Relevant bibliographies by topics / CHRIS hyperspectral

Academic literature on the topic 'CHRIS hyperspectral'

Author: Grafiati
Published: 4 June 2021
Last updated: 9 February 2022

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Journal articles on the topic "CHRIS hyperspectral"

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Verrelst, Jochem, Juan Rivera Caicedo, Jorge Vicent, Pablo Morcillo Pallarés, and José Moreno. "Approximating Empirical Surface Reflectance Data through Emulation: Opportunities for Synthetic Scene Generation." Remote Sensing 11, no. 2 (January 16, 2019): 157. http://dx.doi.org/10.3390/rs11020157.

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Collection of spectroradiometric measurements with associated biophysical variables is an essential part of the development and validation of optical remote sensing vegetation products. However, their quality can only be assessed in the subsequent analysis, and often there is a need for collecting extra data, e.g., to fill in gaps. To generate empirical-like surface reflectance data of vegetated surfaces, we propose to exploit emulation, i.e., reconstruction of spectral measurements through statistical learning. We evaluated emulation against classical interpolation methods using an empirical field dataset with associated hyperspectral spaceborne CHRIS and airborne HyMap reflectance spectra, to produce synthetic CHRIS and HyMap reflectance spectra for any combination of input biophysical variables. Results indicate that: (1) emulation produces surface reflectance data more accurately than interpolation when validating against a separate part of the field dataset; and (2) emulation produces the spectra multiple times (tens to hundreds) faster than interpolation. This technique opens various data processing opportunities, e.g., emulators not only allow rapidly producing large synthetic spectral datasets, but they can also speed up computationally intensive processing routines such as synthetic scene generation. To demonstrate this, emulators were run to simulate hyperspectral imagery based on input maps of a few biophysical variables coming from CHRIS, HyMap and Sentinel-2 (S2). The emulators produced spaceborne CHRIS-like and airborne HyMap-like surface reflectance imagery in the order of seconds, thereby approximating the spectra of vegetated surfaces sufficiently similar to the reference images. Similarly, it took a few minutes to produce a hyperspectral data cube with a spatial texture of S2 and a spectral resolution of HyMap.
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Fan, W., X. Xu, X. Liu, B. Yan, and Y. Cui. "Accurate LAI retrieval method based on PROBA/CHRIS data." Hydrology and Earth System Sciences Discussions 6, no. 6 (November 12, 2009): 7001–24. http://dx.doi.org/10.5194/hessd-6-7001-2009.

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Abstract. Leaf area index (LAI) is one of the key structural variables in terrestrial vegetation ecosystems. Remote sensing offers a chance to derive LAI in regional scales accurately. Variations of background, atmospheric conditions and the anisotropy of canopy reflectance are three factors that can strongly restrain the accuracy of retrieved LAI. Based on the hybrid canopy reflectance model, a new hyperspectral directional second derivative method (DSD) is proposed in this paper. This method can estimate LAI accurately through analyzing the canopy anisotropy. The effect of the background can also be effectively removed. So the inversion precision and the dynamic range can be improved remarkably, which has been proved by numerical simulations. As the derivative method is very sensitive to the random noise, we put forward an innovative filtering approach, by which the data can be de-noised in spectral and spatial dimensions synchronously. It shows that the filtering method can remove the random noise effectively; therefore, the method can be performed to the remotely sensed hyperspectral image. The study region is situated in Zhangye, Gansu Province, China; the hyperspectral and multi-angular image of the study region has been acquired from Compact High-Resolution Imaging Spectrometer/Project for On-Board Autonomy (CHRIS/PROBA), on 4 and 14 June 2008. After the pre-processing procedures, the DSD method was applied, and the retrieve LAI was validated by the ground truth of 11 sites. It shows that by applying innovative filtering method, the new LAI inversion method is accurate and effective.
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Fan, W. J., X. R. Xu, X. C. Liu, B. Y. Yan, and Y. K. Cui. "Accurate LAI retrieval method based on PROBA/CHRIS data." Hydrology and Earth System Sciences 14, no. 8 (August 10, 2010): 1499–507. http://dx.doi.org/10.5194/hess-14-1499-2010.

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Abstract. Leaf area index (LAI) is one of the key structural variables in terrestrial vegetation ecosystems. Remote sensing offers an opportunity to accurately derive LAI at regional scales. The anisotropy of canopy reflectance, variations in background characteristics, and variability in atmospheric conditions constitute three factors that can strongly constrain the accuracy of retrieved LAI. Based on a hybrid canopy reflectance model, a new hyperspectral directional second derivative method (DSD) is proposed in this paper. This method can estimate LAI accurately through analyzing the canopy anisotropy. The effect of the background can also be effectively removed. With the aid of a widely-accepted atmospheric model, the influence of atmospheric conditions can be minimized as well. Thus the inversion precision and the dynamic range can be markedly improved, which has been proved by numerical simulations. As the derivative method is very sensitive to random noise, we put forward an innovative filtering approach, by which the data can be de-noised in spectral and spatial dimensions synchronously. It shows that the filtering method can remove random noise effectively; therefore, the method can be applied to hyperspectral images. The study region was situated in Zhangye, Gansu Province, China; hyperspectral and multi-angular images of the study region were acquired via the Compact High-Resolution Imaging Spectrometer/Project for On-Board Autonomy (CHRIS/PROBA), on 4 June 2008. After the pre-processing procedures, the DSD method was applied, and the retrieved LAI was validated by ground reference data at 11 sites. Results show that the new LAI inversion method is accurate and effective with the aid of the innovative filtering method.
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Garcia Millan, Virginia, and Arturo Sanchez-Azofeifa. "Quantifying Changes on Forest Succession in a Dry Tropical Forest Using Angular-Hyperspectral Remote Sensing." Remote Sensing 10, no. 12 (November 22, 2018): 1865. http://dx.doi.org/10.3390/rs10121865.

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The tropical dry forest (TDF) is one the most threatened ecosystems in South America, existing on a landscape with different levels of ecological succession. Among satellites dedicated to Earth observation and monitoring ecosystem succession, CHRIS/PROBA is the only satellite that presents quasi-simultaneous multi-angular pointing and hyperspectral imaging. These two characteristics permit the study of structural and compositional differences of TDFs with different levels of succession. In this paper, we use 2008 and 2014 CHRIS/PROBA images from a TDF in Minas Gerais, Brazil to study ecosystem succession after abandonment. Using a −55° angle of observation; several classifiers including spectral angle mapper (SAM), support vector machine (SVM), and decision trees (DT) were used to test how well they discriminate between different successional stages. Our findings suggest that the SAM is the most appropriate method to classify TDFs as a function of succession (accuracies ~80 for % for late stage, ~85% for the intermediate stage, ~70% for early stage, and ~50% for other classes). Although CHRIS/PROBA cannot be used for large-scale/long-term monitoring of tropical forests because of its experimental nature; our results support the potential of using multi-angle hyperspectral data to characterize the structure and composition of TDFs in the near future.
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Wang, Qiang, Yong Pang, Weiwei Jia, Haowei Zhang, and Chaoyang Wang. "Effective and Universal Pre-Processing for Multi-Angle CHRIS/PROBA Images." Journal of the Indian Society of Remote Sensing 49, no. 7 (March 16, 2021): 1581–91. http://dx.doi.org/10.1007/s12524-020-01288-0.

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AbstractCompared with traditional nadir observations, multi-angle hyperspectral remote sensing can obtain more spatial and spectral information and improve the inversion precision of structure information on the Earth’s surface. However, processing multi-angle remote-sensing images presents new challenges. Owing to the multi-angle sensors used to obtain multi-angle images, there are major differences in the spatial and spectral information between each angle in these images. Data from the Compact High-resolution Imaging Spectrometer (CHRIS) on Project for On-Board Autonomy (PROBA) should be pre-processed to extract the BRDF (Bidirectional Reflectance Distribution Functions). Given the limitations of the pre-processing software currently available for CHRIS/PROBA images, and the lack of metadata and auxiliary origin schedules, some CHRIS multi-angle images cannot be pre-processed correctly. In the study, to promote the application of multi-angle data, a formula for calculating key parameters according to in-orbit geometric imaging relationships is derived to design a multi-angle image process flow including image rollovers, bad-line repairs, orthorectification and atmospheric corrections accounting for terrain effects. Test results indicate that the pre-processing method can quickly and effectively recover multi-angle hyperspectral information and obtain spectral characteristics of multi-angle observations.
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Gürsoy, Ö., A. C. Birdal, F. Özyonar, and E. Kasaka. "Determining and Monitoring the Water Quality of Kizilirmak River of Turkey: First Results." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-7/W3 (April 30, 2015): 1469–74. http://dx.doi.org/10.5194/isprsarchives-xl-7-w3-1469-2015.

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Water resources are getting more and more important with each passing day in case of survival of humanity. For this reason, assessing water resources’ quality and also monitoring them have attracted lots of attention in the recent years. Remote sensing has been growing widely in the last decade and its resources are very usable when it comes to water resources management. In this study, by using remote sensing technology, satellite images that have 350 to 1050 nanometres wavelength band sensors (e.g. CHRIS Proba) are used to determine the quality of the Kizilirmak River’s water. Kizilirmak River is born and also pours out to sea in country limits of Turkey. It is the longest river of the country by the length of 1355 kilometres. Through the river’s resources, ground based spectral measurements are made to identify the quality differences of the water at the test spots that have been determined before. In this context at Imranli, where the river contacts civilization for the first time, which is located in Sivas city of Turkey, samples are gathered in order to do ground based spectroradiometer measurements. These samples are gathered simultaneously with the image acquiring time of CHRIS Proba satellite. Spectral signatures that are obtained from ground measurements are used as reference data in order to classify CHRIS Proba satellite’s hyperspectral images over the study area. Satellite images are classified based on Chemical Oxygen Demand (COD), Turbidity and Electrical Conductivity (EC) attributes. As a result, interpretations obtained from classified CHRIS Proba satellite hyperspectral images of the study area are presented.
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Sahithi, V. S., and S. Agrawal. "Sub pixel location identification using super resolved multilooking CHRIS data." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-8 (November 28, 2014): 463–68. http://dx.doi.org/10.5194/isprsarchives-xl-8-463-2014.

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CHRIS /Proba is a multiviewing hyperspectral sensor that monitors the earth in five different zenith angles +55°, +36°, nadir, −36° and −55° with a spatial resolution of 17 m and within a spectral range of 400–1050 nm in mode 3. These multiviewing images are suitable for constructing a super resolved high resolution image that can reveal the mixed pixel of the hyperspectral image. In the present work, an attempt is made to find the location of various features constituted within the 17m mixed pixel of the CHRIS image using various super resolution reconstruction techniques. Four different super resolution reconstruction techniques namely interpolation, iterative back projection, projection on to convex sets (POCS) and robust super resolution were tried on the −36, nadir and +36 images to construct a super resolved high resolution 5.6 m image. The results of super resolution reconstruction were compared with the scaled nadir image and bicubic convoluted image for comparision of the spatial and spectral property preservance. A support vector machine classification of the best super resolved high resolution image was performed to analyse the location of the sub pixel features. Validation of the obtained results was performed using the spectral unmixing fraction images and the 5.6 m classified LISS IV image.
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Duca, Riccardo, and Fabio Del Frate. "Hyperspectral and Multiangle CHRIS–PROBA Images for the Generation of Land Cover Maps." IEEE Transactions on Geoscience and Remote Sensing 46, no. 10 (October 2008): 2857–66. http://dx.doi.org/10.1109/tgrs.2008.2000741.

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Wang, Qing, Zhengke Zhang, Zengzhou Hao, Bingling Liu, and Jilian Xiong. "Optical Classification of Coastal Water Body in China using Hyperspectral Imagery CHRIS/PROBA." IOP Conference Series: Earth and Environmental Science 668, no. 1 (February 1, 2021): 012017. http://dx.doi.org/10.1088/1755-1315/668/1/012017.

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Awad, Mohamad M. "HYPERSPECTRAL REMOTE SENSING ROLE IN ENHANCING CROP MAPPING: A COMPARISON BETWEEN DIFFERENT SUPERVISED SEGMENTATION ALGORITHMS." SWS Journal of EARTH AND PLANETARY SCIENCES 1, no. 1 (June 1, 2019): 25–37. http://dx.doi.org/10.35603/eps2019/issue1.03.

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In agriculture sector there is need for cheap, fast, and accurate data and technologies to help decision makers to find solutions for many agricultural problems. Many solutions depend significantly on the accuracy and efficiency of the crop mapping and crop yield estimation processes. High resolution spectral remote sensing can improve substantially crop mapping by reducing similarities between different crop types which has similar ecological conditions. This paper presents a new approach of combining a new tool, hyperspectral images and technologies to enhance crop mapping. The tool includes spectral signatures database for the major crops in the Eastern Mediterranean Basin and other important metadata and processing functions. To prove the efficiency of the new approach, major crops such as “winter wheat” and “spring potato” are mapped using the spectral signatures database in the new tool, three different supervised algorithms, and CHRIS-Proba hyperspectral satellite images. The evaluation of the results showed that deploying different hyperspectral data and technologies can improve crop mapping. The improvements can be noticed with the increase of the accuracy to more than 86% with the use of the supervised algorithm Spectral Angle Mapper (SAM).
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Dissertations / Theses on the topic "CHRIS hyperspectral"

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Kamalesh, Vidhya Lakshmi. "Vegetation parameter retrieval from hyperspectral, multiple view angle PROBA/CHRIS data." Thesis, Swansea University, 2011. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.678514.

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Sugianto, Biological Earth &amp Environmental Science UNSW. "Multi-angular hyperspectral data and its influences on soil and plant property measurements: spectral mapping and functional data analysis approach." Awarded by:University of New South Wales. Biological, Earth and Environmental Science, 2006. http://handle.unsw.edu.au/1959.4/25531.

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This research investigates the spectral reflectance characteristics of soil and vegetation using multi-angular and single view hyperspectral data. The question of the thesis is ???How much information can be obtained from multi-angular hyperspectral remote sensing in comparison with single view angle hyperspectral remote sensing of soil and vegetation???? This question is addressed by analysing multi-angular and single view angle hyperspectral remote sensing using data from the field, airborne and space borne hyperspectral sensors. Spectral mapping, spectral indices and Functional Data Analysis (FDA) are used to analyse the data. Spectral mapping has been successfully used to distinguish features of soil and cotton with hyperspectral data. Traditionally, spectral mapping is based on collecting endmembers of pure pixels and using these as training areas for supervised classification. There are, however, limitations in the use of these algorithms when applied to multi-angular images, as the reflectance of a single ground unit will differ at each angle. Classifications using six-class endmembers identified using single angle imagery were assessed using multi-angular Compact High Resolution Imaging Spectrometer (CHRIS) imagery, as well as a set of vegetation indices. The results showed no significant difference between the angles. Low nutrient content in the soil produced lower vegetation index values, and more nutrients increased the index values. This research introduces FDA as an image processing tool for multi-angular hyperspectral imagery of soil and cotton, using basis functions for functional principal component analysis (fPCA) and functional linear modelling. FDA has advantages over conventional statistical analysis because it does not assume the errors in the data are independent and uncorrelated. Investigations showed that B-splines with 20-basis functions was the best fit for multi-angular soil spectra collected using the spectroradiometer and the satellite mounted CHRIS. Cotton spectra collected from greenhouse plants using a spectrodiometer needed 30-basis functions to fit the model, while 20-basis functions were sufficient for cotton spectra extracted from CHRIS. Functional principal component analysis (fPCA) of multi-angular soil spectra show the first fPCA explained a minimum of 92.5% of the variance of field soil spectra for different azimuth and zenith angles and 93.2% from CHRIS for the same target. For cotton, more than 93.6% of greenhouse trial and 70.6% from the CHRIS data were explained by the first fPCA. Conventional analysis of multi-angular hyperspectral data showed significant differences exist between soil spectra acquired at different azimuth and zenith angles. Forward scan direction of zenith angle provides higher spectral reflectance than backward direction. However, most multi-angular hyperspectral data analysed as functional data show no significant difference from nadir, except for small parts of the wavelength of cotton spectra using CHRIS. There is also no significant difference for soil spectra analysed as functional data collected from the field, although there was some difference for soil spectra extracted from CHRIS. Overall, the results indicate that multi-angular hyperspectral data provides only a very small amount of additional information when used for conventional analyses.
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Conference papers on the topic "CHRIS hyperspectral"

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Gómez-Chova, Luis, Julia Amorós, Gustavo Camps-Valls, José D. Martin, Javier Calpe, Luis Alonso, Luis Guanter, Juan C. Fortea, and José Moreno. "Cloud detection for CHRIS/Proba hyperspectral images." In Remote Sensing, edited by Klaus Schäfer, Adolfo Comerón, James R. Slusser, Richard H. Picard, Michel R. Carleer, and Nicolaos I. Sifakis. SPIE, 2005. http://dx.doi.org/10.1117/12.627704.

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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.

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Lavender, Samantha, Giuseppe Ottavianelli, Mike Cutter, Roberto Biasutti, Clement Albinet, and Amy Beaton. "CHRIS/PROBA-1 Radiometric Calibration Assessment." In 2021 11th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing (WHISPERS). IEEE, 2021. http://dx.doi.org/10.1109/whispers52202.2021.9483995.

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Alonso, L., L. Gomez-Chova, J. Moreno, L. Guanter, C. Brockmann, N. Fomferra, R. Quast, and P. Regner. "CHRIS/Proba Toolbox for hyperspectral and multiangular data exploitations." In 2009 IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2009). IEEE, 2009. http://dx.doi.org/10.1109/igarss.2009.5418041.

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Raval, S., R. N. Merton, and D. Laurence. "Mine tailings water mapping using CHRIS Proba imagery." In 2012 4th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS). IEEE, 2012. http://dx.doi.org/10.1109/whispers.2012.6874298.

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Del Frate, F., R. Duca, and D. Solimini. "Urban features retrieved by hyperspectral multi-angle CHRIS Proba images." In 2007 Urban Remote Sensing Joint Event. IEEE, 2007. http://dx.doi.org/10.1109/urs.2007.371816.

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Menenti, Massimo, Fabio Maselli, Marta Chiesi, Riccardo Benedetti, Simone Cristofori, Donatella Guzzi, Federico Magnani, Sabrina Raddi, and Carmine Maffei. "Multi-angular hyperspectral observations of Mediterranean forest with PROBA-CHRIS." In Optical Science and Technology, the SPIE 49th Annual Meeting, edited by Sylvia S. Shen and Paul E. Lewis. SPIE, 2004. http://dx.doi.org/10.1117/12.559348.

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Mannheim, Sandra, Karl Segel, Birgit Heim, and Hermann Kaufmann. "Monitoring of trophic parameter Chl-a using hyperspectral CHRIS-PROBA data." In Optical Science and Technology, the SPIE 49th Annual Meeting, edited by Sylvia S. Shen and Paul E. Lewis. SPIE, 2004. http://dx.doi.org/10.1117/12.556347.

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Bach, Heike, and Silke Begiebing. "Analyses of hyperspectral directional data from CHRIS/PROBA using land surface models." In 2007 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2007. http://dx.doi.org/10.1109/igarss.2007.4423391.

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Gómez-Chova, Luis, Luis Alonso, Luis Guanter, Gustavo Camps-Valls, Javier Calpe, and José Moreno. "Modelling spatial and spectral systematic noise patterns on CHRIS/PROBA hyperspectral data." In Remote Sensing, edited by Lorenzo Bruzzone. SPIE, 2006. http://dx.doi.org/10.1117/12.690033.

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