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A high-resolution panchromatic-multispectral satellite image fusion method assisted with building segmentation / Fang Gao in Computers & geosciences, vol 168 (November 2022)  

Public [article]  inComputers & geosciences > vol 168 (November 2022) . - n° 105219 Titre : A high-resolution panchromatic-multispectral satellite image fusion method assisted with building segmentation Type de document : Article/Communication Auteurs : Fang Gao, Auteur ; Yihui Li, Auteur ; Peng Zhang, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 105219 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] bâtiment [Termes IGN] filtre de Gauss [Termes IGN] fusion d'images [Termes IGN] image à haute résolution [Termes IGN] image Jilin [Termes IGN] image multibande [Termes IGN] image panchromatique [Termes IGN] image satellite [Termes IGN] lissage de données Résumé : (auteur) The main difficulty of panchromatic-multispectral image fusion is to balance the quality of spatial information and the spectral fidelity. Most of the practical fusion methods determine the optimal parameters based on the spatial and spectral characteristics of all original panchromatic and multispectral bands. However, for built-up and non-built-up areas (like cropland, forest) in one image, there may be large differences in their spatial and spectral characteristics, so their fused results are not optimal respectively with same parameters. To address above issues, this paper presents a high-resolution satellite image fusion method assisted with building segmentation. First, the proposed approach computes the average gradient and Gaussian filtering parameters of built-up and non-built-up areas separately according to the building segmentation results, on the basis of smoothing filter-based intensity modulation (SFIM). Then the intermediate data of two types of areas are computed in parallel and they are composited to obtain the final fused image, weighted by the pixel-wise “building factors” derived from the building segmentation results. Moreover, to better simulate the spatial characteristics of the multispectral image, we perform the “gradient simulation” operation to extract the gradient values in the multispectral image. Experimental results on Jilin-1 satellite images show that the proposed method provides competitive performance in spatial resolution, multispectral fidelity and quantity of information, as compared to the state-of-the-art methods in mainstream commercial software. Numéro de notice : A2022-721 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1016/j.cageo.2022.105219 Date de publication en ligne : 11/09/2022 En ligne : https://doi.org/10.1016/j.cageo.2022.105219 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101657 [article]

Challenges and limitations of earthquake-induced building damage mapping techniques using remote sensing images-A systematic review / Sahar S. Matin in Geocarto international, Vol 37 n° 21 ([01/10/2022])  

Public [article]  inGeocarto international > Vol 37 n° 21 [01/10/2022] . - pp 6186 - 6212 Titre : Challenges and limitations of earthquake-induced building damage mapping techniques using remote sensing images-A systematic review Type de document : Article/Communication Auteurs : Sahar S. Matin, Auteur ; Biswajeet Pradhan, Auteur Année de publication : 2022 Article en page(s) : pp 6186 - 6212 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] apprentissage profond [Termes IGN] cartographie automatique [Termes IGN] cartographie thématique [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] déformation d'édifice [Termes IGN] détection de changement [Termes IGN] dommage matériel [Termes IGN] données lidar [Termes IGN] image optique [Termes IGN] image radar moirée [Termes IGN] secours d'urgence [Termes IGN] séisme Résumé : (auteur) Assessing the extent and level of building damages is crucial to support post-earthquake rescue and relief activities. There is a large body of literature proposing novel frameworks for automating earthquake-induced building damage mapping using high-resolution remote sensing images. Yet, its deployment in real-world scenarios is largely limited to the manual interpretation of images. Although manual interpretation is costly and labor-intensive, it is preferred over automatic and semi-automatic building damage mapping frameworks such as machine learning and deep learning because of its reliability. Therefore, this review paper explores various automatic and semi-automatic building damage mapping techniques with a quest to understand the pros and cons of different methodologies to narrow the gap between research and practice. Further, the research gaps and opportunities are identified for the future development of real-world scenarios earthquake-induced building damage mapping. This review can serve as a guideline for researchers, decision-makers, and practitioners in the emergency management service domain. Numéro de notice : A2022-719 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2021.1933213 Date de publication en ligne : 07/06/2021 En ligne : https://doi.org/10.1080/10106049.2021.1933213 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101651 [article]

Challenging the link between functional and spectral diversity with radiative transfer modeling and data / Javier Pacheco-Labradora in Remote sensing of environment, vol 280 (October 2022)  

Public [article]  inRemote sensing of environment > vol 280 (October 2022) Titre : Challenging the link between functional and spectral diversity with radiative transfer modeling and data Type de document : Article/Communication Auteurs : Javier Pacheco-Labradora, Auteur ; Mirco Migliavacca, Auteur ; Xuanlong Ma, Auteur ; Miguel D. Mahecha, Auteur ; Nuno Carvalhais, Auteur ; Ulrich Weber, Auteur ; Raquel Benavides, Auteur ; Olivier Bouriaud , Auteur ; et al., Auteur Année de publication : 2022 Projets : 3-projet - voir note / Note générale : bibliographie JPL, MMi, and MMa acknowledge the German Aerospace Center (DLR) project OBEF-Accross2 “The Potential of Earth Observations to Capture Patterns of Biodiversity” (Contract No. 50EE1912, German Aerospace Center). JPL, MMi, AH, CW, MMa, GK, FJB, and UW acknowledge the German Aerospace Center (DLR) for providing DESIS imagery through the Announcement of Opportunity “EBioIDEA: Enhancing Biodiversity Inventories with DESIS Imagery Analysis”. FunDivEUROPE data collection was supported by the European Union Seventh Framework Programme (FP7/2007-2013) (grant agreement number: 265171) and the EU H2020 project Soil4Europe (Bioidversa 2017-2019). The in-situ plant traits data collected over Romanian and Spanish sites were supported by a Marie-Curie Fellowship (DIVERFOR, FP7-PEOPLE-2011-IEF. No. 302445) to R. Benavides. OB acknowledges funding from project 10PFE/2021 Ministry of Research, Innovation and Digitalization within Program 1 - Development of national research and development system, Subprogram 1.2 - Institutional Performance - RDI excellence funding projects. XM was supported by the National Natural Science Foundation of China (42171305), the Director Fund of the International Research Center of Big Data for Sustainable Development Goals (CBAS2022DF006), and the Open Fund of State Key Laboratory of Remote Sensing Science (OFSLRSS202229). We thank Prof. Dr. Michael Scherer-Lorenzen for coordinating the interaction with the FunDivEUROPE network and Dr. Fernando Valladares for coordinating data production in FunDivEUROPE sites in Spain. We thank Yuhan Li for helping collect and process Sentinel-2 data in 2020 for the verification task. ESA's Copernicus Open Access Hub enabled the free use of Sentinel-2 data. Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] biodiversité végétale [Termes IGN] image hyperspectrale [Termes IGN] image optique [Termes IGN] image Sentinel-MSI [Termes IGN] limite de résolution spectrale [Termes IGN] modèle de transfert radiatif [Termes IGN] variabilité Résumé : (auteur) In a context of accelerated human-induced biodiversity loss, remote sensing (RS) is emerging as a promising tool to map plant biodiversity from space. Proposed approaches often rely on the Spectral Variation Hypothesis (SVH), linking the heterogeneity of terrestrial vegetation to the variability of the spectroradiometric signals. Yet, due to observational limitations, the SVH has been insufficiently tested, remaining unclear which metrics, methods, and sensors could provide the most reliable estimates of plant biodiversity. Here we assessed the potential of RS to infer plant biodiversity using radiative transfer simulations and inversion. We focused specifically on “functional diversity,” which represents the spatial variability in plant functional traits. First, we simulated vegetation communities and evaluated the information content of different functional diversity metrics (FDMs) derived from their optical reflectance factors (R) or the corresponding vegetation “optical traits,” estimated via radiative transfer model inversion. Second, we assessed the effect of the spatial resolution, the spectral characteristics of the sensor, and signal noise on the relationships between FDMs derived from field and remote sensing datasets. Finally, we evaluated the plausibility of the simulations using Sentinel-2 (multispectral, 10 m pixel) and DESIS (hyperspectral, 30 m pixel) imagery acquired over sites of the Functional Significance of Forest Biodiversity in Europe (FunDivEUROPE) network. We demonstrate that functional diversity can be inferred both by reflectance and optical traits. However, not all the FDMs tested were suited for assessing plant functional diversity from RS. Rao's Q index, functional dispersion, and functional richness were the best-performing metrics. Furthermore, we demonstrated that spatial resolution is the most limiting RS feature. In agreement with simulations, Sentinel-2 imagery provided better estimates of plant diversity than DESIS, despite the coarser spectral resolution. However, Sentinel-2 offered inaccurate results at DESIS spatial resolution. Overall, our results identify the strengths and weaknesses of optical RS to monitor plant functional diversity. Future missions and biodiversity products should consider and benefit from the identified potentials and limitations of the SVH. Numéro de notice : A2022-582 Affiliation des auteurs : LIF+Ext (2020- ) Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.rse.2022.113170 Date de publication en ligne : 18/07/2022 En ligne : https://doi.org/10.1016/j.rse.2022.113170 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101343 [article]

Evaluation of Landsat 8 image pansharpening in estimating soil organic matter using multiple linear regression and artificial neural networks / Abdelkrim Bouasria in Geo-spatial Information Science, vol 25 n° 3 (October 2022)  

Public [article]  inGeo-spatial Information Science > vol 25 n° 3 (October 2022) . - pp 353 - 364 Titre : Evaluation of Landsat 8 image pansharpening in estimating soil organic matter using multiple linear regression and artificial neural networks Type de document : Article/Communication Auteurs : Abdelkrim Bouasria, Auteur ; Khalid Ibno Namra, Auteur ; Abdelmejid Rahimi, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 353 - 364 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] état du sol [Termes IGN] image Landsat-OLI [Termes IGN] image panchromatique [Termes IGN] Maroc [Termes IGN] matière organique [Termes IGN] modèle de simulation [Termes IGN] pansharpening (fusion d'images) [Termes IGN] Perceptron multicouche [Termes IGN] régression multiple [Termes IGN] réseau neuronal artificiel Résumé : (auteur) In agricultural systems, the regular monitoring of Soil Organic Matter (SOM) dynamics is essential. This task is costly and time-consuming when using the conventional method, especially in a very fragmented area and with intensive agricultural activity, such as the area of Sidi Bennour. The study area is located in the Doukkala irrigated perimeter in Morocco. Satellite data can provide an alternative and fill this gap at a low cost. Models to predict SOM from a satellite image, whether linear or nonlinear, have shown considerable interest. This study aims to compare SOM prediction using Multiple Linear Regression (MLR) and Artificial Neural Networks (ANN). A total of 368 points were collected at a depth of 0–30 cm and analyzed in the laboratory. An image at 15 m resolution (MSPAN) was produced from a 30 m resolution (MS) Landsat-8 image using image pansharpening processing and panchromatic band (15 m). The results obtained show that the MLR models predicted the SOM with (training/validation) R2 values of 0.62/0.63 and 0.64/0.65 and RMSE values of 0.23/0.22 and 0.22/0.21 for the MS and MSPAN images, respectively. In contrast, the ANN models predicted SOM with R2 values of 0.65/0.66 and 0.69/0.71 and RMSE values of 0.22/0.10 and 0.21/0.18 for the MS and MSPAN images, respectively. Image pansharpening improved the prediction accuracy by 2.60% and 4.30% and reduced the estimation error by 0.80% and 1.30% for the MLR and ANN models, respectively. Numéro de notice : A2022-722 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1080/10095020.2022.2026743 Date de publication en ligne : 15/02/2022 En ligne : https://doi.org/10.1080/10095020.2022.2026743 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101665 [article]

Classification of pine wilt disease at different infection stages by diagnostic hyperspectral bands / Niwen Li in Ecological indicators, vol 142 (September 2022)  

Public [article]  inEcological indicators > vol 142 (September 2022) Titre : Classification of pine wilt disease at different infection stages by diagnostic hyperspectral bands Type de document : Article/Communication Auteurs : Niwen Li, Auteur ; Langning Huo, Auteur ; Xiaoli Zhang, Auteur Année de publication : 2022 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] aiguille [Termes IGN] analyse discriminante [Termes IGN] image hyperspectrale [Termes IGN] Pinus densiflora [Termes IGN] Pinus koraiensis [Termes IGN] santé des forêts [Termes IGN] signature spectrale [Termes IGN] surveillance forestière Mots-clés libres : competitive adaptive reweighted sampling = échantillonnage compétitif adaptatif pondéré Résumé : (auteur) Pine wilt disease (PWD) is a very destructive forest disease that causes the mortality of pine. The infected trees usually die within three months, and the disease spreads fast with the long-horned beetle as the medium if the infected trees are not removed from the forest in time. Therefore, detecting the infected trees at different infection stage, especially the early infection, is crucial for preventing PWD spread. This study aims to exhibit the spectral differences of the pine needles between healthy pines and infected pines at different infection stages and reveal the diagnostic spectral bands for classifying the different infected stage trees. We collected needle samples from healthy, early-, middle-, late-stage infected trees in a Japanese pine (Pinus densiflora) forest and a Korean pine (Pinus koraiensis) forest in northern China to explore the spectral and biochemical properties differences of these four classes, and selected the sensitive bands combining competitive adaptive reweighted sampling (CARS) and successive projections algorithm (SPA). The selected bands were used for the four infection stages classification by linear discriminant analysis (LDA) algorithm. The results show that Chlorophyll a, chlorophyll b, carotenoids, and moisture content decreases with the aggravation of infection. The green (510–530 nm), red-edge (680–760 nm), and short-wave infrared (1400–1420 nm and 1925–1965 nm) bands are the sensitive bands, and the overall accuracy is 77 % and 78 % for the Japanese pine and Korean pine respectively when using these bands for classifying healthy, early-, middle-, late-stage infected trees. The results demonstrate that physiological parameters including Chlorophyll a, chlorophyll b, carotenoids, and moisture content can be used as the diagnostic parameters of PWD, and the selected sensitive spectral bands are feasible for detecting the stress symptoms of the Japanese pine and Korean pine. Numéro de notice : A2022-617 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.ecolind.2022.109198 Date de publication en ligne : 26/07/2022 En ligne : https://doi.org/10.1016/j.ecolind.2022.109198 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101374 [article]

Crowdsourcing-based application to solve the problem of insufficient training data in deep learning-based classification of satellite images / Ekrem Saralioglu in Geocarto international, vol 37 n° 18 ([01/09/2022])  

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Detection of potential gold mineralization areas using MF-fuzzy approach on multispectral data / Tohid Nouri in Geocarto international, Vol 37 n° 17 ([20/08/2022])  

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Detection and characterization of slow-moving landslides in the 2017 Jiuzhaigou earthquake area by combining satellite SAR observations and airborne Lidar DSM / Jiehua Cai in Engineering Geology, vol 305 (August 2022)  

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Hyperspectral unmixing using transformer network / Preetam Ghosh in IEEE Transactions on geoscience and remote sensing, vol 60 n° 8 (August 2022)  

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Estimating generalized measures of local neighbourhood context from multispectral satellite images using a convolutional neural network / Alex David Singleton in Computers, Environment and Urban Systems, vol 95 (July 2022)  

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Semantic feature-constrained multitask siamese network for building change detection in high-spatial-resolution remote sensing imagery / Qian Shen in ISPRS Journal of photogrammetry and remote sensing, vol 189 (July 2022)  

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Artificial intelligence techniques in extracting building and tree footprints using aerial imagery and LiDAR data / Saeideh Sahebi Vayghan in Geocarto international, vol 37 n° 10 ([01/06/2022])  

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HyperNet: A deep network for hyperspectral, multispectral, and panchromatic image fusion / Kun Li in ISPRS Journal of photogrammetry and remote sensing, vol 188 (June 2022)  

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Precise crop classification of hyperspectral images using multi-branch feature fusion and dilation-based MLP / Haibin Wu in Remote sensing, vol 14 n° 11 (June-1 2022)  

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Application oriented quality evaluation of Gaofen-7 optical stereo satellite imagery / Jiaojiao Tian in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol V-1-2022 (2022 edition)  

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