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Retrieval of ultraviolet diffuse attenuation coefficients from ocean color using the kernel principal components analysis over ocean / Kunpeng Sun in IEEE Transactions on geoscience and remote sensing, vol 59 n° 6 (June 2021)
[article]
Titre : Retrieval of ultraviolet diffuse attenuation coefficients from ocean color using the kernel principal components analysis over ocean Type de document : Article/Communication Auteurs : Kunpeng Sun, Auteur ; Tinglu Zhang, Auteur ; Shuguo Chen, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 4579 - 4589 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse en composantes principales
[Termes IGN] atténuation
[Termes IGN] couleur de l'océan
[Termes IGN] image Aqua-MODIS
[Termes IGN] image NPP-VIIRS
[Termes IGN] méthode fondée sur le noyau
[Termes IGN] rayonnement ultraviolet
[Termes IGN] régression multipleRésumé : (auteur) Underwater ultraviolet radiation (UVR), which plays a significant role in photobiological and photochemical processes, is one of the key factors in marine ecosystems. A new algorithm KpcaUV, based on kernel principal component analysis (KPCA) and multiple linear regression (MLR), was proposed in this study for the retrieval of the UVR diffuse attenuation coefficient Kd(λ) from remote sensing reflectance Rrs(λ) in the global ocean. KPCA can be applied in all areas that principal components analysis (PCA) can be used. More importantly, KPCA can help mapping data into high dimensions and reducing the nonlinearity between inputs and outputs, which will improve the performance and robustness of algorithms when deriving large dynamic ranges parameters. Compared with SeaUVc, which is one of the most successful Kd(λ) retrieval algorithms in UVR, the results showed that KpcaUV (with R2 : 0.970 and RMSE: 14.0%) performed similar to SeaUVc (with R2 : 0.963 and RMSE: 15.6%) when implemented with high-quality data. Nevertheless, KpcaUV was more robust and consistent than SeaUVc when implemented on the satellite images with different levels of quality control. The RMSD of SeaUVc had a significant reduction from 26.8% (QA ≥ 0.6) to 12.7% (QA = 1.0), and the RMSD of KpcaUV varied less than SeaUVc from 14.6% (QA ≥ 0.6) to 10.1% (QA = 1). Hence, considering its good nonlinear-problem-solving ability and robustness when applied to multiple satellites, KpcaUV proposed by this study can be used to obtain Kd(380) for the continuous observation of the large area. Numéro de notice : A2021-421 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2020.3020294 Date de publication en ligne : 16/09/2020 En ligne : https://doi.org/10.1109/TGRS.2020.3020294 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97773
in IEEE Transactions on geoscience and remote sensing > vol 59 n° 6 (June 2021) . - pp 4579 - 4589[article]Aboveground biomass estimates of tropical mangrove forest using Sentinel-1 SAR coherence data : The superiority of deep learning over a semi-empirical model / S.M. Ghosh in Computers & geosciences, vol 150 (May 2021)
[article]
Titre : Aboveground biomass estimates of tropical mangrove forest using Sentinel-1 SAR coherence data : The superiority of deep learning over a semi-empirical model Type de document : Article/Communication Auteurs : S.M. Ghosh, Auteur ; M.D. Behera, Auteur Année de publication : 2021 Article en page(s) : n° 104737 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications
[Termes IGN] bande C
[Termes IGN] biomasse aérienne
[Termes IGN] classification par réseau neuronal
[Termes IGN] forêt tropicale
[Termes IGN] image Sentinel-SAR
[Termes IGN] Inde
[Termes IGN] mangrove
[Termes IGN] R (langage)Résumé : (auteur) The availability of advanced Machine Learning algorithms has made the estimation process of biophysical parameters more efficient. However, the efficiency of those methods seldom compared with the efficiency of already established semi-empirical procedures. Aboveground biomass (AGB) of mangrove forests is a crucial biophysical parameter as it is positively correlated to the carbon stocks and fluxes. The free availability of Sentinel-1 C-band SAR data and machine learning algorithms hold promises in estimating AGB of tropical mangrove forests. We reported high AGB (70 t/ha to 666 t/ha) using 185 field quadrats of 0.04ha each from Bhitarkanika Wildlife Sanctuary, located on the eastern Indian coast that could be attributed to species composition. The AGB maps generated using Interferometric Water Cloud Model (IWCM) and Deep Learning models were different from each other as they rely on different variables. IWCM was more dependent, especially on ground and vegetation components of coherence, while canopy height acted as the most crucial variable in the Deep Learning model. However, the negligible variations in Deep Learning-based AGB maps can be attributed to interpreting the importance of coherence and VH backscatter. Due to low canopy penetration power of C-band SAR, high temporal decorrelation resulting from longer time gap between interferometric image pairs, and high spatial heterogeneity of mangrove forests, IWCM found as an unsuitable method for AGB estimation. Interestingly, a Deep Learning algorithm could translate the exact relationship between predictor variables and mangrove AGB in Bhitarkanika Wildlife Sanctuary. The AGB estimation studies in mangrove forests using Sentinel data should focus more on using machine learning algorithms like Deep Learning rather than semi-empirical models. Numéro de notice : A2021-941 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.1016/j.cageo.2021.104737 En ligne : https://doi.org/10.1016/j.cageo.2021.104737 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99751
in Computers & geosciences > vol 150 (May 2021) . - n° 104737[article]Automatic detection and classification of low-level orographic precipitation processes from space-borne radars using machine learning / Malarvizhi Arulraj in Remote sensing of environment, vol 257 (May 2021)
[article]
Titre : Automatic detection and classification of low-level orographic precipitation processes from space-borne radars using machine learning Type de document : Article/Communication Auteurs : Malarvizhi Arulraj, Auteur ; Ana P. Baros, Auteur Année de publication : 2021 Article en page(s) : n° 112355 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications
[Termes IGN] Appalaches
[Termes IGN] apprentissage automatique
[Termes IGN] bande S
[Termes IGN] classification automatique
[Termes IGN] classification barycentrique
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] image GPM
[Termes IGN] orographie
[Termes IGN] précipitationRésumé : (auteur) Ground-clutter is a significant cause of missed-detection and underestimation of precipitation in complex terrain from space-based radars such as the Global Precipitation Measurement Mission (GPM) Dual-frequency Precipitation Radar (DPR). This research proposes an Artificial Intelligence (AI) framework consisting of a precipitation detection model (PDM) and a precipitation regime classification model (PCM) to improve orographic precipitation retrievals from GPM-DPR using machine learning. The PDM is a Random Forest Classifier using GPM Microwave Imager (GMI) calibrated brightness temperatures (Tbs) and low-level precipitation mixing ratios from the High-Resolution Rapid Refresh (HRRR) analysis as inputs. The PCM is a Convolutional Neural Network that predicts the precipitation regime class, defined independently based on quantitative features of ground-based radar reflectivity profiles, using GPM DPR Ku-band (Ku-PR) reflectivity profiles and GMI Tbs. The AI framework is demonstrated for warm-season precipitation in the Southern Appalachian Mountains over. Numéro de notice : A2021-279 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1016/j.rse.2021.112355 Date de publication en ligne : 19/02/2021 En ligne : https://doi.org/10.1016/j.rse.2021.112355 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97372
in Remote sensing of environment > vol 257 (May 2021) . - n° 112355[article]Automatic filter coefficient calculation in lifting scheme wavelet transform for lossless image compression / Ignacio Hernández-Bautista in The Visual Computer, vol 37 n° 5 (May 2021)
[article]
Titre : Automatic filter coefficient calculation in lifting scheme wavelet transform for lossless image compression Type de document : Article/Communication Auteurs : Ignacio Hernández-Bautista, Auteur ; Jesús Ariel Carrasco-Ochoa, Auteur ; José Francisco Martínez-Trinidad, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 957 - 972 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse spectrale
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] compression d'image
[Termes IGN] décomposition spectrale
[Termes IGN] transformation en ondelettesRésumé : (auteur) In this paper, a new method for automatic filter coefficient calculation in lifting scheme wavelet transform for image lossless compression is proposed. Actually, there is no specific rule for setting filter coefficients (a, b). Therefore, this work proposes an automatic method to calculate the filter coefficients depending on the spectral analysis of each image. Also, filter coefficients are determined for five decomposition levels and for each quadrant through applying the discrete wavelet transform in the lossless image compression problem. Spectral patterns are computed and fixed into small length vectors for building different wavelet decomposition levels; these vectors are automatically computed using a 1-NN classifier. Experimental results over standard images show that calculating the wavelet filter coefficients using the proposed method generates higher compression rates (in entropy and bitstream values) against standard wavelet and linear prediction filters. Numéro de notice : A2021-398 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1007/s00371-020-01846-0 Date de publication en ligne : 24/04/2020 En ligne : https://doi.org/10.1007/s00371-020-01846-0 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97693
in The Visual Computer > vol 37 n° 5 (May 2021) . - pp 957 - 972[article]Evaluating P-Band TomoSAR for biomass retrieval in boreal forest / Erik Blomberg in IEEE Transactions on geoscience and remote sensing, vol 59 n° 5 (May 2021)
[article]
Titre : Evaluating P-Band TomoSAR for biomass retrieval in boreal forest Type de document : Article/Communication Auteurs : Erik Blomberg, Auteur ; Lars M.H. Ulander, Auteur ; Stefano Tebaldini, Auteur ; Laurent Ferro-Famil, Auteur Année de publication : 2021 Article en page(s) : pp 3793 - 3804 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications
[Termes IGN] bande P
[Termes IGN] biomasse forestière
[Termes IGN] forêt boréale
[Termes IGN] Suède
[Termes IGN] tomographie radarRésumé : (Auteur) P-band synthetic aperture radar (SAR) is sensitive to above-ground biomass (AGB) but retrieval accuracy has been shown to deteriorate in topographic areas. In boreal forest, the signal penetrates through the canopy to interact with the ground producing variations in backscatter depending on ground topography, forest structure, and soil moisture. Tomographic processing of multiple SAR images Tomographic SAR (TomoSAR) provides information about the vertical backscatter distribution. This article evaluates the use of P-band TomoSAR data to improve AGB retrievals from backscattered intensity by suppressing the backscattered signal from the ground. This approach can be used even when the tomographic resolution is insufficient to resolve the vertical backscatter profile. The analysis is based on P-band data from two campaigns: BioSAR-1 (2007) in Remingstorp, southern Sweden, and BioSAR-2 (2008) in Krycklan (KR), northern Sweden. BioSAR airborne data were also processed to correspond as closely as possible to future BIOMASS TomoSAR acquisitions, with BioSAR-2-based results shown. A power law AGB model using volumetric HV polarized backscatter performs best in KR, with training residual root mean-squared error (RMSE) of 30%–36% (27–33 t/ha) for airborne data and 38%–39% for simulated BIOMASS data. Airborne TomoSAR data suggest that both vertical and horizontal tomographic resolution are of importance and that it is possible to greatly reduce AGB retrieval bias when compared with airborne P-band SAR backscatter intensity-based retrievals. A lack of significant ground slopes in Remningstorp reduces the benefit of using TomoSAR data which performs similar to retrievals based solely on P-band SAR backscatter intensity. Numéro de notice : A2021-339 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2020.3020775 Date de publication en ligne : 22/09/2020 En ligne : https://doi.org/10.1109/TGRS.2020.3020775 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97570
in IEEE Transactions on geoscience and remote sensing > vol 59 n° 5 (May 2021) . - pp 3793 - 3804[article]Forest height retrieval using P-band airborne multi-baseline SAR data: A novel phase compensation method / Hongliang Lu in ISPRS Journal of photogrammetry and remote sensing, vol 175 (May 2021)PermalinkIntegrated water vapour observations in the Caribbean arc from a network of ground-based GNSS receivers during EUREC4A / Olivier Bock in Earth System Science Data, vol 13 n° 5 (May 2021)PermalinkIntegration of laser scanner and photogrammetry for heritage BIM enhancement / Yahya Alshawabkeh in ISPRS International journal of geo-information, vol 10 n° 5 (May 2021)PermalinkInversion of solar-induced chlorophyll fluorescence using polarization measurements of vegetation / Haiyan Yao in Photogrammetric Engineering & Remote Sensing, PERS, vol 87 n° 5 (May 2021)PermalinkMapping and quantification of the dwarf eelgrass Zostera noltii using a random forest algorithm on a SPOT 7 satellite image / Salma Benmokhtar in ISPRS International journal of geo-information, vol 10 n° 5 (May 2021)PermalinkObservable quality assessment of broadband very long baseline interferometry system / Ming H. Xu in Journal of geodesy, vol 95 n° 5 (May 2021)PermalinkRefining MODIS NIR atmospheric water vapor retrieval algorithm using GPS-derived water vapor data / Jia He in IEEE Transactions on geoscience and remote sensing, vol 59 n° 5 (May 2021)PermalinkA stacked dense denoising–segmentation network for undersampled tomograms and knowledge transfer using synthetic tomograms / Dimitrios Bellos in Machine Vision and Applications, vol 32 n° 3 (May 2021)PermalinkValidation and analysis of Terra and Aqua MODIS, and SNPP VIIRS vegetation indices under zero vegetation conditions: A case study using Railroad Valley Playa / Tomoaki Miura in Remote sensing of environment, vol 257 (May 2021)PermalinkInteger phase clock method with single-satellite ambiguity fixing and its application in LEO satellite orbit determination / Kai Shao in Acta Geodaetica et Cartographica Sinica, vol 50 n° 4 ([20/04/2021])Permalink