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Residences information extraction from Landsat imagery using the multi-parameter decision tree method / Yujie Yang in Geocarto international, vol 34 n° 14 ([30/10/2019])  

Public [article]  inGeocarto international > vol 34 n° 14 [30/10/2019] . - pp 1621 - 1633 Titre : Residences information extraction from Landsat imagery using the multi-parameter decision tree method Type de document : Article/Communication Auteurs : Yujie Yang, Auteur ; Shijie Wang, Auteur ; Xiaoyong Bai, Auteur ; et al., Auteur Année de publication : 2019 Article en page(s) : pp 1621 - 1633 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes descripteurs IGN] albedo [Termes descripteurs IGN] analyse spectrale [Termes descripteurs IGN] classification par arbre de décision [Termes descripteurs IGN] détection de changement [Termes descripteurs IGN] détection du bâti [Termes descripteurs IGN] eau [Termes descripteurs IGN] image Landsat-OLI [Termes descripteurs IGN] Normalized Difference Vegetation Index [Termes descripteurs IGN] occupation du sol [Termes descripteurs IGN] ombre [Termes descripteurs IGN] série temporelle [Termes descripteurs IGN] seuillage d'image Résumé : (auteur) The rapid and accurate grasp of changes in residences is crucial for urban planning and urbanisation. However, the traditional methods for extracting residences exists several problems, which lead to inaccurate extraction results. In this study, the Landsat image is used to establish a new method for extracting the residences quickly and accurately. The specific steps are as follows: (1) We calculate surface albedo to exclude the interference of waters and shadows; (2) Using single-band threshold method, we eliminate the interference of shadows; (3) Normalized Difference Vegetation Index is calculated to exclude the effects of vegetation; (4) Roads are removed by calculating the shape index. Verification shows that the accuracy of this extraction method is 92.81%, which is more accurate than the traditional methods and solves the problems existed in the traditional methods. This novel method is a new reference for other land cover research on the technical aspect. Numéro de notice : A2019-528 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2018.1494760 date de publication en ligne : 07/09/2018 En ligne : https://doi.org/10.1080/10106049.2018.1494760 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94106 [article]

Segmenting mangrove ecosystems drone images using SLIC superpixels / Edward Zimudzi in Geocarto international, vol 34 n° 14 ([30/10/2019])  

Public [article]  inGeocarto international > vol 34 n° 14 [30/10/2019] . - pp 1648 - 1662 Titre : Segmenting mangrove ecosystems drone images using SLIC superpixels Type de document : Article/Communication Auteurs : Edward Zimudzi, Auteur ; Ian Sanders, Auteur ; Nicholas Rollings, Auteur ; Christian Omlin, Auteur Année de publication : 2019 Article en page(s) : pp 1648 - 1662 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes descripteurs IGN] algorithme de superpixels [Termes descripteurs IGN] classification par nuées dynamiques [Termes descripteurs IGN] classification pixellaire [Termes descripteurs IGN] écosystème [Termes descripteurs IGN] Fidji [Termes descripteurs IGN] image de drone [Termes descripteurs IGN] mangrove [Termes descripteurs IGN] modèle numérique de surface [Termes descripteurs IGN] orthophotoplan numérique [Termes descripteurs IGN] segmentation d'image [Termes descripteurs IGN] SLIC algorithm Résumé : (auteur) Mangrove ecosystems play a very important ecological role on land–ocean interfaces in tropical regions. These ecosystems comprise of various tree species and aquatic animals, protecting the environment and providing a habitat that supports many living organisms including humans. The identification of image regions in mangrove ecosystems plays a significant role in ecosystem monitoring and conservation. Recent studies have suggested oversegmentation of colour images using superpixels as a solution to the segmentation of image regions. This study used the SLIC superpixel algorithm and k-means clustering to segment images taken from a camera mounted on a drone from a mangrove ecosystem in Fiji. The SLIC superpixel algorithm performed well to demarcate image regions with similar colour and texture information into patches and to use k-means for the segmentation of the whole image. These results lend support to the use of superpixel algorithms for the segmentation of mangrove ecosystems. Understanding how superpixels can be used for the segmentation of drone images will assist conservation efforts in mangrove ecosystems. Numéro de notice : A2019-539 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2018.1497093 date de publication en ligne : 22/10/2018 En ligne : https://doi.org/10.1080/10106049.2018.1497093 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94114 [article]

Optimal segmentation of high spatial resolution images for the classification of buildings using random forests / James Bialas in International journal of applied Earth observation and geoinformation, vol 82 (October 2019)  

Public [article]  inInternational journal of applied Earth observation and geoinformation > vol 82 (October 2019) . - pp Titre : Optimal segmentation of high spatial resolution images for the classification of buildings using random forests Type de document : Article/Communication Auteurs : James Bialas, Auteur ; Thomas Oommen, Auteur ; Timothy C. Havens, Auteur Année de publication : 2019 Article en page(s) : pp Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes descripteurs IGN] analyse d'image orientée objet [Termes descripteurs IGN] apprentissage automatique [Termes descripteurs IGN] bâtiment [Termes descripteurs IGN] Christchurch [Termes descripteurs IGN] classification par forêts aléatoires [Termes descripteurs IGN] dommage matériel [Termes descripteurs IGN] image à haute résolution [Termes descripteurs IGN] image aérienne [Termes descripteurs IGN] précision de la classification [Termes descripteurs IGN] qualité du processus [Termes descripteurs IGN] segmentation d'image [Termes descripteurs IGN] séisme [Termes descripteurs IGN] zone urbaine Résumé : (auteur) In the application of machine learning to geographic object based image analysis, several parameters influence overall classifier performance. One of the first parameters is segmentation size—for example, how many pixels should be grouped together to form an image object. Often, trial and error methods are used to obtain segmentation parameters that best delineate the borders of real world objects. Several attempts at automated methods have produced promising results, but manual intervention is still necessary. Meanwhile, numerous measures of segmentation quality have been defined, but their relationship to classifier performance is not then directly shown. For example, as measures of segmentation quality improve, do classification results improve as well? Our work considers the problem of building classification in high resolution aerial imagery of urban areas. Based on user defined training polygons generated with or without a reference segmentation, we have found several measures of segmentation quality and feature performance that can help users narrow the range of appropriate segmentations. Furthermore, our work finds that given this range, performance of machine learning algorithms remains relatively constant for any given segmentation as long as features used for classification are chosen correctly. We find that the range of scale parameters capable of producing an accurate classification is much broader than typically assumed and trial and error methods for finding this parameter may be an acceptable approach. Numéro de notice : A2019-472 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.jag.2019.06.005 date de publication en ligne : 08/06/2019 En ligne : https://doi.org/https://doi.org/10.1016/j.jag.2019.06.005 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93632 [article]

Mapping of forest tree distribution and estimation of forest biodiversity using Sentinel-2 imagery in the University Research Forest Taxiarchis in Chalkidiki, Greece / Maria Kampouri in Geocarto international, vol 34 n° 12 ([15/09/2019])  

Public [article]  inGeocarto international > vol 34 n° 12 [15/09/2019] . - pp 1273 - 1285 Titre : Mapping of forest tree distribution and estimation of forest biodiversity using Sentinel-2 imagery in the University Research Forest Taxiarchis in Chalkidiki, Greece Type de document : Article/Communication Auteurs : Maria Kampouri, Auteur ; Polychronis Kolokoussis, Auteur ; Demetre Argialas, Auteur ; Vassilia Karathanassi, Auteur Année de publication : 2019 Article en page(s) : pp 1273 - 1285 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes descripteurs IGN] analyse d'image orientée objet [Termes descripteurs IGN] biomasse forestière [Termes descripteurs IGN] conservation des ressources forestières [Termes descripteurs IGN] écosystème forestier [Termes descripteurs IGN] espèce végétale [Termes descripteurs IGN] Grèce [Termes descripteurs IGN] image multibande [Termes descripteurs IGN] image multitemporelle [Termes descripteurs IGN] image Sentinel-MSI [Termes descripteurs IGN] indicateur de biodiversité [Termes descripteurs IGN] indice de diversité [Termes descripteurs IGN] modèle numérique de surface [Termes descripteurs IGN] réalité de terrain [Termes descripteurs IGN] segmentation d'image Résumé : (Auteur) The aim of this study is to investigate the potential of Sentinel-2 imagery for the identification and determination of forest patches of particular interest, with respect to ecosystem integrity and biodiversity and to produce a relevant biodiversity map, based on Simpson’s diversity index in Taxiarchis university research forest, Chalkidiki, North Greece. The research is based on OBIA being developed on to bi-temporal summer and winter Sentinel-2 imagery. Fuzzy rules, which are based on topographic factors, such as terrain elevation and slope for the distribution of each tree species, derived from expert knowledge and field observations, were used to improve the accuracy of tree species classification. Finally, Simpson’s diversity index for forest tree species, was calculated and mapped, constituting a relative indicator for biodiversity for forest ecosystem organisms (fungi, insects, birds, reptiles, mammals) and carrying implications for the identification of patches prone to disturbance or that should be prioritized for conservation. Numéro de notice : A2019-465 Thématique : BIODIVERSITE/FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2018.1489424 date de publication en ligne : 12/09/2018 En ligne : https://doi.org/10.1080/10106049.2018.1489424 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93616 [article]

Development and evaluation of a deep learning model for real-time ground vehicle semantic segmentation from UAV-based thermal infrared imagery / Mehdi Khoshboresh Masouleh in ISPRS Journal of photogrammetry and remote sensing, vol 155 (September 2019)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 155 (September 2019) . - pp 172 - 186 Titre : Development and evaluation of a deep learning model for real-time ground vehicle semantic segmentation from UAV-based thermal infrared imagery Type de document : Article/Communication Auteurs : Mehdi Khoshboresh Masouleh, Auteur ; Reza Shah-Hosseini, Auteur Année de publication : 2019 Article en page(s) : pp 172 - 186 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes descripteurs IGN] apprentissage profond [Termes descripteurs IGN] détection d'objet [Termes descripteurs IGN] image de drone [Termes descripteurs IGN] image RVB [Termes descripteurs IGN] image thermique [Termes descripteurs IGN] segmentation d'image [Termes descripteurs IGN] segmentation sémantique [Termes descripteurs IGN] véhicule automobile Résumé : (Auteur) Real-time unmanned aerial vehicles (UAVs)-based thermal infrared images processing, due to high spatial resolution and knowledge of the various infrared radiant energy level distribution of solid bodies, has important applications such as monitoring and control of the various phenomena in different natural situations. One of these applications is monitoring the ground vehicles in cities by using detection or semantic segmentation of them in the thermal images. In this research, our purpose is to improve the performance of deep learning combined model by using Gaussian-Bernoulli Restricted Boltzmann Machine (GB-RBM) specifications for the segmentation of the ground vehicles from UAV-based thermal infrared imagery. The proposed model is studied in three steps. First, designing the proposed model by using an encoder-decoder structure and addition of extracted features from convolutional layers and restricted Boltzmann machine in the network. Second, the implementation of the research goals on four sets of UAV-based thermal infrared imagery named NPU_CS_UAV_IR_DATA that was collected from some streets of China by using FLIR TAU2 thermal infrared sensor in 2017. Finally, analyzing the performance of the proposed model by using five state-of-the-art models in semantic segmentation. The results evaluated the performance of the proposed model as a robust model with the average precision and average processing time of approximately 0.97, and 19.73 s for all datasets, respectively. Numéro de notice : A2019-315 Thématique : IMAGERIE Nature : Article DOI : 10.1016/j.isprsjprs.2019.07.009 date de publication en ligne : 25/07/2019 En ligne : https://doi.org/10.1016/j.isprsjprs.2019.07.009 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93341 [article]

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