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Automatic shadow detection in aerial and terrestrial images / Vander Luis de Souza Freitas in Boletim de Ciências Geodésicas, vol 23 n° 4 (oct - dec 2017)
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Titre : Automatic shadow detection in aerial and terrestrial images Type de document : Article/Communication Auteurs : Vander Luis de Souza Freitas, Auteur ; Barbara Maximino da Fonseca Reis, Auteur ; Antonio Maria Garcia Tommaselli, Auteur Année de publication : 2017 Article en page(s) : pp 578 - 590 Note générale : bibliographie Langues : Portugais (por) Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] détection d'ombre
[Termes IGN] image aérienne
[Termes IGN] image terrestreRésumé : (auteur) Shadows exist in almost all aerial and outdoor images, and they can be useful for estimating Sun position estimation or measuring object size. On the other hand, they represent a problem in processes such as object detection/recognition, image matching, etc., because they may be confused with dark objects and change the image radiometric properties. We address this problem on aerial and outdoor color images in this work. We use a filter to find low intensities as a first step. For outdoor color images, we analyze spectrum ratio properties to refine the detection, and the results are assessed with a dataset containing ground truth. For the aerial case we validate the detections depending of the hue component of pixels. This stage takes into account that, in deep shadows, most pixels have blue or violet wavelengths because of an atmospheric scattering effect. Numéro de notice : A2017-757 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1590/S1982-21702017000400038 En ligne : http://dx.doi.org/10.1590/S1982-21702017000400038 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=89112
in Boletim de Ciências Geodésicas > vol 23 n° 4 (oct - dec 2017) . - pp 578 - 590[article]Documents numériques
en open access
Automatic shadow detection ... - pdf éditeurAdobe Acrobat PDFA geometric correspondence feature based-mismatch removal in vision based-mapping and navigation / Zeyu Li in Photogrammetric Engineering & Remote Sensing, PERS, vol 83 n° 10 (October 2017)
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Titre : A geometric correspondence feature based-mismatch removal in vision based-mapping and navigation Type de document : Article/Communication Auteurs : Zeyu Li, Auteur ; Jinling Wang, Auteur ; Charles Toth, Auteur Année de publication : 2017 Article en page(s) : pp 693 - 704 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse de groupement
[Termes IGN] appariement de données localisées
[Termes IGN] attribut géomètrique
[Termes IGN] erreur de positionnement
[Termes IGN] regroupement de données
[Termes IGN] vision par ordinateurRésumé : (auteur) Images with large-area repetitive texture, significant viewpoint, and illumination changes as well as occlusions often induce high-percentage keypoint mismatches, affecting the performance of vision-based mapping and navigation. Traditional methods for mismatch elimination tend to fail when the percentage of mismatches is high. In order to remove mismatches effectively, a new geometry-based approach is proposed in this paper, where Geometric Correspondence Feature (GCF) is used to represent the tentative correspondence. Based on the clustering property of GCFs from correct matches, a new clustering algorithm is developed to identify the cluster formed by the correct matches.
With the defined quality factor calculated from the identified cluster, a Progressive Sample Consensus (PROSAC) process integrated with hyperplane-model is employed to further eliminate mismatches. Extensive experiments based on both simulated and real images in indoor and outdoor environments have demonstrated that the proposed approach can significantly improve the performance of mismatch elimination in the presence of high-percentage mismatches.Numéro de notice : A2017-690 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Article DOI : 10.14358/PERS.83.10.693 En ligne : https://doi.org/10.14358/PERS.83.10.693 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=87856
in Photogrammetric Engineering & Remote Sensing, PERS > vol 83 n° 10 (October 2017) . - pp 693 - 704[article]Hyperspectral dimensionality reduction for biophysical variable statistical retrieval / Juan Pablo Rivera-Caicedo in ISPRS Journal of photogrammetry and remote sensing, vol 132 (October 2017)
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Titre : Hyperspectral dimensionality reduction for biophysical variable statistical retrieval Type de document : Article/Communication Auteurs : Juan Pablo Rivera-Caicedo, Auteur ; Jochem Verrelst, Auteur ; Jordi Munoz-Mari, Auteur ; et al., Auteur Année de publication : 2017 Article en page(s) : pp 88 - 101 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] image HYMAP
[Termes IGN] image hyperspectrale
[Termes IGN] Leaf Area Index
[Termes IGN] régression linéaire
[Termes IGN] variable biophysique (végétation)Résumé : (Auteur) Current and upcoming airborne and spaceborne imaging spectrometers lead to vast hyperspectral data streams. This scenario calls for automated and optimized spectral dimensionality reduction techniques to enable fast and efficient hyperspectral data processing, such as inferring vegetation properties. In preparation of next generation biophysical variable retrieval methods applicable to hyperspectral data, we present the evaluation of 11 dimensionality reduction (DR) methods in combination with advanced machine learning regression algorithms (MLRAs) for statistical variable retrieval. Two unique hyperspectral datasets were analyzed on the predictive power of DR + MLRA methods to retrieve leaf area index (LAI): (1) a simulated PROSAIL reflectance data (2101 bands), and (2) a field dataset from airborne HyMap data (125 bands). For the majority of MLRAs, applying first a DR method leads to superior retrieval accuracies and substantial gains in processing speed as opposed to using all bands into the regression algorithm. This was especially noticeable for the PROSAIL dataset: in the most extreme case, using the classical linear regression (LR), validation results (RMSECV) improved from 0.06 (12.23) without a DR method to 0.93 (0.53) when combining it with a best performing DR method (i.e., CCA or OPLS). However, these DR methods no longer excelled when applied to noisy or real sensor data such as HyMap. Then the combination of kernel CCA (KCCA) with LR, or a classical PCA and PLS with a MLRA showed more robust performances ( of 0.93). Gaussian processes regression (GPR) uncertainty estimates revealed that LAI maps as trained in combination with a DR method can lead to lower uncertainties, as opposed to using all HyMap bands. The obtained results demonstrated that, in general, biophysical variable retrieval from hyperspectral data can largely benefit from dimensionality reduction in both accuracy and computational efficiency. Numéro de notice : A2017-640 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2017.08.012 En ligne : https://doi.org/10.1016/j.isprsjprs.2017.08.012 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=86995
in ISPRS Journal of photogrammetry and remote sensing > vol 132 (October 2017) . - pp 88 - 101[article]Réservation
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Titre : Atmospheric correction over coastal waters using multilayer neural networks Type de document : Article/Communication Auteurs : Yongzhen Fan, Auteur ; Wei Li, Auteur ; Charles K. Gatebe, Auteur ; Cédric Jamet, Auteur ; et al., Auteur Année de publication : 2017 Article en page(s) : pp 218 - 240 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] correction atmosphérique
[Termes IGN] couleur de l'océan
[Termes IGN] eaux côtières
[Termes IGN] image Aqua-MODIS
[Termes IGN] Perceptron multicouche
[Termes IGN] transfert radiatifRésumé : (auteur) Standard atmospheric correction (AC) algorithms work well in open ocean areas where the water inherent optical properties (IOPs) are correlated with pigmented particles. However, the IOPs of turbid coastal waters may independently vary with pigmented particles, suspended inorganic particles, and colored dissolved organic matter (CDOM). In turbid coastal waters standard AC algorithms often exhibit large inaccuracies that may lead to negative water-leaving radiances (Lw) or remote sensing reflectance (Rrs). We introduce a new atmospheric correction algorithm for coastal waters based on a multilayer neural network (MLNN) method. We use a coupled atmosphere-ocean radiative transfer model to simulate the Rayleigh-corrected radiance (Lrc) at the top of the atmosphere (TOA) and the Rrs just above the surface simultaneously, and train a MLNN to derive the aerosol optical depth (AOD) and Rrs directly from the TOA Lrc. The method is validated using both a synthetic dataset and Aerosol Robotic Network – Ocean Color (AERONET–OC) measurements. The SeaDAS NIR algorithm, the SeaDAS NIR/SWIR algorithm, and the MODIS version of the Case 2 regional water - CoastColour (C2RCC) algorithm are also included in the comparison with AERONET–OC measurements. The performance of the AC algorithms is evaluated with four statistical metrics: the Pearson correlation coefficient (R), the average percentage difference (APD), the mean percentage bias, and the root mean square difference (RMSD). The comparison with AERONET–OC measurements shows that the MLNN algorithm significantly improves retrieval of normalized Lw in blue bands (412 nm and 443 nm) and yields minor improvements in green and red bands compared with the other three algorithms. On a global scale, the MLNN algorithm reduces APD in normalized Lw by up to 13% in blue bands and by 2–7% in green and red bands when compared with the standard SeaDAS NIR algorithm. In highly absorbing coastal waters, such as the Baltic Sea, the MLNN algorithm reduces APD in normalized Lw by more than 60% in blue bands compared to the standard SeaDAS NIR algorithm, while in highly scattering coastal waters, such as the Black Sea, the MLNN algorithm reduces APD by more than 25%. These results indicate that the MLNN algorithm is suitable for application in turbid coastal waters. Application of the MLNN algorithm to MODIS Aqua images in several coastal areas also shows that it is robust and resilient to contamination due to sunglint or adjacency effects of land and cloud edges. The MLNN algorithm is very fast once the neural network has been properly trained and is therefore suitable for operational use. A significant advantage of the MLNN algorithm is that it does not need SWIR bands. Numéro de notice : A2017-417 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.rse.2017.07.016 En ligne : https://doi.org/10.1016/j.rse.2017.07.016 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=86310
in Remote sensing of environment > vol 199 (15 September 2017) . - pp 218 - 240[article]Examination of Sentinel-2A multi-spectral instrument (MSI) reflectance anisotropy and the suitability of a general method to normalize MSI reflectance to nadir BRDF adjusted reflectance / David P. Roy in Remote sensing of environment, vol 199 (15 September 2017)
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Titre : Examination of Sentinel-2A multi-spectral instrument (MSI) reflectance anisotropy and the suitability of a general method to normalize MSI reflectance to nadir BRDF adjusted reflectance Type de document : Article/Communication Auteurs : David P. Roy, Auteur ; Jian Li, Auteur ; Hankui K. Zhang, Auteur ; Lin Yan, Auteur ; et al., Auteur Année de publication : 2017 Article en page(s) : pp 25 - 38 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] anisotropie
[Termes IGN] distribution du coefficient de réflexion bidirectionnelle BRDF
[Termes IGN] image Landsat
[Termes IGN] image Sentinel-MSI
[Termes IGN] image Terra-MODIS
[Termes IGN] réflectance
[Termes IGN] Sentinel-2Résumé : (auteur) The Sentinel-2A multi-spectral instrument (MSI) acquires multi-spectral reflective wavelength observations with directional effects due to surface reflectance anisotropy and changes in the solar and viewing geometry. Directional effects were examined by considering two ten day periods of Sentinel-2A data acquired close to the solar principal and orthogonal planes over approximately 20° × 10° of southern Africa. More than 6.6 million (January 2016) and 10.6 million (April 2016) pairs of reflectance observations sensed 3 or 7 days apart in the forward and backscatter directions in overlapping Sentinel-2A orbit swaths were considered. The Sentinel-2A data were projected into the MODIS sinusoidal projection but first had to be registered due to a misregistration issue evident in the overlapping orbits. The top of atmosphere reflectance data were corrected to surface reflectance using the SEN2COR atmospheric correction software. Only pairs of forward and backward reflectance values that were cloud and snow-free, unsaturated, and had no significant change in their 3 or 7 day separation, were considered. The maximum observed Sentinel-2A view zenith angle was 11.93°. Greater BRDF effects were apparent in the January data (acquired close to the solar principal plane) than the April data (acquired close to the orthogonal plane) and at higher view zenith angle. For the January data the average difference between the surface reflectance in the forward and backward scatter directions at the Sentinel-2A scan edges increased with wavelength from 0.035 (blue), 0.047 (green), 0.057 (red), 0.078 (NIR), to about 0.1 (SWIR). These differences may constitute a significant source of noise for certain applications.
The suitability of a recently published methodology developed to generate Landsat nadir BRDF-adjusted reflectance (NBAR) was examined for Sentinel-2A application. The methodology uses fixed MODIS BRDF spectral parameters and is attractive because it has little sensitivity to the land cover type, condition, or surface disturbance and can be derived in a computationally efficient manner globally. It was applied to the southern Africa Sentinel-2A data and shown to reduce Sentinel-2A BRDF effects. The average difference between the reflectance in the forward and backward scatter directions at the Sentinel-2A scan edges was smaller in the NBAR data than in the corresponding surface reflectance data. Residual BRDF effects in the Sentinel-2A NBAR data occurred likely because of atmospheric correction and sensor calibration errors and inadequacies in the NBAR derivation approach. These issues are discussed with recommendations for future research including global and red-edge Sentinel-2A NBAR derivation that were not considered in this study.Numéro de notice : A2017-416 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.rse.2017.06.019 En ligne : https://doi.org/10.1016/j.rse.2017.06.019 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=86309
in Remote sensing of environment > vol 199 (15 September 2017) . - pp 25 - 38[article]Understanding the temporal behavior of crops using Sentinel-1 and Sentinel-2-like data for agricultural applications / Amanda Veloso in Remote sensing of environment, vol 199 (15 September 2017)
PermalinkBand subset selection for anomaly detection in hyperspectral imagery / Lin Wang in IEEE Transactions on geoscience and remote sensing, vol 55 n° 9 (September 2017)
PermalinkDenoising of natural images through robust wavelet thresholding and genetic programming / Asem Khmag in The Visual Computer, vol 33 n°9 (September 2017)
PermalinkA Geometric and Radiometric Simultaneous Correction Model (GRSCM) framework for high-accuracy remotely sensed image preprocessing / Chang Li in Photogrammetric Engineering & Remote Sensing, PERS, vol 83 n° 9 (September 2017)
PermalinkMultiple cues-based active contours for target contour tracking under sophisticated background / Peng Lv in The Visual Computer, vol 33 n°9 (September 2017)
PermalinkA new GPU bundle adjustment method for large-scale data / Zhou Shunping ; Xiong Xiaodong ; Junfeng Zhu in Photogrammetric Engineering & Remote Sensing, PERS, vol 83 n° 9 (September 2017)
PermalinkReconstruction of time-varying tidal flat topography using optical remote sensing imageries / Kuo-Hsin Tseng in ISPRS Journal of photogrammetry and remote sensing, vol 131 (September 2017)
PermalinkRecurrent neural networks to correct satellite image classification maps / Emmanuel Maggiori in IEEE Transactions on geoscience and remote sensing, vol 55 n° 9 (September 2017)
PermalinkRemote sensing scene classification by unsupervised representation learning / Xiaoqiang Lu in IEEE Transactions on geoscience and remote sensing, vol 55 n° 9 (September 2017)
PermalinkSDE: A novel selective, discriminative and equalizing feature representation for visual recognition / Guo-Sen Xie in International journal of computer vision, vol 124 n° 2 (1 September 2017)
PermalinkA Stepwise-Then-Orthogonal Regression (STOR) with quality control for optimizing the RFM of high-resolution satellite imagery / Chang Li in Photogrammetric Engineering & Remote Sensing, PERS, vol 83 n° 9 (September 2017)
PermalinkUsing 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)
PermalinkColour Helmholtz stereopsis for reconstruction of dynamic scenes with arbitrary unknown reflectance / Nadejda Roubtsova in International journal of computer vision, vol 124 n° 1 (August 2017)
PermalinkFrom subpixel to superpixel : a novel fusion framework for hyperspectral image classification / Ting Lu in IEEE Transactions on geoscience and remote sensing, vol 55 n° 8 (August 2017)
PermalinkA graph-based approach to detect spatiotemporal dynamics in satellite image time series / Fabio Guttler in ISPRS Journal of photogrammetry and remote sensing, vol 130 (August 2017)
PermalinkLearning and transferring deep joint spectral–spatial features for hyperspectral classification / Jingxiang Yang in IEEE Transactions on geoscience and remote sensing, vol 55 n° 8 (August 2017)
PermalinkLearning a discriminative distance metric with label consistency for scene classification / Yuebin Wang in IEEE Transactions on geoscience and remote sensing, vol 55 n° 8 (August 2017)
PermalinkLearning sensor-specific spatial-spectral features of hyperspectral images via convolutional neural networks / Shaohui Mei in IEEE Transactions on geoscience and remote sensing, vol 55 n° 8 (August 2017)
PermalinkLocal and global evaluation for remote sensing image segmentation / Tengfei Su in ISPRS Journal of photogrammetry and remote sensing, vol 130 (August 2017)
PermalinkMorphologically decoupled structured sparsity for rotation-invariant hyperspectral image analysis / Saurabh Prasad in IEEE Transactions on geoscience and remote sensing, vol 55 n° 8 (August 2017)
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