects
- G06T17/05—Geographic models
-
- 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
- G06T11/20—Drawing from basic elements, e.g. lines or circles
- G06T11/206—Drawing of charts or graphs
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
-
- 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
- G06T3/00—Geometric image transformation in the plane of the image, e.g. from bit-mapped to bit-mapped creating a different image
- G06T3/40—Scaling the whole image or part thereof
- G06T3/4053—Super resolution, i.e. output image resolution higher than sensor resolution
- G06T3/4061—Super resolution, i.e. output image resolution higher than sensor resolution by injecting details from a different spectral band
-
- 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
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Song et al. | Spatiotemporal satellite image fusion using deep convolutional neural networks | |
Amarsaikhan et al. | Data fusion and multisource image classification | |
Li et al. | Coastal wetland loss and environmental change due to rapid urban expansion in Lianyungang, Jiangsu, China | |
Nivedita Priyadarshini et al. | A comparative study of advanced land use/land cover classification algorithms using Sentinel-2 data | |
Wang et al. | Spectral-spatial global graph reasoning for hyperspectral image classification | |
Yang et al. | MSFusion: Multistage for remote sensing image spatiotemporal fusion based on texture transformer and convolutional neural network | |
Xu et al. | Bridging CNN and transformer with cross-attention fusion network for hyperspectral image classification | |
Cai et al. | A novel unsupervised deep learning method for the generalization of urban form | |
Wang et al. | Using multiple subpixel shifted images with spatial–spectral information in soft-then-hard subpixel mapping | |
Wang et al. | Sub-pixel mapping of spectral imagery based on deviation information measurement | |
Li et al. | A pseudo-siamese deep convolutional neural network for spatiotemporal satellite image fusion | |
Cui et al. | Feature fusion network model based on dual attention mechanism for hyperspectral image classification | |
CN106373120A (en) | Multi-temporal remote sensing image change detection method based on non-negative matrix decomposition and nucleus FCM | |
Bai et al. | AeroDetectNet: a lightweight, high-precision network for enhanced detection of small objects in aerial remote sensing imagery | |
Wang et al. | PCDASNet: position-constrained differential attention Siamese network for building damage assessment | |
CN111680667A (en) | A classification method of remote sensing images based on deep neural network | |
Wang et al. | Target detection algorithm based on super-resolution color remote sensing image reconstruction | |
Han et al. | Subpixel spectral variability network for hyperspectral image classification | |
Wang et al. | LLNet: Lightweight network with a channel and spatial attention mechanism for local climate zone classification from Sentinel-2 image | |
Wu et al. | DF4LCZ: A SAM-empowered data fusion framework for scene-level local climate zone classification | |
CN107220615B (en) | Urban impervious surface information extraction method fusing interest point big data | |
Pu et al. | Mapping urban areas using dense time series of landsat images and google earth engine | |
Liang et al. | Transformer-based multi-scale feature fusion network for remote sensing change detection | |
Li et al. | Subpixel change detection based on improved abundance values for remote sensing images | |
Tan et al. | BD-MSA: Body decouple VHR remote sensing image change detection method guided by multiscale feature information aggregation |