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The FIRST model: Spatiotemporal fusion incorrporting spectral autocorrelation / Shuaijun Liu in Remote sensing of environment, vol 279 (15 September 2022)  

Public [article]  inRemote sensing of environment > vol 279 (15 September 2022) . - n° 113111 Titre : The FIRST model: Spatiotemporal fusion incorrporting spectral autocorrelation Type de document : Article/Communication Auteurs : Shuaijun Liu, Auteur ; Junxiong Zhou, Auteur ; Yuean Qiu, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 113111 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse comparative [Termes IGN] autocorrélation [Termes IGN] bande spectrale [Termes IGN] détection de changement [Termes IGN] données spatiotemporelles [Termes IGN] fusion de données [Termes IGN] image Landsat-OLI [Termes IGN] image Terra-MODIS [Termes IGN] réflectance de surface [Termes IGN] réflectance spectrale [Termes IGN] régression des moindres carrés partiels [Termes IGN] régression multiple Résumé : (auteur) Over the past decade, spatiotemporal fusion has become an indispensable tool for monitoring land surface dynamics due to its promising ability to produce surface reflectance products with both high spatial and temporal resolutions. However, existing fusion methods usually generate multispectral band products by predicting each spectral band separately, so the useful information of spectral autocorrelation within the spectrum has been ignored and waits to be exploited. To address this issue, we propose a novel spatiotemporal fusion method, the spatiotemporal Fusion Incorrporting Spectral autocorrelaTion (FIRST) model, to fully utilize the multiple spectral bands of surface reflectance products. Compared with other fusion methods, the model has three distinct advantages: (1) it utilizes spectral autocorrelation in a many-to-many regression framework that simultaneously inputs and predicts multispectral bands without the collinearity effect; (2) it maintains high fusion accuracy when the spatiotemporal variation is large with acceptable computational efficiency; and (3) it can produce robust results even with input images contaminated by haze and thin clouds. We tested the FIRST model at several experimental sites and compared it with four typical methods, the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM), Flexible Spatiotemporal DAta Fusion (FSDAF) model, the regression model Fitting, spatial Filtering and residual Compensation (Fit-FC) model and the enhanced STARFM (ESTARFM). The results demonstrate that FIRST yields better overall performance for its simple and effective technical principles. FIRST is thus expected to provide high-quality remotely sensed data with high spatial resolution and frequent observations for various applications. Numéro de notice : A2022-554 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1016/j.rse.2022.113111 Date de publication en ligne : 16/06/2022 En ligne : https://doi.org/10.1016/j.rse.2022.113111 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101166 [article]

A second-order attention network for glacial lake segmentation from remotely sensed imagery / Shidong Wang in ISPRS Journal of photogrammetry and remote sensing, vol 189 (July 2022)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 189 (July 2022) . - pp 289 - 301 Titre : A second-order attention network for glacial lake segmentation from remotely sensed imagery Type de document : Article/Communication Auteurs : Shidong Wang, Auteur ; Maria V. Peppa, Auteur ; Wen Xiao, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 289 - 301 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] apprentissage profond [Termes IGN] attention (apprentissage automatique) [Termes IGN] changement climatique [Termes IGN] covariance [Termes IGN] image Landsat-8 [Termes IGN] Inde [Termes IGN] itération [Termes IGN] lac glaciaire [Termes IGN] réflectance de surface [Termes IGN] segmentation d'image [Termes IGN] tenseur Résumé : (auteur) Climate change is increasing the risk of glacial lake outburst floods (GLOFs) in many of the world’s most vulnerable and high mountain regions. Simultaneously, remote sensing technologies now facilitate continuous monitoring of glacial lake evolution around the globe, although accurate and reliable automated glacial lake mapping from satellite data remains challenging. In this study, a Second-order Attention Network (SoAN) is devised for the automated segmentation of lakes from satellite imagery. In particular, a novel Second-order Attention Module (SoAM) is proposed to capture the long-range spatial dependencies and establish channel attention derived from the covariance representations of local features. Furthermore, as the dimensions of the input and output tensors are identical and it simply relies on matrix calculations, the proposed SoAM can be embedded into different positions of a given architecture while maintaining similar reference speed. The designed network is implemented on Landsat-8 imagery and outputs are compared against representative deep learning models, demonstrating improved results with a Dice of 81.02% and a F2 Score of 85.17%. Numéro de notice : A2022-470 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2022.05.007 Date de publication en ligne : 29/05/2022 En ligne : https://doi.org/10.1016/j.isprsjprs.2022.05.007 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100814 [article]

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A phenology-based vegetation index classification (PVC) algorithm for coastal salt marshes using Landsat 8 images / Jing Zeng in International journal of applied Earth observation and geoinformation, vol 110 (June 2022)  

Public [article]  inInternational journal of applied Earth observation and geoinformation > vol 110 (June 2022) . - n° 102776 Titre : A phenology-based vegetation index classification (PVC) algorithm for coastal salt marshes using Landsat 8 images Type de document : Article/Communication Auteurs : Jing Zeng, Auteur ; Yonghua Sun, Auteur ; Peirun Cao, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 102776 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] classification par arbre de décision [Termes IGN] classification semi-dirigée [Termes IGN] image Landsat-8 [Termes IGN] indice de végétation [Termes IGN] Kiangsou (Chine) [Termes IGN] marais salant [Termes IGN] phénologie [Termes IGN] réflectance de surface Résumé : (auteur) Coastal salt marshes, as a globally significant intertidal ecosystem, are highly productive but extremely fragile and unstable. Mapping coastal salt marshes accurately is the basis of assessing global climate change, biological invasion, and coastal erosion. Using Landsat 8 images, this paper integrated the advantages of pixel- and phenology-based algorithms and vegetation indices in vegetation classification. An enhanced phenology-based vegetation index classification (PVC) algorithm is proposed to obtain the spatial distribution and community composition of coastal salt marshes in Bohai Sea of China accurately and quickly. The results showed that (1) the coastal redness vegetation index (CRVI) can be used to extract Suaeda spp. effectively, and the phenology-based vegetation indices (PVIs) dataset can alleviate the spatial variability of phenology in coastal salt marshes; (2) the crucial phenological periods for identifying coastal salt marshes are May, October, and November, and the optimal PVIs are consistent with the phenological characteristics of salt marshes; (3) during the year 2018–2019, the overall accuracy (OA) of the PVC algorithm in Yancheng coast of Jiangsu Province and Bohai Sea coast reached 80.49 % and 90.8 % respectively. A total of 14,763.39 ha of salt marshes were found in the coastal area of Bohai Sea, and Shandong Province had the most abundant types of salt marshes and the largest area; (4) the classification model based on the PVC algorithm is stable and scalable in 2016–2017 and 2020–2021, with the OA of 89.19% and 86.67% respectively. These results demonstrate the value of the PVC algorithm in vegetation classification, and this study can provide a referable semi-automatic vegetation classification method for other coastal areas. Numéro de notice : A2022-551 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1016/j.jag.2022.102776 Date de publication en ligne : 10/05/2022 En ligne : https://doi.org/10.1016/j.jag.2022.102776 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101154 [article]

A continuous change tracker model for remote sensing time series reconstruction / Yangjian Zhang in Remote sensing, vol 14 n° 9 (May-1 2022)  

Public [article]  inRemote sensing > vol 14 n° 9 (May-1 2022) . - n° 2280 Titre : A continuous change tracker model for remote sensing time series reconstruction Type de document : Article/Communication Auteurs : Yangjian Zhang, Auteur ; Li Wang, Auteur ; Yuanhuizi He, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 2280 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] algorithme de filtrage [Termes IGN] analyse harmonique [Termes IGN] compression d'image [Termes IGN] détection de changement [Termes IGN] extraction de traits caractéristiques [Termes IGN] Leaf Area Index [Termes IGN] Normalized Difference Vegetation Index [Termes IGN] phénologie [Termes IGN] production primaire brute [Termes IGN] reconstruction d'image [Termes IGN] réflectance de surface [Termes IGN] série temporelle Résumé : (auteur) It is hard for current time series reconstruction methods to achieve the balance of high-precision time series reconstruction and explanation of the model mechanism. The goal of this paper is to improve the reconstruction accuracy with a well-explained time series model. Thus, we developed a function-based model, the CCTM (Continuous Change Tracker Model) model, that can achieve high precision in time series reconstruction by tracking the time series variation rate. The goal of this paper is to provide a new solution for high-precision time series reconstruction and related applications. To test the reconstruction effects, the model was applied to four types of datasets: normalized difference vegetation index (NDVI), gross primary productivity (GPP), leaf area index (LAI), and MODIS surface reflectance (MSR). Several new observations are as follows. First, the CCTM model is well explained and based on the second-order derivative theorem, which divides the yearly time series into four variation types including uniform variations, decelerated variations, accelerated variations, and short-periodical variations, and each variation type is represented by a designed function. Second, the CCTM model provides much better reconstruction results than the Harmonic model on the NDVI, GPP, MSR, and LAI datasets for the seasonal segment reconstruction. The combined use of the Savitzky–Golay filter and the CCTM model is better than the combinations of the Savitzky–Golay filter with other models. Third, the Harmonic model has the best trend-fitting ability on the yearly time series dataset, with the highest R-Square and the lowest RMSE among the four function fitting models. However, with seasonal piecewise fitting, the four models all achieved high accuracy, and the CCTM performs the best. Fourth, the CCTM model should also be applied to time series image compression, two compression patterns with 24 coefficients and 6 coefficients respectively are proposed. The daily MSR dataset can achieve a compression ratio of 15 by using the 6-coefficients method. Finally, the CCTM model also has the potential to be applied to change detection, trend analysis, and phenology and seasonal characteristics extractions. Numéro de notice : A2022-384 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.3390/rs14092280 Date de publication en ligne : 09/05/2022 En ligne : https://doi.org/10.3390/rs14092280 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100662 [article]

Development of the GLASS 250-m leaf area index product (version 6) from MODIS data using the bidirectional LSTM deep learning model / Han Ma in Remote sensing of environment, vol 273 (May 2022)  

Public [article]  inRemote sensing of environment > vol 273 (May 2022) . - n° 112985 Titre : Development of the GLASS 250-m leaf area index product (version 6) from MODIS data using the bidirectional LSTM deep learning model Type de document : Article/Communication Auteurs : Han Ma, Auteur ; Shunlin Liang, Auteur Année de publication : 2022 Article en page(s) : n° 112985 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] apprentissage profond [Termes IGN] cohérence temporelle [Termes IGN] image Terra-MODIS [Termes IGN] Leaf Area Index [Termes IGN] réflectance de surface [Termes IGN] régression [Termes IGN] série temporelle [Termes IGN] surveillance de la végétation Résumé : (auteur) Leaf area index (LAI) is a terrestrial essential climate variable that is required in a variety of ecosystem and climate models. The Global LAnd Surface Satellite (GLASS) LAI product has been widely used, but its current version (V5) from Moderate Resolution Imaging Spectroradiometer (MODIS) data has several limitations, such as frequent temporal fluctuation, large data gaps, high dependence on the quality of surface reflectance, and low computational efficiency. To address these issues, this paper presents a deep learning model to generate a new version of the LAI product (V6) at 250-m resolution from MODIS data from 2000 onward. Unlike most existing algorithms that estimate one LAI value at one time for each pixel, this model estimates LAI for 2 years simultaneously. Three widely used LAI products (MODIS C6, GLASS V5, and PROBA-V V1) are used to generate global representative time-series LAI training samples using K-means clustering analysis and least difference criteria. We explore four machine learning models, the general regression neural network (GRNN), long short-term memory (LSTM), gated recurrent unit (GRU), and Bidirectional LSTM (Bi-LSTM), and identify Bi-LSTM as the best model for product generation. This new product is directly validated using 79 high-resolution LAI reference maps from three in situ observation networks. The results show that GLASS V6 LAI achieves higher accuracy, with a root mean square (RMSE) of 0.92 at 250 m and 0.86 at 500 m, while the RMSE is 0.98 for PROBA-V at 300 m, 1.08 for GLASS V5, and 0.95 for MODIS C6 both at 500 m. Spatial and temporal consistency analyses also demonstrate that the GLASS V6 LAI product is more spatiotemporally continuous and has higher quality in terms of presenting more realistic temporal LAI dynamics when the surface reflectance is absent for a long period owing to persistent cloud/aerosol contaminations. The results indicate that the new Bi-LSTM deep learning model runs significantly faster than the GLASS V5 algorithm, avoids the reconstruction of surface reflectance data, and is resistant to the noises (cloud and snow contamination) or missing values contained in surface reflectance than other methods, as the Bi-LSTM can effectively extract information across the entire time series of surface reflectance rather than a single time point. To our knowledge, this is the first global time-series LAI product at the 250-m spatial resolution that is freely available to the public (www.geodata.cn and www.glass.umd.edu). Numéro de notice : A2022-284 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.1016/j.rse.2022.112985 Date de publication en ligne : 10/03/2022 En ligne : https://doi.org/10.1016/j.rse.2022.112985 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100303 [article]

Assessment of sky diffuse irradiance and building reflected irradiance in cast shadows / Manchun Lei (2021)  

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Learning harmonised Pleiades and Sentinel-2 surface reflectances / J. Michel (2021)  

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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)  

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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)  

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Application of geographic Information system and remote sensing in multiple criteria analysis to identify priority areas for biodiversity conservation in Vietnam / Xuan Dinh Vu (2020)  

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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)  

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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)  

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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)  

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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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