Zhao et al., 2013 - Google Patents
Hyperspectral imagery super-resolution by spatial–spectral joint nonlocal similarityZhao et al., 2013
View PDF- Document ID
- 3979417000869022752
- Author
- Zhao Y
- Yang J
- Chan J
- Publication year
- Publication venue
- IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
External Links
Snippet
Hyperspectral (HS) super-resolution reconstruction is an ill-posed inversion problem, for which the solution from reconstruction constraint is not unique. To address this, an HS image super-resolution method is proposed to first utilize the joint regulation of spatial and spectral …
- 230000003595 spectral 0 abstract description 35
Classifications
-
- 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
- G06T2207/30—Subject of image; Context of image processing
- G06T2207/30181—Earth observation
-
- 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
- G06T2207/20016—Hierarchical, coarse-to-fine, multiscale or multiresolution image processing; Pyramid transform
-
- 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
- G06T2207/20—Special algorithmic details
- G06T2207/20048—Transform domain processing
- G06T2207/20056—Discrete and fast Fourier transform, [DFT, FFT]
-
- 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
- G06K9/6232—Extracting features by transforming the feature space, e.g. multidimensional scaling; Mappings, e.g. subspace methods
- G06K9/6247—Extracting features by transforming the feature space, e.g. multidimensional scaling; Mappings, e.g. subspace methods based on an approximation criterion, e.g. principal component 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
- G06K9/0063—Recognising patterns in remote scenes, e.g. aerial images, vegetation versus urban areas
-
- 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
- G06T7/00—Image analysis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T5/00—Image enhancement or restoration, e.g. from bit-mapped to bit-mapped creating a similar image
-
- 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
- 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
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Zhao et al. | Hyperspectral imagery super-resolution by spatial–spectral joint nonlocal similarity | |
| Mookambiga et al. | Comprehensive review on fusion techniques for spatial information enhancement in hyperspectral imagery | |
| Veganzones et al. | Hyperspectral super-resolution of locally low rank images from complementary multisource data | |
| Zhao et al. | Hyperspectral imagery super-resolution by sparse representation and spectral regularization | |
| He et al. | A new pansharpening method based on spatial and spectral sparsity priors | |
| Song et al. | Improving the spatial resolution of landsat TM/ETM+ through fusion with SPOT5 images via learning-based super-resolution | |
| Wang et al. | Low-rank tensor completion pansharpening based on haze correction | |
| Liu et al. | A practical pan-sharpening method with wavelet transform and sparse representation | |
| Li et al. | Removal of optically thick clouds from high-resolution satellite imagery using dictionary group learning and interdictionary nonlocal joint sparse coding | |
| Yi et al. | Joint hyperspectral superresolution and unmixing with interactive feedback | |
| Fu et al. | Hyperspectral image denoising via robust subspace estimation and group sparsity constraint | |
| Peng et al. | Hyperspectral image superresolution using global gradient sparse and nonlocal low-rank tensor decomposition with hyper-laplacian prior | |
| Su et al. | Superpixel-based weighted collaborative sparse regression and reweighted low-rank representation for hyperspectral image unmixing | |
| Zhang et al. | Considering nonoverlapped bands construction: A general dictionary learning framework for hyperspectral and multispectral image fusion | |
| Song et al. | Remote sensing images destriping with an enhanced low-rank prior and total variation regulation | |
| Zhang et al. | Local-aware coupled network for hyperspectral image super-resolution | |
| Imani et al. | Pansharpening optimisation using multiresolution analysis and sparse representation | |
| Xu et al. | Hyperspectral image super resolution reconstruction with a joint spectral-spatial sub-pixel mapping model | |
| Licciardi et al. | Super-resolution of hyperspectral images using local spectral unmixing | |
| Lal et al. | Enhanced dictionary based sparse representation fusion for multi-temporal remote sensing images | |
| Teo et al. | Pyramid-based image empirical mode decomposition for the fusion of multispectral and panchromatic images | |
| An et al. | Hyperspectral image fusion by multiplication of spectral constraint and NMF | |
| Valero et al. | Patch-based reconstruction of high resolution satellite image time series with missing values using spatial, spectral and temporal similarities | |
| Sun et al. | A two-stage spatiotemporal fusion method for remote sensing images | |
| Brook | Three-dimensional wavelets-based denoising of hyperspectral imagery |