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Polarization of light reflected by grass: modeling using visible-sunlit areas / Bin Yang in Photogrammetric Engineering & Remote Sensing, PERS, vol 86 n° 12 (December 2020)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 86 n° 12 (December 2020) . - pp 745 - 752 Titre : Polarization of light reflected by grass: modeling using visible-sunlit areas Type de document : Article/Communication Auteurs : Bin Yang, Auteur ; Lei Yan, Auteur ; Siyuan Liu, Auteur Année de publication : 2020 Article en page(s) : pp 745 - 752 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes descripteurs IGN] aérosol [Termes descripteurs IGN] canopée [Termes descripteurs IGN] distribution du coefficient de réflexion bidirectionnelle BRDF [Termes descripteurs IGN] ensoleillement [Termes descripteurs IGN] image POLDER [Termes descripteurs IGN] image Terra-MODIS [Termes descripteurs IGN] Leaf Area Index [Termes descripteurs IGN] polarisation [Termes descripteurs IGN] réflectance de surface [Termes descripteurs IGN] réflectance végétale Résumé : (Auteur) The Bidirectional polarization distribution function (BPDF) of land surfaces is important for studies of land surfaces and aerosol. With the availability of a huge number of polarization measurements, several semi-empirical BPDF models have been proposed. However, these models do not pay much attention to canopy structure, which is fundamental for generation of polarization. In this article, we propose a new BPDF model using canopy structure information, which is parameterized by visible-sunlit areas. It is evaluated over grassland using POLDER BPDF and MODIS leaf area index data sets. Experiments suggest that compared to Nadal–Bréon and Litvinov models, the new BPDF model reduces root-mean-square error by 7% and 10%, respectively. The new BPDF model also provides better performance when it is fitted using observations clustered by sun zenith angle. The new BPDF model thus provides an effective tool for the study of land surface polarization. Numéro de notice : A2020-763 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.86.12.745 date de publication en ligne : 01/12/2020 En ligne : https://doi.org/10.14358/PERS.86.12.745 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96552 [article]

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Complete and accurate data correction for seamless mosaicking of airborne hyperspectral images: A case study at a mining site in Inner Mongolia, China / Kun Tan in ISPRS Journal of photogrammetry and remote sensing, vol 165 (July 2020)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 165 (July 2020) . - pp 1 - 15 Titre : Complete and accurate data correction for seamless mosaicking of airborne hyperspectral images: A case study at a mining site in Inner Mongolia, China Type de document : Article/Communication Auteurs : Kun Tan, Auteur ; Chao Niu, Auteur ; Xiuping Jia, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 1 - 15 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes descripteurs IGN] atténuation [Termes descripteurs IGN] correction atmosphérique [Termes descripteurs IGN] distorsion du signal [Termes descripteurs IGN] distribution du coefficient de réflexion bidirectionnelle BRDF [Termes descripteurs IGN] image hyperspectrale [Termes descripteurs IGN] mine [Termes descripteurs IGN] Mongolie intérieure (Chine) [Termes descripteurs IGN] mosaïquage d'images [Termes descripteurs IGN] radiance [Termes descripteurs IGN] rayonnement infrarouge [Termes descripteurs IGN] réflectance de surface [Termes descripteurs IGN] Short Waves InfraRed [Termes descripteurs IGN] spectroradiométrie [Termes descripteurs IGN] surveillance écologique Résumé : (auteur) Airborne hyperspectral remote sensing is an important application in the ecological monitoring of the environment in mining areas, and accurate preprocessing of the original images is the key to quantitative information retrieval. The original image data need radiation correction to acquire surface reflectance data. Due to the impact of the field angle, incidental radiance, and the bidirectional reflectance distribution function (BRDF), there can be a brightness gradient between adjacent strips, which leads to radiance difference and obvious chromatic aberration of the mosaicked images. We propose a novel data correction method for seamless mosaicking of airborne hyperspectral images. Firstly, visible and near-infrared (VNIR) and shortwave infrared (SWIR) sensors are calibrated in the laboratory, and the radiation calibration model of the sensor is established by an integrating-sphere system. A correction function is then established by combining the BRDF effect and the radiation attenuation coefficients. We also normalize the exposure time, sun altitude angle, and sensor altitude angle according to the flight strip. The results showed that this method is able to eliminate the signal distortion, allowing the seamless mosaicking of 37 strip images which were taken in different date and conditions in the study area. After the atmospheric correction of the imagery was completed, the accuracy of the preprocessing results was evaluated by field-measured ASD spectroradiometer data. The coefficient of determination R2 of the results for the reflectance was greater than 0.9. The experiments show that the proposed method has a good performance in radiation accuracy, and can provide high-quality hyperspectral data for the follow-up application of the ecological monitoring of a mining area. Numéro de notice : A2020-465 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2020.04.022 date de publication en ligne : 16/05/2020 En ligne : https://doi.org/10.1016/j.isprsjprs.2020.04.022 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95092 [article]

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A hybrid training approach for leaf area index estimation via Cubist and random forests machine-learning / Rasmus M. Houborg in ISPRS Journal of photogrammetry and remote sensing, vol 135 (January 2018)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 135 (January 2018) . - pp 173 - 188 Titre : A hybrid training approach for leaf area index estimation via Cubist and random forests machine-learning Type de document : Article/Communication Auteurs : Rasmus M. Houborg, Auteur ; Matthew F. McCabe, Auteur Année de publication : 2018 Article en page(s) : pp 173 - 188 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes descripteurs IGN] classification par forêts aléatoires [Termes descripteurs IGN] CubiST algorithm [Termes descripteurs IGN] image RapidEye [Termes descripteurs IGN] Leaf Area Index [Termes descripteurs IGN] réflectance de surface [Termes descripteurs IGN] régression Résumé : (Auteur) With an increasing volume and dimensionality of Earth observation data, enhanced integration of machine-learning methodologies is needed to effectively analyze and utilize these information rich datasets. In machine-learning, a training dataset is required to establish explicit associations between a suite of explanatory ‘predictor’ variables and the target property. The specifics of this learning process can significantly influence model validity and portability, with a higher generalization level expected with an increasing number of observable conditions being reflected in the training dataset. Here we propose a hybrid training approach for leaf area index (LAI) estimation, which harnesses synergistic attributes of scattered in-situ measurements and systematically distributed physically based model inversion results to enhance the information content and spatial representativeness of the training data. To do this, a complimentary training dataset of independent LAI was derived from a regularized model inversion of RapidEye surface reflectances and subsequently used to guide the development of LAI regression models via Cubist and random forests (RF) decision tree methods. The application of the hybrid training approach to a broad set of Landsat 8 vegetation index (VI) predictor variables resulted in significantly improved LAI prediction accuracies and spatial consistencies, relative to results relying on in-situ measurements alone for model training. In comparing the prediction capacity and portability of the two machine-learning algorithms, a pair of relatively simple multi-variate regression models established by Cubist performed best, with an overall relative mean absolute deviation (rMAD) of ∼11%, determined based on a stringent scene-specific cross-validation approach. In comparison, the portability of RF regression models was less effective (i.e., an overall rMAD of ∼15%), which was attributed partly to model saturation at high LAI in association with inherent extrapolation and transferability limitations. Explanatory VIs formed from bands in the near-infrared (NIR) and shortwave infrared domains (e.g., NDWI) were associated with the highest predictive ability, whereas Cubist models relying entirely on VIs based on NIR and red band combinations (e.g., NDVI) were associated with comparatively high uncertainties (i.e., rMAD ∼ 21%). The most transferable and best performing models were based on combinations of several predictor variables, which included both NDWI- and NDVI-like variables. In this process, prior screening of input VIs based on an assessment of variable relevance served as an effective mechanism for optimizing prediction accuracies from both Cubist and RF. While this study demonstrated benefit in combining data mining operations with physically based constraints via a hybrid training approach, the concept of transferability and portability warrants further investigations in order to realize the full potential of emerging machine-learning techniques for regression purposes. Numéro de notice : A2018-070 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2017.10.004 En ligne : https://doi.org/10.1016/j.isprsjprs.2017.10.004 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=89428 [article]

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Forest change detection in incomplete satellite images with deep neural networks / Salman H. Khan in IEEE Transactions on geoscience and remote sensing, vol 55 n° 9 (September 2017)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 55 n° 9 (September 2017) . - pp 5407 - 5423 Titre : Forest change detection in incomplete satellite images with deep neural networks Type de document : Article/Communication Auteurs : Salman H. Khan, Auteur ; Xuming He, Auteur ; Fatih Porikli, Auteur ; Mohammed Bennamoun, Auteur Année de publication : 2017 Article en page(s) : pp 5407 - 5423 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes descripteurs IGN] analyse diachronique [Termes descripteurs IGN] analyse multirésolution [Termes descripteurs IGN] apprentissage profond [Termes descripteurs IGN] détection de changement [Termes descripteurs IGN] forêt [Termes descripteurs IGN] réflectance de surface [Termes descripteurs IGN] réseau neuronal artificiel [Termes descripteurs IGN] retouche [Termes descripteurs IGN] surveillance de la végétation Résumé : (Auteur) Land cover change monitoring is an important task from the perspective of regional resource monitoring, disaster management, land development, and environmental planning. In this paper, we analyze imagery data from remote sensing satellites to detect forest cover changes over a period of 29 years (1987-2015). Since the original data are severely incomplete and contaminated with artifacts, we first devise a spatiotemporal inpainting mechanism to recover the missing surface reflectance information. The spatial filling process makes use of the available data of the nearby temporal instances followed by a sparse encoding-based reconstruction. We formulate the change detection task as a region classification problem. We build a multiresolution profile (MRP) of the target area and generate a candidate set of bounding-box proposals that enclose potential change regions. In contrast to existing methods that use handcrafted features, we automatically learn region representations using a deep neural network in a data-driven fashion. Based on these highly discriminative representations, we determine forest changes and predict their onset and offset timings by labeling the candidate set of proposals. Our approach achieves the state-of-the-art average patch classification rate of 91.6% (an improvement of ~16%) and the mean onset/offset prediction error of 4.9 months (an error reduction of five months) compared with a strong baseline. We also qualitatively analyze the detected changes in the unlabeled image regions, which demonstrate that the proposed forest change detection approach is scalable to new regions. Numéro de notice : A2017-663 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2017.2707528 En ligne : http://dx.doi.org/10.1109/TGRS.2017.2707528 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=87105 [article]

Using landsat surface reflectance data as a reference target for multiswath hyperspectral data collected over mixed agricultural rangeland areas / Cooper McCann in IEEE Transactions on geoscience and remote sensing, vol 55 n° 9 (September 2017)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 55 n° 9 (September 2017) . - pp 5002 - 5014 Titre : Using landsat surface reflectance data as a reference target for multiswath hyperspectral data collected over mixed agricultural rangeland areas Type de document : Article/Communication Auteurs : Cooper McCann, Auteur ; Kevin S. Repasky, Auteur ; Mikindra Morin, Auteur ; et al., Auteur Année de publication : 2017 Article en page(s) : pp 5002 - 5014 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes descripteurs IGN] agriculture [Termes descripteurs IGN] image hyperspectrale [Termes descripteurs IGN] image Landsat [Termes descripteurs IGN] image multibande [Termes descripteurs IGN] mosaïquage d'images [Termes descripteurs IGN] paturage [Termes descripteurs IGN] qualité radiométrique (image) [Termes descripteurs IGN] réflectance de surface Résumé : (Auteur) Low-cost flight-based hyperspectral imaging systems have the potential to provide important information for ecosystem and environmental studies as well as aide in land management. To realize this potential, methods must be developed to provide large-area surface reflectance data allowing for temporal data sets at the mesoscale. This paper describes a bootstrap method of producing a large-area, radiometrically referenced hyperspectral data set using the Landsat surface reflectance (LaSRC) data product as a reference target. The bootstrap method uses standard hyperspectral processing techniques that are extended to remove uneven illumination conditions between flight passes, allowing for radiometrically self-consistent data after mosaicking. Through selective spectral and spatial resampling, LaSRC data are used as a radiometric reference target. Advantages of the bootstrap method include the need for minimal site access, no ancillary instrumentation, and automated data processing. Data from two hyperspectral flights over the same managed agricultural and unmanaged range land covering approximately 5.8 km2 acquired on June 21, 2014 and June 24, 2015 are presented. Data from a flight over agricultural land collected on June 6, 2016 are compared with concurrently collected ground-based reflectance spectra as a means of validation. Numéro de notice : A2017-665 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2017.2699618 En ligne : http://dx.doi.org/10.1109/TGRS.2017.2699618 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=87102 [article]

Angular reflectance of leaves with a dual-wavelength terrestrial lidar and its implications for leaf-bark separation and leaf moisture estimation / Steven Hancock in IEEE Transactions on geoscience and remote sensing, vol 55 n° 6 (June 2017)  

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Spatiotemporal downscaling approaches for monitoring 8-day 30 m actual evapotranspiration / Yinghai Ke in ISPRS Journal of photogrammetry and remote sensing, vol 126 (April 2017)  

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Hierarchically exploring the width of spectral bands for urban material classification / Arnaud Le Bris (2017)  

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Automatic detection of clouds and shadows using high resolution satellite image time series / Nicolas Champion (2016)  

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Measuring the directional variations of land surface reflectance from MODIS / François-Marie Bréon in IEEE Transactions on geoscience and remote sensing, vol 53 n° 8 (August 2015)

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Levelling co-located GNSS and tide gauge stations using GNSS reflectometry / Alvaro Santamaria Gomez in Journal of geodesy, vol 89 n° 3 (March 2015)  

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Seeing through shadow: Modelling surface irradiance for topographic correction of Landsat ETM+ data / Tobias Schulmann in ISPRS Journal of photogrammetry and remote sensing, vol 99 (January 2015)  

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Limnimétrie par réflectrométrie GNSS à faible coût / Eduardo Rodrigues in Géomatique suisse, vol 112 n° 8 (août 2014)  

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An improved dark object method to retrieve 500 m-resolution AOT (Aerosol Optical Thickness) image from MODIS data: A case study in the Pearl River Delta area, China / Lili Li in ISPRS Journal of photogrammetry and remote sensing, vol 89 (March 2014)

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Directional relations and frames of reference / Eliseo Clementini in Geoinformatica, vol 17 n° 2 (April 2013)  

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