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Extracting soil salinization information with a fractional-order filtering algorithm and grid-search support vector machine (GS-SVM) model / Xiaoping Wang in International Journal of Remote Sensing IJRS, vol 41 n° 3 (15 - 22 janvier 2020)
[article]
Titre : Extracting soil salinization information with a fractional-order filtering algorithm and grid-search support vector machine (GS-SVM) model Type de document : Article/Communication Auteurs : Xiaoping Wang, Auteur ; Fei Zhang, Auteur ; Hsiang-Te Kung, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 953 - 973 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] algorithme de filtrage
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] état du sol
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] image Sentinel-MSI
[Termes IGN] sel
[Termes IGN] sol salin
[Termes IGN] zone sècheRésumé : (auteur) The remote sensing information on the extraction method is of great importance to improve the accuracy and efficiency of soil salinization information. The objective of this study is to develop remote sensing extraction techniques to improve soil salinization maps. The following procedures were used in this study: (1) developed a fractional-order algorithm-based methodology of filter from high-resolution remote sensing imagery (Sentinel-2 MSI); (2) investigated the changing trend of image under different order filters; and (3) used a grid-search algorithm-support vector machines (GS-SVM) classification to employ extraction information of soil salinization. The results showed that the Fractional-order filter method outperformed the integer derivative in extracted information of soil salinization. In comparison of the classification accuracy between fractional-order processing algorithm and integer-order image processing algorithm, the fractional order has improved remarkably. The optimal classification model was 0.6 order, 0.8 order, 1.4 order, 1.6 order, and 1.8 order models. The overall accuracy and kappa coefficient (κ) of these models are 91.90% and 0.90, respectively. Analysing and comparing between soil salt index and filtering algorithm (1.2 order), the researchers found that the classification results of the two methods are similar. In general, this method can successfully extract soil salinization information in dry regions. Numéro de notice : A2020-213 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1080/01431161.2019.1654142 Date de publication en ligne : 14/08/2019 En ligne : https://doi.org/10.1080/01431161.2019.1654142 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94898
in International Journal of Remote Sensing IJRS > vol 41 n° 3 (15 - 22 janvier 2020) . - pp 953 - 973[article]Research on the estimation model of vegetation water content in halophyte leaves based on the newly developed vegetation indices / Zhe Li in Photogrammetric Engineering & Remote Sensing, PERS, vol 84 n° 9 (September 2018)
[article]
Titre : Research on the estimation model of vegetation water content in halophyte leaves based on the newly developed vegetation indices Type de document : Article/Communication Auteurs : Zhe Li, Auteur ; Fei Zhang, Auteur ; Lihua Chen, Auteur ; Haiwei Zhang, Auteur ; Hsiang-Te Kung, Auteur Année de publication : 2018 Article en page(s) : pp 538 - 548 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image
[Termes IGN] croissance végétale
[Termes IGN] feuille (végétation)
[Termes IGN] indice de végétation
[Termes IGN] plante halophile
[Termes IGN] Populus euphratica
[Termes IGN] signature spectrale
[Termes IGN] Sinkiang (Chine)
[Termes IGN] Tamarix (genre)
[Termes IGN] teneur en eau de la végétationRésumé : (Auteur) The vegetation water content (VWC) quantitative is useful for monitoring vegetation physiological growth. The relationship between VWC and vegetation water indices was analyzed. The optimal estimation model was established. The results show that: (1) Absorption bands primarily fell within 380 to 400 nm, 680 to 720 nm, 1420 to 1450 nm, 1900 to 1940 nm, and 2450 to 2500 nm; (2) comparing published vegetation water indices and developed vegetation indices, it showed that DVI(1712,1382), NDSI(2201,1870) and RSI(2259,1870) had a better correlation with VWC than the published vegetation water; and (3) NDSI(2201,1870) and RSI(2259,1870) performed well in estimating vegetation water content, DVI(1712,1382) had a rough estimate of its water content. Moreover, the linear combination of DVI(1712,1382), NDSI(2201,1870) and RSI(2259,1870) improved the estimation of VWC. The best vegetation indices for estimating VWC were found to be the linear combination of DVI(1712,1382), NDSI(2201,1870) and RSI(2259,1870) in arid area of northwestern China. Numéro de notice : A2018-361 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.84.9.537 Date de publication en ligne : 01/09/2018 En ligne : https://doi.org/10.14358/PERS.84.9.537 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=90672
in Photogrammetric Engineering & Remote Sensing, PERS > vol 84 n° 9 (September 2018) . - pp 538 - 548[article]Réservation
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