Rout et al., 2019 - Google Patents
Deepswir: A deep learning based approach for the synthesis of short-wave infrared band using multi-sensor concurrent datasetsRout et al., 2019
View PDF- Document ID
- 17219979912864350169
- Author
- Rout L
- Bhateja Y
- Garg A
- Mishra I
- Moorthi S
- Dhar D
- Publication year
- Publication venue
- arXiv preprint arXiv:1905.02749
External Links
Snippet
Convolutional Neural Network (CNN) is achieving remarkable progress in various computer vision tasks. In the past few years, the remote sensing community has observed Deep Neural Network (DNN) finally taking off in several challenging fields. In this study, we …
- 230000002194 synthesizing 0 title description 12
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
- G06K9/00624—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
- 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
- G06T2207/10032—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
- 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
- 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
- G06T2207/10—Image acquisition modality
- G06T2207/10024—Color 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
- 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
- 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
-
- 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
-
- 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
- G06T5/00—Image enhancement or restoration, e.g. from bit-mapped to bit-mapped creating a similar image
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Zhang et al. | Remote sensing image spatiotemporal fusion using a generative adversarial network | |
| Zhu et al. | Deep learning meets SAR: Concepts, models, pitfalls, and perspectives | |
| Kemker et al. | Algorithms for semantic segmentation of multispectral remote sensing imagery using deep learning | |
| Ozcelik et al. | Rethinking CNN-based pansharpening: Guided colorization of panchromatic images via GANs | |
| Ren et al. | A dual-stream high resolution network: Deep fusion of GF-2 and GF-3 data for land cover classification | |
| Long et al. | Dual self-attention Swin transformer for hyperspectral image super-resolution | |
| Enomoto et al. | Filmy cloud removal on satellite imagery with multispectral conditional generative adversarial nets | |
| Mou et al. | Multitemporal very high resolution from space: Outcome of the 2016 IEEE GRSS data fusion contest | |
| Gómez-Chova et al. | Multimodal classification of remote sensing images: A review and future directions | |
| Ge et al. | Improved semisupervised UNet deep learning model for forest height mapping with satellite SAR and optical data | |
| Ban et al. | Object-based fusion of multitemporal multiangle ENVISAT ASAR and HJ-1B multispectral data for urban land-cover mapping | |
| Nijhawan et al. | A deep learning hybrid CNN framework approach for vegetation cover mapping using deep features | |
| Pastorino et al. | Semantic segmentation of remote-sensing images through fully convolutional neural networks and hierarchical probabilistic graphical models | |
| Fotso Kamga et al. | Advancements in satellite image classification: methodologies, techniques, approaches and applications | |
| Albanwan et al. | Image fusion in remote sensing: An overview and meta analysis | |
| Postadjian et al. | Investigating the potential of deep neural networks for large-scale classification of very high resolution satellite images | |
| Wang et al. | Problems in remote sensing of landscapes and habitats | |
| Liu et al. | Thick cloud removal under land cover changes using multisource satellite imagery and a spatiotemporal attention network | |
| Singh et al. | Deep learning-based semantic segmentation of three-dimensional point cloud: a comprehensive review | |
| Ghamisi et al. | Multisource and multitemporal data fusion in remote sensing | |
| Rout et al. | Deepswir: A deep learning based approach for the synthesis of short-wave infrared band using multi-sensor concurrent datasets | |
| Duan et al. | Efficient cloud removal network for satellite images using sar-optical image fusion | |
| Jing et al. | Cloud removal for optical remote sensing imagery using the SPA-CycleGAN network | |
| Yang et al. | Deep residual network with multi-image attention for imputing under clouds in satellite imagery | |
| He et al. | Two spectral–spatial implicit neural representations for arbitrary-resolution hyperspectral pansharpening |