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Hyperspectral image clustering with Albedo recovery Fuzzy C-Means / Peyman Azimpour in International Journal of Remote Sensing IJRS, vol 41 n° 16 (01-10 May 2020)
[article]
Titre : Hyperspectral image clustering with Albedo recovery Fuzzy C-Means Type de document : Article/Communication Auteurs : Peyman Azimpour, Auteur ; R. Shad, Auteur ; M. Ghaemi, Auteur ; H. Etemadfard, Auteur Année de publication : 2020 Article en page(s) : pp 6117 - 6134 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] albedo
[Termes IGN] classification floue
[Termes IGN] image hyperspectrale
[Termes IGN] regroupement de données
[Termes IGN] télédétectionRésumé : (auteur) Hyperspectral image clustering is usually used for unsupervised learning in different applications. However, the traditional clustering methods have not been considered the complex relationships among neighbouring pixels. The Albedo and Shading elements can define pixel values in the HyperSpectral Images (HSIs). In HSIs, features are different from each other because of their natural physical characteristics and the physical nature of different image features can be described by the Albedo element. Therefore, in this paper, we generate the natural Albedo feature of the HSIs by applying Albedo recovery step to exploit main information from HSIs. Then, we utilized the Fuzzy C-means clustering method to cluster the natural Albedo dataset. In this paper, we propose a novel accurate Albedo Recovery based Fuzzy C-Means (ARFCM) method to cluster HSIs. In the dataset, each feature vector is processed by the Albedo recovery step to create a new feature vector. This new feature vector can describe the dataset better than the original one. Comparing clustering methods as one of the powerful clustering algorithms are widely used in the remote sensing fields of studying. The experiments conducted on several benchmark datasets demonstrated that the proposed clustering method achieves higher performance than other methods and present the efficiency and effectiveness of the proposed method. The results of experiments over different HSI datasets indicated that the proposed method could produce reliable and suitable results compared to the other methods. This shows the robustness of the proposed ARFCM algorithm over the various HSI datasets. Other methods may provide a good response in a given dataset and do not perform well in the other data. Consequently, the ARFCM method, regardless of the study area characteristics and the sensor features, always renders remarkable clustering accuracy. Numéro de notice : A2020-453 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/01431161.2020.1736728 Date de publication en ligne : 01/06/2020 En ligne : https://doi.org/10.1080/01431161.2020.1736728 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95542
in International Journal of Remote Sensing IJRS > vol 41 n° 16 (01-10 May 2020) . - pp 6117 - 6134[article]Incorporating Sentinel-1 SAR imagery with the MODIS MCD64A1 burned area product to improve burn date estimates and reduce burn date uncertainty in wildland fire mapping / Kristofer Lasko in Geocarto international, vol 35 n° 6 ([01/05/2020])
[article]
Titre : Incorporating Sentinel-1 SAR imagery with the MODIS MCD64A1 burned area product to improve burn date estimates and reduce burn date uncertainty in wildland fire mapping Type de document : Article/Communication Auteurs : Kristofer Lasko, Auteur Année de publication : 2020 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] Asie du sud-est
[Termes IGN] bande C
[Termes IGN] carte de la végétation
[Termes IGN] cartographie des risques
[Termes IGN] dynamique de la végétation
[Termes IGN] image Aqua-MODIS
[Termes IGN] image multibande
[Termes IGN] image radar moirée
[Termes IGN] image Sentinel-SAR
[Termes IGN] image Terra-MODIS
[Termes IGN] incendie de forêt
[Termes IGN] incertitude temporelle
[Termes IGN] Laos
[Termes IGN] qualité de l'air
[Termes IGN] Thaïlande
[Termes IGN] zone sinistréeRésumé : (auteur) Wildland fires result in a unique signal detectable by multispectral remote sensing and synthetic aperture radar (SAR). However, in many regions, such as Southeast Asia, persistent cloud cover and aerosols temporarily obstruct multispectral satellite observations of burned area, including the MODIS MCD64A1 Burned Area Product (BAP). Multiple days between cloud free pre- and post-burn MODIS observations result in burn date uncertainty. We incorporate cloud-penetrating, C-band SAR-with the MODIS MCD64A1 BAP in Southeast Asia, to exploit the strengths of each dataset to better estimate the burn date and reduce the potential burn date uncertainty range. We incorporate built-in quality control using MCD64A1 to reduce erroneous pixel updating. We test the method over part of Laos and Thailand during April 2016 and found average uncertainty reduction of 4.5 d, improving 15% of MCD64A1 pixels. A new BAP could improve monitoring temporal trends of wildland fires, air quality studies and monitoring post-fire vegetation dynamics. Numéro de notice : A2020-226 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2019.1608592 Date de publication en ligne : 10/06/2019 En ligne : https://doi.org/https://doi.org/10.1080/10106049.2019.1608592 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94948
in Geocarto international > vol 35 n° 6 [01/05/2020][article]Above-ground biomass estimation and yield prediction in potato by using UAV-based RGB and hyperspectral imaging / Bo Li in ISPRS Journal of photogrammetry and remote sensing, vol 162 (April 2020)
[article]
Titre : Above-ground biomass estimation and yield prediction in potato by using UAV-based RGB and hyperspectral imaging Type de document : Article/Communication Auteurs : Bo Li, Auteur ; Xiangming Xu, Auteur ; Li Zhang, Auteur Année de publication : 2020 Article en page(s) : pp 161 -1 72 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] biomasse aérienne
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] couvert végétal
[Termes IGN] hauteur de la végétation
[Termes IGN] image captée par drone
[Termes IGN] image hyperspectrale
[Termes IGN] image RVB
[Termes IGN] indice de végétation
[Termes IGN] pomme de terre
[Termes IGN] régression des moindres carrés partiels
[Termes IGN] rendement agricoleRésumé : (auteur) Rapid and accurate biomass and yield estimation facilitates efficient plant phenotyping and site-specific crop management. A low altitude unmanned aerial vehicle (UAV) was used to acquire RGB and hyperspectral imaging data for a potato crop canopy at two growth stages to estimate the above-ground biomass and predict crop yield. Field experiments included six cultivars and multiple treatments of nitrogen, potassium, and mixed compound fertilisers. Crop height was estimated using the difference between digital surface model and digital elevation models derived from RGB imagery. Combining with two narrow-band vegetation indices selected by the RReliefF feature selection algorithm. Random Forest regression models demonstrated high prediction accuracy for both fresh and dry above-ground biomass, with a coefficient of determination (r2) > 0.90. Crop yield was predicted using four narrow-band vegetation indices and crop height (r2 = 0.63) with imagery data obtained 90 days after planting. A Partial Least Squares regression model based on the full wavelength spectra demonstrated improved yield prediction (r2 = 0.81). This study demonstrated the merits of UAV-based RGB and hyperspectral imaging for estimating the above-ground biomass and yield of potato crops, which can be used to assist in site-specific crop management. Numéro de notice : A2020-125 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2020.02.013 Date de publication en ligne : 28/02/2020 En ligne : https://doi.org/10.1016/j.isprsjprs.2020.02.013 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94750
in ISPRS Journal of photogrammetry and remote sensing > vol 162 (April 2020) . - pp 161 -1 72[article]Assessment of malaria hazard, vulnerability, and risks in Dire Dawa City Administration of eastern Ethiopia using GIS and remote sensing / Abdinasir Moha in Applied geomatics, vol 12 n° 1 (April 2020)
[article]
Titre : Assessment of malaria hazard, vulnerability, and risks in Dire Dawa City Administration of eastern Ethiopia using GIS and remote sensing Type de document : Article/Communication Auteurs : Abdinasir Moha, Auteur ; Molla Maru, Auteur ; Tebarek Lika, Auteur Année de publication : 2020 Article en page(s) : pp 15 - 22 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] ArcGIS
[Termes IGN] cartographie des risques
[Termes IGN] changement climatique
[Termes IGN] Ethiopie
[Termes IGN] image infrarouge
[Termes IGN] image Landsat-OLI
[Termes IGN] maladie parasitaire
[Termes IGN] risque sanitaire
[Termes IGN] système d'information géographique
[Termes IGN] utilisation du solRésumé : (auteur) Malaria is a serious vector-borne disease affecting a greater proportion of the world’s population. Sub-Saharan Africa carries a disproportionately high share of the global malaria burden. Ethiopia is generally considered a low-to-moderate malaria transmission intensity country. However, the health sector in Ethiopia is greatly affected by climate change, which has profound consequences on the transmission cycles of vector-borne infectious diseases like malaria. The main objective of the study was to assess the spatial distribution of malaria hazard, vulnerability, and risk areas in Dire Dawa City Administration. GIS and remote-sensing in general and multi-criteria evaluation (MCE) in particular was used for assessing and mapping malaria hazard, risk, and vulnerable areas in Dire Dawa City Administration based on the data collected from various sources. The malaria hazard map of the study area labeled 0.6% of the region as low-hazard level, 79.7% moderate, 19.7% high, and 0.1% very low. Results of malaria vulnerability analysis reveal that about 23%, 73%, and 4% of the region was found to be vulnerable to malaria risk at very high, high, and low levels, respectively. The malaria risk map classifies 80% of the region as a moderate malaria-risk area and 20% as high malaria-risk area. This assessment advocates that the GIS and remote-sensing technology as tools can be used to provide timely information on malaria hazard, vulnerability, and risk areas for planning and taking measures at various levels ranging from early warning, monitoring, and control to prevention against malaria epidemics in a resource-efficient and cost-effective way. Numéro de notice : A2020-557 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s12518-019-00276-5 Date de publication en ligne : 17/07/2019 En ligne : https://doi.org/10.1007/s12518-019-00276-5 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95862
in Applied geomatics > vol 12 n° 1 (April 2020) . - pp 15 - 22[article]Detection of Xylella fastidiosa infection symptoms with airborne multispectral and thermal imagery: Assessing bandset reduction performance from hyperspectral analysis / T. Poblete in ISPRS Journal of photogrammetry and remote sensing, vol 162 (April 2020)
[article]
Titre : Detection of Xylella fastidiosa infection symptoms with airborne multispectral and thermal imagery: Assessing bandset reduction performance from hyperspectral analysis Type de document : Article/Communication Auteurs : T. Poblete, Auteur ; C. Camino, Auteur ; P.S.A. Beck, Auteur Année de publication : 2020 Article en page(s) : pp 27 - 40 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] chlorophylle
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] espèce végétale
[Termes IGN] fluorescence
[Termes IGN] image hyperspectrale
[Termes IGN] image multibande
[Termes IGN] image satellite
[Termes IGN] image thermique
[Termes IGN] Italie
[Termes IGN] maladie bactérienne
[Termes IGN] maladie phytosanitaire
[Termes IGN] Olea europaea
[Termes IGN] stress hydrique
[Termes IGN] surveillance de la végétation
[Termes IGN] télédétection aérienne
[Termes IGN] traitement d'imageRésumé : (auteur) Xylella fastidiosa (Xf) is a harmful plant pathogenic bacterium, able to infect over 500 plant species worldwide. Successful eradication and containment strategies for harmful pathogens require large-scale monitoring techniques for the detection of infected hosts, even when they do not display visual symptoms. Although a previous study using airborne hyperspectral and thermal imagery has shown promising results for the early detection of Xf-infected olive (Olea europaea) trees, further work is needed when adopting these techniques for large scale monitoring using multispectral cameras on board airborne platforms and satellites. We used hyperspectral and thermal imagery collected during a two-year airborne campaign in a Xf-infected area in southern Italy to assess the performance of spectrally constrained machine-learning algorithms for this task. The algorithms were used to assess multispectral bandsets, selected from the original hyperspectral imagery, that were compatible with large-scale monitoring from unmanned platforms and manned aircraft. In addition, the contribution of solar–induced chlorophyll fluorescence (SIF) and the temperature-based Crop Water Stress Index (CWSI) retrieved from hyperspectral and thermal imaging, respectively, were evaluated to quantify their relative importance in the algorithms used to detect Xf infection. The detection performance using support vector machine algorithms decreased from ∼80% (kappa, κ = 0.42) when using the original full hyperspectral dataset including SIF and CWSI to ∼74% (κ = 0.36) when the optimal set of six spectral bands most sensitive to Xf infection were used in addition to the CWSI thermal indicator. When neither SIF nor CWSI were used, the detection yielded less than 70% accuracy (decreasing κ to very low performance, 0.29), revealing that tree temperature was more important than chlorophyll fluorescence for the Xf detection. This work demonstrates that large-scale Xf monitoring can be supported using airborne platforms carrying multispectral and thermal cameras with a limited number of spectral bands (e.g., six to 12 bands with 10 nm bandwidths) as long as they are carefully selected by their sensitivity to the Xf symptoms. More precisely, the blue (bands between 400 and 450 nm to derive the NPQI index) and thermal (to derive CWSI from tree temperature) were the most critical spectral regions for their sensitivity to Xf symptoms in olive. Numéro de notice : A2020-120 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2020.02.010 Date de publication en ligne : 18/02/2020 En ligne : https://doi.org/10.1016/j.isprsjprs.2020.02.010 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94745
in ISPRS Journal of photogrammetry and remote sensing > vol 162 (April 2020) . - pp 27 - 40[article]Improving the accuracy of land cover classification in cloud persistent areas using optical and radar satellite image time series / Maylis Lopes in Methods in ecology and evolution, vol 11 n° 4 (April 2020)PermalinkMultichannel Pulse-Coupled Neural Network-Based Hyperspectral Image Visualization / Puhong Duan in IEEE Transactions on geoscience and remote sensing, vol 58 n° 4 (April 2020)PermalinkMultiscale Intensity Propagation to Remove Multiplicative Stripe Noise From Remote Sensing Images / Hao Cui in IEEE Transactions on geoscience and remote sensing, vol 58 n° 4 (April 2020)PermalinkA Single Model CNN for Hyperspectral Image Denoising / Alessandro Maffei in IEEE Transactions on geoscience and remote sensing, vol 58 n° 4 (April 2020)PermalinkWhat, where, and how to transfer in SAR target recognition based on deep CNNs / Zhongling Huang in IEEE Transactions on geoscience and remote sensing, vol 58 n° 4 (April 2020)PermalinkA novel nonlinear hyperspectral unmixing approach for images of oil spills at sea / Ying Li in International Journal of Remote Sensing IJRS, vol 41 n° 12 (20 - 30 March 2020)PermalinkDimension reduction methods applied to coastline extraction on hyperspectral imagery / Ozan Arslan in Geocarto international, vol 35 n° 4 ([15/03/2020])PermalinkAn original method for tree species classification using multitemporal multispectral and hyperspectral satellite data / Olga Grigorieva in Silva fennica, vol 54 n° 2 (March 2020)PermalinkA discriminative tensor representation model for feature extraction and classification of multispectral LiDAR data / Qingwang Wang in IEEE Transactions on geoscience and remote sensing, vol 58 n° 3 (March 2020)PermalinkSimultaneous intensity bias estimation and stripe noise removal in infrared images using the global and local sparsity constraints / Li Liu in IEEE Transactions on geoscience and remote sensing, vol 58 n° 3 (March 2020)Permalink