Honkavaara et al., 2016 - Google Patents
- ️Fri Jan 01 2016
Honkavaara et al., 2016
View PDF-
Document ID
- 1308807440335461285 Author
- Eskelinen M
- Pölönen I
- Saari H
- Ojanen H
- Mannila R
- Holmlund C
- Hakala T
- Litkey P
- Rosnell T
- Viljanen N
- Pulkkanen M Publication year
- 2016 Publication venue
- IEEE Transactions on Geoscience and Remote Sensing
External Links
Snippet
Miniaturized hyperspectral imaging sensors are becoming available to small unmanned airborne vehicle (UAV) platforms. Imaging concepts based on frame format offer an attractive alternative to conventional hyperspectral pushbroom scanners because they enable …
- 239000003415 peat 0 title abstract description 59
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01J—MEASUREMENT OF INTENSITY, VELOCITY, SPECTRAL CONTENT, POLARISATION, PHASE OR PULSE CHARACTERISTICS OF INFRA-RED, VISIBLE OR ULTRA-VIOLET LIGHT; COLORIMETRY; RADIATION PYROMETRY
- G01J3/00—Spectrometry; Spectrophotometry; Monochromators; Measuring colour
- G01J3/28—Investigating the spectrum
- G01J3/30—Measuring the intensity of spectral line directly on the spectrum itself
- G01J3/36—Investigating two or more bands of a spectrum by separate detectors
-
- 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
- G01—MEASURING; TESTING
- G01J—MEASUREMENT OF INTENSITY, VELOCITY, SPECTRAL CONTENT, POLARISATION, PHASE OR PULSE CHARACTERISTICS OF INFRA-RED, VISIBLE OR ULTRA-VIOLET LIGHT; COLORIMETRY; RADIATION PYROMETRY
- G01J3/00—Spectrometry; Spectrophotometry; Monochromators; Measuring colour
- G01J3/28—Investigating the spectrum
- G01J3/2823—Imaging spectrometer
-
- 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
- G06T2207/10032—Satellite or aerial image; Remote sensing
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C11/00—Photogrammetry or videogrammetry, e.g. stereogrammetry; Photographic surveying
- G01C11/02—Picture taking arrangements specially adapted for photogrammetry or photographic surveying, e.g. controlling overlapping of pictures
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Honkavaara et al. | 2016 | Remote sensing of 3-D geometry and surface moisture of a peat production area using hyperspectral frame cameras in visible to short-wave infrared spectral ranges onboard a small unmanned airborne vehicle (UAV) |
Aasen et al. | 2018 | Multi-temporal high-resolution imaging spectroscopy with hyperspectral 2D imagers–From theory to application |
Banerjee et al. | 2020 | UAV-hyperspectral imaging of spectrally complex environments |
Iqbal et al. | 2018 | Simplified radiometric calibration for UAS-mounted multispectral sensor |
Aasen et al. | 2015 | Generating 3D hyperspectral information with lightweight UAV snapshot cameras for vegetation monitoring: From camera calibration to quality assurance |
Cao et al. | 2019 | Radiometric calibration assessments for UAS-borne multispectral cameras: Laboratory and field protocols |
Honkavaara et al. | 2012 | Hyperspectral reflectance signatures and point clouds for precision agriculture by light weight UAV imaging system |
Hakala et al. | 2013 | Spectral imaging from UAVs under varying illumination conditions |
US11270112B2 (en) | 2022-03-08 | Systems and methods for rating vegetation health and biomass from remotely sensed morphological and radiometric data |
Miyoshi et al. | 2018 | Radiometric block adjustment of hyperspectral image blocks in the Brazilian environment |
Wang et al. | 2019 | Unmanned Aerial System multispectral mapping for low and variable solar irradiance conditions: Potential of tensor decomposition |
Tuominen et al. | 2017 | Hyperspectral UAV-imagery and photogrammetric canopy height model in estimating forest stand variables |
Guo et al. | 2023 | Inversion of maize leaf area index from UAV hyperspectral and multispectral imagery |
Cubero-Castan et al. | 2018 | Assessment of the radiometric accuracy in a target less work flow using Pix4D software |
Mäkeläinen et al. | 2013 | 2D hyperspectral frame imager camera data in photogrammetric mosaicking |
Näsi et al. | 2016 | UAS based tree species identification using the novel FPI based hyperspectral cameras in visible, NIR and SWIR spectral ranges |
Ahamed et al. | 2012 | Tower remote-sensing system for monitoring energy crops; image acquisition and geometric corrections |
Kurihara et al. | 2020 | Unmanned aerial vehicle (UAV)-based hyperspectral imaging system for precision agriculture and forest management |
Schneider-Zapp et al. | 2019 | A new method to determine multi-angular reflectance factor from lightweight multispectral cameras with sky sensor in a target-less workflow applicable to UAV |
Gautam et al. | 2019 | Footprint determination of a spectroradiometer mounted on an unmanned aircraft system |
Crusiol et al. | 2020 | Reflectance calibration of UAV-based visible and near-infrared digital images acquired under variant altitude and illumination conditions |
Jenerowicz et al. | 2017 | The fusion of satellite and UAV data: simulation of high spatial resolution band |
Tuominen et al. | 2017 | Tree species recognition in species rich area using UAV-borne hyperspectral imagery and stereo-photogrammetric point cloud |
US12026915B2 (en) | 2024-07-02 | Enhanced measurement of photosynthetically active radiation (PAR) and image conversion therefor |
Mancini et al. | 2016 | A multi/hyper-spectral imaging system for land use/land cover using unmanned aerial systems |