Relevant bibliographies by topics / Leafspec Hyperspectral Image Calibration

Academic literature on the topic 'Leafspec Hyperspectral Image Calibration'

Author: Grafiati
Published: 10 December 2022
Last updated: 28 January 2023

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles

Select a source type:

Contents

Consult the lists of relevant articles, books, theses, conference reports, and other scholarly sources on the topic 'Leafspec Hyperspectral Image Calibration.'

Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.

You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.

Journal articles on the topic "Leafspec Hyperspectral Image Calibration"

1

Mäkelä, Mikko, Paul Geladi, Marja Rissanen, Lauri Rautkari, and Olli Dahl. "Hyperspectral near infrared image calibration and regression." Analytica Chimica Acta 1105 (April 2020): 56–63. http://dx.doi.org/10.1016/j.aca.2020.01.019.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

Zhang, Xizhen, Aiwu Zhang, Mengnan Li, Lulu Liu, and Xiaoyan Kang. "Restoration and Calibration of Tilting Hyperspectral Super-Resolution Image." Sensors 20, no. 16 (August 15, 2020): 4589. http://dx.doi.org/10.3390/s20164589.

Full text
Abstract:
Tilting sampling is a novel sampling mode for achieving a higher resolution of hyperspectral imagery. However, most studies on the tilting image have only focused on a single band, which loses the features of hyperspectral imagery. This study focuses on the restoration of tilting hyperspectral imagery and the practicality of its results. First, we reduced the huge data of tilting hyperspectral imagery by the p-value sparse matrix band selection method (pSMBS). Then, we restored the reduced imagery by optimal reciprocal cell combined modulation transfer function (MTF) method. Next, we built the relationship between the restored tilting image and the original normal image. We employed the least square method to solve the calibration equation for each band. Finally, the calibrated tilting image and original normal image were both classified by the unsupervised classification method (K-means) to confirm the practicality of calibrated tilting images in remote sensing applications. The results of classification demonstrate the optimal reciprocal cell combined MTF method can effectively restore the tilting image and the calibrated tiling image can be used in remote sensing applications. The restored and calibrated tilting image has a higher resolution and better spectral fidelity.
APA, Harvard, Vancouver, ISO, and other styles
3

Burger, James, and Paul Geladi. "Hyperspectral NIR image regression part I: calibration and correction." Journal of Chemometrics 19, no. 5-7 (May 2005): 355–63. http://dx.doi.org/10.1002/cem.938.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

Davies, Matthew, Mary B. Stuart, Matthew J. Hobbs, Andrew J. S. McGonigle, and Jon R. Willmott. "Image Correction and In Situ Spectral Calibration for Low-Cost, Smartphone Hyperspectral Imaging." Remote Sensing 14, no. 5 (February 25, 2022): 1152. http://dx.doi.org/10.3390/rs14051152.

Full text
Abstract:
Developments in the portability of low-cost hyperspectral imaging instruments translate to significant benefits to agricultural industries and environmental monitoring applications. These advances can be further explicated by removing the need for complex post-processing and calibration. We propose a method for substantially increasing the utility of portable hyperspectral imaging. Vertical and horizontal spatial distortions introduced into images by ‘operator shake’ are corrected by an in-scene reference card with two spatial references. In situ light-source-independent spectral calibration is performed. This is achieved by a comparison of the ground-truth spectral reflectance of an in-scene red–green–blue target to the uncalibrated output of the hyperspectral data. Finally, bias introduced into the hyperspectral images due to the non-flat spectral output of the illumination is removed. This allows for low-skilled operation of a truly handheld, low-cost hyperspectral imager for agriculture, environmental monitoring, or other visible hyperspectral imaging applications.
APA, Harvard, Vancouver, ISO, and other styles
5

Jiang, Yonghua, Jingyin Wang, Li Zhang, Guo Zhang, Xin Li, and Jiaqi Wu. "Geometric Processing and Accuracy Verification of Zhuhai-1 Hyperspectral Satellites." Remote Sensing 11, no. 9 (April 26, 2019): 996. http://dx.doi.org/10.3390/rs11090996.

Full text
Abstract:
The second batch of Zhuhai-1 microsatellites was successfully launched on 26 April 2018. The batch included four Orbita hyperspectral satellites (referred to as OHS-A, OHS-B, OHS-C, and OHS-D) and one video satellite (OVS-2A), which have excellent hyperspectral data acquisition abilities. For the first time in China, a number of hyperspectral satellite networks have been realized. To ensure the application of hyperspectral remote sensing data, a series of on-orbit geometry processing and accuracy verification studies has been carried out on the “Zhuhai-1” hyperspectral camera since the satellite was launched. This paper presents the geometric processing methods involved in the production of Zhuhai-1 hyperspectral satellite basic products, including geometric calibration and basic product production algorithms. The OHS images were used to perform on-orbit geometric calibration, and the calibration accuracy was better than 0.5 pixels. The registration accuracy of the image spectrum of the basic product after calibration, the single orientation accuracy, and the accuracy of the regional network adjustment were evaluated. The spectral registration accuracy of the OHS basic products is 0.3–0.5 pixels, which is equivalent to the spectral band calibration accuracy. The single orientation accuracy is better than 1.5 pixels and the regional network adjustment accuracy is better than 1.2 pixels. The generated area orthoimages meet the seamless edge requirements, which verifies that the OHS basic product image has good regional mapping capabilities and can meet the application requirements.
APA, Harvard, Vancouver, ISO, and other styles
6

Gorretta, Nathalie, Gilles Rabatel, Jean-Michel Roger, Christophe Fiorio, Camille Lelong, and Veronique Bellon-Maurel. "Hyperspectral Imaging System Calibration Using Image Translations and Fourier Transform." Journal of Near Infrared Spectroscopy 16, no. 4 (January 2008): 371–80. http://dx.doi.org/10.1255/jnirs.809.

Full text
APA, Harvard, Vancouver, ISO, and other styles
7

Aasen, H., J. Bendig, A. Bolten, S. Bennertz, M. Willkomm, and G. Bareth. "Introduction and preliminary results of a calibration for full-frame hyperspectral cameras to monitor agricultural crops with UAVs." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-7 (September 19, 2014): 1–8. http://dx.doi.org/10.5194/isprsarchives-xl-7-1-2014.

Full text
Abstract:
Hyperspectral remote sensing helps to acquire information about the status of agricultural crops to allow optimized management practices in the context of precision agriculture. Due to technological innovations small and lightweight hyperspectral sensors have become available which may be carried by unmanned aerial vehicles (UAVs). In this paper we give a brief overview over existing hyperspectral sensors for UAVs. We focus on a new type of full-frame sensors which capture hyperspectral information in two dimensional image frames. We then develop a calibration procedure for these sensors and identify challenges in remote sensing of vegetation. The calibration is evaluate by in-field data acquired during a flight campaign. The spectral calibration shows good results with less than three percent difference in reflection for 110 of the 125 bands (458 to 886 nm).
APA, Harvard, Vancouver, ISO, and other styles
8

Lyu, S., C. Huang, and M. Hou. "REFLECTANCE RECONSTRUCTION OF HYPERSPECTRAL IMAGE BASED ON GAUSSIAN SURFACE FITTING." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B3-2020 (August 22, 2020): 1365–69. http://dx.doi.org/10.5194/isprs-archives-xliii-b3-2020-1365-2020.

Full text
Abstract:
Abstract. Different from the field of remote sensing, artificial lights are often utilized as the energy source for spectral imaging in the ground hyperspectral applications. The kind of double-spot light source is widely adopted in some large scale ground hyperspectral applications. However, it is hard to reach a satisfied lighting without difference in light intensity in many cases although the lamps are tuned carefully. Therefore, a reflectance calibration of hyperspectral imaging based on the data of diffuse reflectance standard and Gaussian surface fitting is proposed in this paper. The purpose is to improve the reconstruction accuracy of hyperspectral reflectance image by minimized the error caused by the uneven illumination of artificial light source. The method has a higher accuracy than traditional one according to the experiment results.
APA, Harvard, Vancouver, ISO, and other styles
9

Chang, An Jin, Jae Wan Choi, Ah Ram Song, Ye Ji Kim, and Jin Ha Jung. "Vicarious Radiometric Calibration of RapidEye Satellite Image Using CASI Hyperspectral Data." Journal of Korean Society for Geospatial Information System 23, no. 3 (September 30, 2015): 3–10. http://dx.doi.org/10.7319/kogsis.2015.23.3.003.

Full text
APA, Harvard, Vancouver, ISO, and other styles
10

Krtalić, Andrija, Vanja Miljković, Dubravko Gajski, and Ivan Racetin. "Spatial Distortion Assessments of a Low-Cost Laboratory and Field Hyperspectral Imaging System." Sensors 19, no. 19 (October 1, 2019): 4267. http://dx.doi.org/10.3390/s19194267.

Full text
Abstract:
This article describes the adaptation of an existing aerial hyperspectral imaging system in a low-cost setup for collecting hyperspectral data in laboratory and field environment and spatial distortion assessments. The imaging spectrometer system consists of an ImSpector V9 hyperspectral pushbroom scanner, PixelFly high performance digital CCD camera, and a subsystem for navigation, position determination and orientation of the system in space, a sensor bracket and control system. The main objective of the paper is to present the system, with all its limitations, and a spatial calibration method. The results of spatial calibration and calculation of modulation transfer function (MTF) are reported along with examples of images collected and potential uses in agronomy. The distortion value rises drastically at the edges of the image in the near-infrared segment, while the results of MTF calculation showed that the image sharpness was equal for the bands from the visible part of the spectrum, and approached Nyquist’s theory of digitalization. In the near-infrared part of the spectrum, the MTF values showed a less sharp decrease in comparison with the visible part. Preliminary image acquisition indicates that this hyperspectral system has potential in agronomic applications.
APA, Harvard, Vancouver, ISO, and other styles
More sources

Dissertations / Theses on the topic "Leafspec Hyperspectral Image Calibration"

1

Soares, Sófacles Figueredo Carreiro. "Um novo método para transferência de modelos de calibração NIR e uma nova estratégia para classificação de sementes de algodão usando imagem hiperespectral NIR." Universidade Federal da Paraíba, 2016. http://tede.biblioteca.ufpb.br:8080/handle/tede/9237.

Full text
Abstract:
Submitted by ANA KARLA PEREIRA RODRIGUES (anakarla_@hotmail.com) on 2017-08-09T15:33:48Z No. of bitstreams: 1 arquivototal.pdf: 4699110 bytes, checksum: ef3b7c0aa5c4758d2c77e65ad6a81ad3 (MD5)
Made available in DSpace on 2017-08-09T15:33:48Z (GMT). No. of bitstreams: 1 arquivototal.pdf: 4699110 bytes, checksum: ef3b7c0aa5c4758d2c77e65ad6a81ad3 (MD5) Previous issue date: 2016-06-20
Conselho Nacional de Pesquisa e Desenvolvimento Científico e Tecnológico - CNPq
Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - CAPES
This work involves the development of two studies that are presented in chapters 2 and 3. At first, a new method to perform the calibration transfer was designed. This method was developed to make use of separate variables instead of using the full spectrum or spectral windows. To accomplish this task a univariate procedure is initially used to correct the spectra recorded in the secondary equipment, given a set of transfer samples. A robust regression technique is then used to obtain a model with small sensitivity with respect to the univariate correction. The proposed method is employed in two case studies involving near infrared spectrometric determination of specific mass, research octane number and naphtenes in gasoline, and moisture and oil in corn. In both cases, better calibration transfer results were obtained in comparison with piecewise direct standardization (PDS). In the second, a new strategy for cotton seed classification using near infrared (NIR) hyperspectral images (HSI) was developed. Initially the cotton seeds samples were recorded on a station HSI image-NIR and a conventional spectrometer NIR. Thereon, the images were segmented and the mean spectrum of each seed was extract. Classification models SPA-LDA e PLS-DA based on the mean spectral were developed for two data sets. The results for models SPA-LDA and PLSDA showed that the classification with HSI-NIR data set has been achieved with greater accuracy when compared to models for the NIR-conventional data set.
Este trabalho envolve o desenvolvimento de dois estudos, que são apresentados nos capítulos 2 e 3. No primeiro, um novo método para realizar a transferência de calibração foi concebido. Este método foi desenvolvido para fazer uso de variáveis isoladas em vez de usar todo o espectro ou janelas espectrais. Para realizar essa tarefa, um procedimento univariado é inicialmente usado para corrigir os espectros registrados no equipamento secundário, dado um conjunto de amostras de transferência. Uma técnica de regressão robusta é então usada para obter um modelo com pequena sensibilidade em relação aos resíduos da correção univariada. O novo método é então empregado em dois estudos de caso envolvendo análise espectrométrica NIR, em que foram determinados os parâmetros massa específica, RON (Research Octane Number) e teor de naftênicos em gasolina e os teores de água e óleo em amostras de milho. Os resultados do novo método foram melhores do que os obtidos usando o método PDS. No segundo, uma nova estratégia para classificação de sementes de algodão usando imagens hiperespectrais no NIR foi desenvolvido. Inicialmente as amostras de sementes de algodão foram registradas em uma estação de imagem HSI-NIR e em um equipamento NIR convencional. Após isso, as imagens foram segmentadas e os espectros médios de cada semente foram extraídos. Os modelos de classificação SPA-LDA e PLS-DA baseados nos espectros médios foram construídos para os dois conjuntos de dados. Os resultados SPA-LDA e PLS-DA para os modelos demonstraram que a classificação com os dados HSI-NIR foi alcançada com maior exatidão quando comparada aos modelos obtidos usando o NIR-convencional.
APA, Harvard, Vancouver, ISO, and other styles
2

Castorena-Martinez, Juan Enrique. "Non-uniformity correction and calibration of hyperspectral image data." 2010. http://hdl.handle.net/1993/21619.

Full text
APA, Harvard, Vancouver, ISO, and other styles

Book chapters on the topic "Leafspec Hyperspectral Image Calibration"

1

Lins, E. C., S. Pratavieira, W. T. Shigeyosi, M. Dutra-Correa, V. S. Bagnato, C. Kurachi, and L. G. Marcassa. "Assembly, Calibration and Application of a Hyperspectral Image System for Biomedical Imaging." In IFMBE Proceedings, 697–700. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-03879-2_195.

Full text
APA, Harvard, Vancouver, ISO, and other styles

Conference papers on the topic "Leafspec Hyperspectral Image Calibration"

1

Zhang, Xia, Bing Zhang, Fangchao Hu, and Qingxi Tong. "Calibration evaluation of the spaceborne hyperspectral CHRIS image." In Remote Sensing of the Environment: 15th National Symposium on Remote Sensing of China, edited by Qingxi Tong, Wei Gao, and Huadong Guo. SPIE, 2006. http://dx.doi.org/10.1117/12.681242.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

Zhang, Xia, Bing Zhang, Xiurui Geng, Qingxi Tong, and Lanfen Zheng. "Automatic flat field algorithm for hyperspectral image calibration." In Third International Symposium on Multispectral Image Processing and Pattern Recognition, edited by Hanqing Lu and Tianxu Zhang. SPIE, 2003. http://dx.doi.org/10.1117/12.539070.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

Kudenov, Michael W., and Clifton G. Scarboro. "Synthetic neural network calibration of a hyperspectral imaging camera." In Image Sensing Technologies: Materials, Devices, Systems, and Applications V, edited by Nibir K. Dhar and Achyut K. Dutta. SPIE, 2018. http://dx.doi.org/10.1117/12.2305521.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

Livens, Stefan, Joris Blommaert, Dirk Nuyts, Aleksandra Sima, Pieter-Jan Baeck, and Bavo Delaure. "Radiometric calibration of the cosi hyperspectral RPAS camera." In 2016 8th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS). IEEE, 2016. http://dx.doi.org/10.1109/whispers.2016.8071688.

Full text
APA, Harvard, Vancouver, ISO, and other styles
5

Caubet, Christophe, Gilles Guerrini, Pascal Desbarats, and Jean-Philippe Domenger. "Case Study of a Calibration Problem in Acquired Hyperspectral Images." In 2022 IEEE International Conference on Image Processing (ICIP). IEEE, 2022. http://dx.doi.org/10.1109/icip46576.2022.9897839.

Full text
APA, Harvard, Vancouver, ISO, and other styles
6

Boulet, J. C., N. Gorretta, and J. M. Roger. "IDC-Improved Direct Calibration: A new direct calibration method applied to hyperspectral image analysis." In 2009 First Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS). IEEE, 2009. http://dx.doi.org/10.1109/whispers.2009.5289094.

Full text
APA, Harvard, Vancouver, ISO, and other styles
7

Wang, Daming, Guorui Jia, Huijie Zhao, and Ruonan Geng. "Uncertainty analysis of in-flight spectral calibration for hyperspectral imaging spectrometers." In Image and Signal Processing for Remote Sensing, edited by Lorenzo Bruzzone, Francesca Bovolo, and Jon Atli Benediktsson. SPIE, 2017. http://dx.doi.org/10.1117/12.2277702.

Full text
APA, Harvard, Vancouver, ISO, and other styles
8

Brook, Anna, and Eyal Ben Dor. "Supervised Vicarious Calibration (SVC) of hyperspectral remote-sensing data." In 2011 3rd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS). IEEE, 2011. http://dx.doi.org/10.1109/whispers.2011.6080943.

Full text
APA, Harvard, Vancouver, ISO, and other styles
9

Castro, Rodrigo, Daniel Ochoa, and Ronald Criollo. "On the influence of spectral calibration in hyperspectral image classification of leaves." In 2017 CHILEAN Conference on Electrical, Electronics Engineering, Information and Communication Technologies (CHILECON). IEEE, 2017. http://dx.doi.org/10.1109/chilecon.2017.8229687.

Full text
APA, Harvard, Vancouver, ISO, and other styles
10

Markelin, Lauri, Eija Honkavaara, Tuure Takala, and Petri Pellikka. "Calibration and validation of hyperspectral imagery using a permanent test field." In 2013 5th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS). IEEE, 2013. http://dx.doi.org/10.1109/whispers.2013.8080708.

Full text
APA, Harvard, Vancouver, ISO, and other styles

Reports on the topic "Leafspec Hyperspectral Image Calibration"

1

Anderson, Gerald L., and Kalman Peleg. Precision Cropping by Remotely Sensed Prorotype Plots and Calibration in the Complex Domain. United States Department of Agriculture, December 2002. http://dx.doi.org/10.32747/2002.7585193.bard.

Full text
Abstract:
This research report describes a methodology whereby multi-spectral and hyperspectral imagery from remote sensing, is used for deriving predicted field maps of selected plant growth attributes which are required for precision cropping. A major task in precision cropping is to establish areas of the field that differ from the rest of the field and share a common characteristic. Yield distribution f maps can be prepared by yield monitors, which are available for some harvester types. Other field attributes of interest in precision cropping, e.g. soil properties, leaf Nitrate, biomass etc. are obtained by manual sampling of the filed in a grid pattern. Maps of various field attributes are then prepared from these samples by the "Inverse Distance" interpolation method or by Kriging. An improved interpolation method was developed which is based on minimizing the overall curvature of the resulting map. Such maps are the ground truth reference, used for training the algorithm that generates the predicted field maps from remote sensing imagery. Both the reference and the predicted maps are stratified into "Prototype Plots", e.g. 15xl5 blocks of 2m pixels whereby the block size is 30x30m. This averaging reduces the datasets to manageable size and significantly improves the typically poor repeatability of remote sensing imaging systems. In the first two years of the project we used the Normalized Difference Vegetation Index (NDVI), for generating predicted yield maps of sugar beets and com. The NDVI was computed from image cubes of three spectral bands, generated by an optically filtered three camera video imaging system. A two dimensional FFT based regression model Y=f(X), was used wherein Y was the reference map and X=NDVI was the predictor. The FFT regression method applies the "Wavelet Based", "Pixel Block" and "Image Rotation" transforms to the reference and remote images, prior to the Fast - Fourier Transform (FFT) Regression method with the "Phase Lock" option. A complex domain based map Yfft is derived by least squares minimization between the amplitude matrices of X and Y, via the 2D FFT. For one time predictions, the phase matrix of Y is combined with the amplitude matrix ofYfft, whereby an improved predicted map Yplock is formed. Usually, the residuals of Y plock versus Y are about half of the values of Yfft versus Y. For long term predictions, the phase matrix of a "field mask" is combined with the amplitude matrices of the reference image Y and the predicted image Yfft. The field mask is a binary image of a pre-selected region of interest in X and Y. The resultant maps Ypref and Ypred aremodified versions of Y and Yfft respectively. The residuals of Ypred versus Ypref are even lower than the residuals of Yplock versus Y. The maps, Ypref and Ypred represent a close consensus of two independent imaging methods which "view" the same target. In the last two years of the project our remote sensing capability was expanded by addition of a CASI II airborne hyperspectral imaging system and an ASD hyperspectral radiometer. Unfortunately, the cross-noice and poor repeatability problem we had in multi-spectral imaging was exasperated in hyperspectral imaging. We have been able to overcome this problem by over-flying each field twice in rapid succession and developing the Repeatability Index (RI). The RI quantifies the repeatability of each spectral band in the hyperspectral image cube. Thereby, it is possible to select the bands of higher repeatability for inclusion in the prediction model while bands of low repeatability are excluded. Further segregation of high and low repeatability bands takes place in the prediction model algorithm, which is based on a combination of a "Genetic Algorithm" and Partial Least Squares", (PLS-GA). In summary, modus operandi was developed, for deriving important plant growth attribute maps (yield, leaf nitrate, biomass and sugar percent in beets), from remote sensing imagery, with sufficient accuracy for precision cropping applications. This achievement is remarkable, given the inherently high cross-noice between the reference and remote imagery as well as the highly non-repeatable nature of remote sensing systems. The above methodologies may be readily adopted by commercial companies, which specialize in proving remotely sensed data to farmers.
APA, Harvard, Vancouver, ISO, and other styles
You might also be interested in the extended bibliographies on the topic 'Leafspec Hyperspectral Image Calibration' for particular source types:
Journal articles
We offer discounts on all premium plans for authors whose works are included in thematic literature selections. Contact us to get a unique promo code!

To the bibliography