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Particle swarm optimization based water index (PSOWI) for mapping the water extents from satellite images / Mohammad Hossein Gamshadzaei in Geocarto international, vol 36 n° 20 ([01/12/2021])  

Public [article]  inGeocarto international > vol 36 n° 20 [01/12/2021] . - pp 2264 - 2278 Titre : Particle swarm optimization based water index (PSOWI) for mapping the water extents from satellite images Type de document : Article/Communication Auteurs : Mohammad Hossein Gamshadzaei, Auteur ; Majid Rahimzadegan, Auteur Année de publication : 2021 Article en page(s) : pp 2264 - 2278 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse multibande [Termes IGN] analyse spectrale [Termes IGN] Arménie [Termes IGN] bande infrarouge [Termes IGN] cartographie thématique [Termes IGN] détection d'objet [Termes IGN] eau de surface [Termes IGN] extraction de traits caractéristiques [Termes IGN] Google Earth [Termes IGN] image à haute résolution [Termes IGN] image satellite [Termes IGN] indice d'humidité [Termes IGN] Iran [Termes IGN] occupation du sol [Termes IGN] optimisation par essaim de particules [Termes IGN] polygone Résumé : (auteur) Various spectral indices have been introduced to detect water extent from satellite images with different performances in various regions. The aim of this study is to provide an efficient index using particle swarm optimization (PSO) algorithm to detect water spread areas from satellite images with similar performance in different regions. This index is introduced for images containing water absorption bands from visible to middle infrared wavelengths. Eleven images were prepared from different satellites and water bodies with various environmental conditions. In addition, 40 pixels from water and 40 pixels from non-water regions were selected as training data for PSO algorithm. Results were evaluated using digitized polygons of water bodies on high-resolution images of Google Earth. The best results in PSO-based water index (PSOWI) were obtained by the combination of two bands (red and middle infrared). PSOWI represented proper performance in the selected various land covers and satellite images. Numéro de notice : A2021-831 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2019.1700554 Date de publication en ligne : 12/12/2019 En ligne : https://doi.org/10.1080/10106049.2019.1700554 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99004 [article]

Exploring the inclusion of Sentinel-2 MSI texture metrics in above-ground biomass estimation in the community forest of Nepal / Santa Pandit in Geocarto international, vol 35 n° 16 ([01/12/2020])  

Public [article]  inGeocarto international > vol 35 n° 16 [01/12/2020] . - pp 1832 - 1849 Titre : Exploring the inclusion of Sentinel-2 MSI texture metrics in above-ground biomass estimation in the community forest of Nepal Type de document : Article/Communication Auteurs : Santa Pandit, Auteur ; Satoshi Tsuyuki, Auteur ; Timothy Dube, Auteur Année de publication : 2020 Article en page(s) : pp 1832 - 1849 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] analyse multibande [Termes IGN] analyse texturale [Termes IGN] apprentissage automatique [Termes IGN] biomasse aérienne [Termes IGN] classification par forêts d'arbres décisionnels [Termes IGN] forêt [Termes IGN] image Sentinel-MSI [Termes IGN] indice de végétation [Termes IGN] Népal Résumé : (auteur) The potential of the improved resolution Sentinel-2 MSI data was explored through texture metrics, vegetation indices (VIs) and pooled dataset using the Random Forest (RF) machine learning algorithm to estimate Above-ground Biomass (AGB) in a sub-tropical forest of Nepal. Texture metrics were derived based on different working window sizes (3 × 3, 5 × 5, 7 × 7 and 9 × 9), and the results were compared with those obtained, using raw traditional bands (Analysis set 1: 2, 3, 4, 8, 11 and 12), raw traditional and red edge bands (Analysis set 2: Set 1 + Band 5, 6, 7 and 8A), and red edge bands (Analysis set 3) only. Comparatively, the use of pooled data (texture and VIs) yielded higher biomass estimates. The results from pooled data based on the 7 × 7 window size resulted in models with better model fitting parameters. For instance, pooled data produced an R2 = 0.99 and a RMSE = 4.51 t ha−1 (relRMSE = 2.82). Further, the RF model selected dissimilarity, variance and mean from Band 2 and SAVI (Soil adjusted vegetation index) as the most important AGB predictor variables. The results demonstrated that like the red-edge bands, traditional bands were equally important in AGB estimation. Numéro de notice : A2020-727 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2019.1588390 Date de publication en ligne : 10/06/2019 En ligne : https://doi.org/10.1080/10106049.2019.1588390 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96334 [article]

Multichannel Pulse-Coupled Neural Network-Based Hyperspectral Image Visualization / Puhong Duan in IEEE Transactions on geoscience and remote sensing, vol 58 n° 4 (April 2020)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 58 n° 4 (April 2020) . - pp 2444 - 2456 Titre : Multichannel Pulse-Coupled Neural Network-Based Hyperspectral Image Visualization Type de document : Article/Communication Auteurs : Puhong Duan, Auteur ; Xudong Kang, Auteur ; Shutao Li, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 2444 - 2456 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse d'image numérique [Termes IGN] analyse multibande [Termes IGN] chromatopsie [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] cohérence des couleurs [Termes IGN] image en couleur composée [Termes IGN] image hyperspectrale [Termes IGN] image RVB [Termes IGN] synthèse trichromatique [Termes IGN] visualisation de données Résumé : (auteur) Hyperspectral Image (HSI) visualization, which aims at displaying as much material information of original images as possible on a trichromatic monitor with natural color, plays an important role in image interpretation and analysis. However, most of the HSI visualization methods only focus on presenting the detail information of a scene without providing natural colors and distinguishing land covers with similar colors. In order to address this problem, this article proposes a multichannel pulse-coupled neural network (MPCNN)-based HSI visualization method, which consists of the following steps. First, the MPCNN is proposed and explored to fuse the original HSI so as to obtain a fused band with rich spatial details. Then, a color mapping scheme is proposed to determine the weights of red, green, and blue (RGB) channels. Finally, the weighted RGB channels are stacked together for visualization. Experiments performed on four hyperspectral data sets demonstrate that the proposed method not only displays the HSI with nature colors but also improves the details in the image. The effectiveness of the proposed method is demonstrated in terms of both visual effect and objective indexes. Numéro de notice : A2020-197 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2019.2949427 Date de publication en ligne : 20/11/2019 En ligne : https://doi.org/10.1109/TGRS.2019.2949427 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94867 [article]

Individual tree crown segmentation in tropical peat swamp forest using airborne hyperspectral data / Sitinor Atikah Nordin in Geocarto international, vol 34 n° 11 ([15/08/2019])  

Public [article]  inGeocarto international > vol 34 n° 11 [15/08/2019] . - pp 1218 - 1236 Titre : Individual tree crown segmentation in tropical peat swamp forest using airborne hyperspectral data Type de document : Article/Communication Auteurs : Sitinor Atikah Nordin, Auteur ; Zulkiflee Abd Latif, Auteur ; Hamdan Omar, Auteur Année de publication : 2019 Article en page(s) : pp 1218 - 1236 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse en composantes principales [Termes IGN] analyse multibande [Termes IGN] Asie du sud-est [Termes IGN] bande rouge [Termes IGN] canopée [Termes IGN] capteur hyperspectral [Termes IGN] carte forestière [Termes IGN] forêt tropicale [Termes IGN] image hyperspectrale [Termes IGN] image proche infrarouge [Termes IGN] image satellite [Termes IGN] niveau de gris (image) [Termes IGN] réflectance végétale [Termes IGN] segmentation d'image [Termes IGN] teneur en chlorophylle des feuilles [Termes IGN] tourbière Résumé : (Auteur) Individual tree crown segmentation is important step for deriving various information for fine-scale analysis of ecological process. However, only several studies have applied tree crown segmentation in tropical forest ecosystems, especially in mixed peat swamp forests. In this study, hyperspectral data were used to detect changes in the biochemical and biophysical characteristics, which are important factors for tree crown segmentation. Principal Component Analysis method was performed to investigate its influence on crown segmentation. Visually Selected PCs, 160 PCs and 160 Spectral Bands image were used and two segmentation techniques; Watershed Transformation and Region Growing segmentation were applied on those images. The highest accuracy was achieved for the crown segmentation is using Region Growing segmentation, based on 1:1 measurement, D value and RMSE value. The results obtained from 160 PCs image using region growing algorithm shows better accuracy with D value of 0.2 (80% accuracy, 20% error) and RMSE of 9.9 m2. Numéro de notice : A2019-463 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2018.1475511 Date de publication en ligne : 24/05/2018 En ligne : https://doi.org/10.1080/10106049.2018.1475511 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93605 [article]

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Mapping leaf chlorophyll content from Sentinel-2 and RapidEye data in spruce stands using the invertible forest reflectance model / Roshanak Darvishzadeh in International journal of applied Earth observation and geoinformation, vol 79 (July 2019)  

Public [article]  inInternational journal of applied Earth observation and geoinformation > vol 79 (July 2019) . - pp 58-70 Titre : Mapping leaf chlorophyll content from Sentinel-2 and RapidEye data in spruce stands using the invertible forest reflectance model Type de document : Article/Communication Auteurs : Roshanak Darvishzadeh, Auteur ; Andrew K. Skidmore, Auteur ; Haidi Abdullah, Auteur ; Elias Cherenet, Auteur Année de publication : 2019 Article en page(s) : pp 58-70 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] analyse multibande [Termes IGN] bande rouge [Termes IGN] bande spectrale [Termes IGN] Bavière (Allemagne) [Termes IGN] canopée [Termes IGN] carte de la végétation [Termes IGN] image RapidEye [Termes IGN] image Sentinel-MSI [Termes IGN] modèle d'inversion [Termes IGN] Picea abies [Termes IGN] réflectance végétale [Termes IGN] spectrophotométrie [Termes IGN] teneur en chlorophylle des feuilles Résumé : (auteur) Leaf chlorophyll plays an essential role in controlling photosynthesis, physiological activities and forest health. In this study, the performance of Sentinel-2 and RapidEye satellite data and the Invertible Forest Reflectance Model (INFORM) radiative transfer model (RTM) for retrieving and mapping of leaf chlorophyll content in the Norway spruce (Picea abies) stands of a temperate forest was evaluated. Biochemical properties of leaf samples as well as stand structural characteristics were collected in two subsequent field campaigns during July 2015 and 2016 in the Bavarian Forest National Park (BFNP), Germany, parallel with the timing of the RapidEye and Sentinel-2 images. Leaf chlorophyll was measured both destructively and nondestructively using wet chemical spectrophotometry analysis and a hand-held chlorophyll content meter. The INFORM was utilised in the forward mode to generate two lookup tables (LUTs) in the spectral band settings of RapidEye and Sentinel-2 data using information obtained from the field campaigns. Before generating the LUTs, the sensitivity of the model input parameters to the spectral data from RapidEye and Sentinel-2 were examined. The canopy reflectance of the studied plots were obtained from the satellite images and used as input for the inversion of LUTs. The coefficient of determination (R2), root mean square errors (RMSE), and the normalised root mean square errors (NRMSE), between the retrieved and measured leaf chlorophyll, were then used to examine the attained results from RapidEye and Sentinel-2 data, respectively. The use of multiple solutions and spectral subsets for the inversion process were further investigated to enhance the retrieval accuracy of foliar chlorophyll. The result of the sensitivity analysis demonstrated that the simulated canopy reflectance of Sentinel-2 is sensitive to the alternation of all INFORM input parameters, while the simulated canopy reflectance from RapidEye did not show sensitivity to leaf water content variations. In general, there was agreement between the simulated and measured reflectance spectra from RapidEye and Sentinel-2, particularly in the visible and red-edge regions. However, examining the average absolute error from the simulated and measured reflectance revealed a large discrepancy in spectral bands around the near-infrared shoulder. The relationship between retrieved and measured leaf chlorophyll content from the Sentinel-2 data had a higher coefficient of determination with a higher NRMSE (NRMSE = 0.36 μg/cm2, R2 = 0.45) compared to those obtained using the RapidEye data (NRMSE = 0.31 μg/cm2 and R2 = 0.39). Using the mean of the ten best solutions (retrieved chlorophyll) the retrieval error for both Sentinel-2 and RapidEye data decreased (NRMSE = 0.34, NRMSE = 0.26, respectively), as compared to only selecting the single best solution. When the Sentinel-2 red edge bands were used as the spectral subset, the retrieval error of leaf chlorophyll decreased indicating the importance of red edge, as well as properly located spectral bands, for leaf chlorophyll estimation. The chlorophyll maps produced by the inversion of the two LUTs effectively represented the variation of foliar chlorophyll in BFNP and confirmed our earlier findings on the observed stress pattern caused by insect infestation. Our findings emphasise the importance of multispectral satellites which benefits from red edge spectral bands such as Sentinel-2 as well as RapidEye for regional mapping of vegetation foliar properties, particularly, chlorophyll using RTMs such as INFORM. Numéro de notice : A2019-460 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.jag.2019.03.003 Date de publication en ligne : 08/03/2019 En ligne : https://doi.org/10.1016/j.jag.2019.03.003 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93577 [article]

Conditional random field and deep feature learning for hyperspectral image classification / Fahim Irfan Alam in IEEE Transactions on geoscience and remote sensing, vol 57 n° 3 (March 2019)  

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A critical comparison among pansharpening algorithms / Gemine Vivone in IEEE Transactions on geoscience and remote sensing, vol 53 n° 5 (mai 2015)  

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Méthode de sélection des bandes à base de l'analyse en composantes indépendantes appliquée aux images hyperspectrales de télédétection / Seloua Chouaf in Revue Française de Photogrammétrie et de Télédétection, n° 204 (Octobre 2013)  

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Application of multiple endmember spectral mixture analysis (MESMA) to AVIRIS imagery for coastal salt marsh mapping: a case study in China Camp, CA, USA / L. Li in International Journal of Remote Sensing IJRS, vol 26 n° 23 (December 2005)  

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Multispectral Thermal Imager: mission and applications overview / J.J. Szymanski in IEEE Transactions on geoscience and remote sensing, vol 43 n° 9 (September 2005)  

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A quantitative comparison of methods for classifying burned areas with LISS-3 imagery / R.M. Roman-Cuesta in International Journal of Remote Sensing IJRS, vol 26 n° 9 (May 2005)  

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Mapping Lake CDOM [coloured dissolved organic matter] by satellite remote sensing / T. Kuster in Remote sensing of environment, vol 94 n° 4 (28/02/2005)  

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Mapping tropical forest structure in south-eastern Madagascar using remote sensing and artificial neural networks / J.C. Ingram in Remote sensing of environment, vol 94 n° 4 (28/02/2005)  

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Cartographie de la fraction argileuse du sol dans le rif marocain à l'aide du capteur ASTER et de l'analyse géostatique / M. Chikhaoui in Revue internationale de géomatique, vol 14 n° 3 - 4 (septembre 2004 – février 2005)  

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Mapping wildfire burns severity in southern California forests and shrub lands using enhanced Thematic Mapper imagery / J. Rogan in Geocarto international, vol 16 n° 4 (December 2001 - February 2002)  

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