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Exploring fuzzy local spatial information algorithms for remote sensing image classification / Anjali Madhu in Remote sensing, vol 13 n° 20 (October-2 2021)  

Public [article]  inRemote sensing > vol 13 n° 20 (October-2 2021) . - n° 4163 Titre : Exploring fuzzy local spatial information algorithms for remote sensing image classification Type de document : Article/Communication Auteurs : Anjali Madhu, Auteur ; Anil Kumar, Auteur ; Peng Jia, Auteur Année de publication : 2021 Article en page(s) : n° 4163 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse de groupement [Termes IGN] classification dirigée [Termes IGN] classification floue [Termes IGN] classification pixellaire [Termes IGN] distance euclidienne [Termes IGN] erreur moyenne quadratique [Termes IGN] Inde [Termes IGN] matrice d'erreur [Termes IGN] occupation du sol [Termes IGN] théorie des possibilités Résumé : (auteur) Fuzzy c-means (FCM) and possibilistic c-means (PCM) are two commonly used fuzzy clustering algorithms for extracting land use land cover (LULC) information from satellite images. However, these algorithms use only spectral or grey-level information of pixels for clustering and ignore their spatial correlation. Different variants of the FCM algorithm have emerged recently that utilize local spatial information in addition to spectral information for clustering. Such algorithms are seen to generate clustering outputs that are more enhanced than the classical spectral-based FCM algorithm. Nonetheless, the scope of integrating spatial contextual information with the conventional PCM algorithm, which has several advantages over the FCM algorithm for supervised classification, has not been explored much. This study proposed integrating local spatial information with the PCM algorithm using simpler but proven approaches from available FCM-based local spatial information algorithms. The three new PCM-based local spatial information algorithms: Possibilistic c-means with spatial constraints (PCM-S), possibilistic local information c-means (PLICM), and adaptive possibilistic local information c-means (ADPLICM) algorithms, were developed corresponding to the available fuzzy c-means with spatial constraints (FCM-S), fuzzy local information c-means (FLICM), and adaptive fuzzy local information c-means (ADFLICM) algorithms. Experiments were conducted to analyze and compare the FCM and PCM classifier variants for supervised LULC classifications in soft (fuzzy) mode. The quantitative assessment of the soft classification results from fuzzy error matrix (FERM) and root mean square error (RMSE) suggested that the new PCM-based local spatial information classifiers produced higher accuracies than the PCM, FCM, or its local spatial variants, in the presence of untrained classes and noise. The promising results from PCM-based local spatial information classifiers suggest that the PCM algorithm, which is known to be naturally robust to noise, when integrated with local spatial information, has the potential to result in more efficient classifiers capable of better handling ambiguities caused by spectral confusions in landscapes. Numéro de notice : A2021-806 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.3390/rs13204163 Date de publication en ligne : 18/10/2021 En ligne : https://doi.org/10.3390/rs13204163 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98864 [article]

Demystifying Internet of Things security / Sunil Cheruvu (2020)  

Public Titre : Demystifying Internet of Things security : successful IoT Device/Edge and platform security deployment Type de document : Monographie Auteurs : Sunil Cheruvu, Éditeur scientifique ; Anil Kumar, Éditeur scientifique ; Ned Smith, Éditeur scientifique ; David M. Wheeler, Éditeur scientifique Editeur : New York : Apress Année de publication : 2020 Importance : 488 p. ISBN/ISSN/EAN : 978-1-4842-2895-1 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Télématique [Termes IGN] internet des objets [Termes IGN] réseau informatique [Termes IGN] sécurité informatique Résumé : (Editeur) [Introduction] Break down the misconceptions of the Internet of Things by examining the different security building blocks available in Intel Architecture (IA) based IoT platforms. This open access book reviews the threat pyramid, secure boot, chain of trust, and the SW stack leading up to defense-in-depth. The IoT presents unique challenges in implementing security and Intel has both CPU and Isolated Security Engine capabilities to simplify it. This book explores the challenges to secure these devices to make them immune to different threats originating from within and outside the network. The requirements and robustness rules to protect the assets vary greatly and there is no single blanket solution approach to implement security. This book provides clarity to industry professionals and provides and overview of different security solutions. Note de contenu : Introduction CHAPTER 1 - Conceptualizing the Secure Internet of Things CHAPTER 2 - IoT Frameworks and Complexity CHAPTER 3 - Base Platform Security Hardware Building Blocks CHAPTER 4 - IoT Software Security Building Blocks CHAPTER 5 - Connectivity Technologies for IoT CHAPTER 6 - IoT Vertical Applications and Associated Security Requirements Conclusion Numéro de notice : 26801 Affiliation des auteurs : non IGN Thématique : INFORMATIQUE/SOCIETE NUMERIQUE Nature : Monographie DOI : 10.1007/978-1-4842-2896-8 En ligne : https://doi.org/10.1007/978-1-4842-2896-8 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100094

Temporal MODIS data for identification of wheat crop using noise clustering soft classification approach / Priyadarshi Upadhyay in Geocarto international, vol 31 n° 3 - 4 (March - April 2016)  

Public [article]  inGeocarto international > vol 31 n° 3 - 4 (March - April 2016) . - pp 278 - 295 Titre : Temporal MODIS data for identification of wheat crop using noise clustering soft classification approach Type de document : Article/Communication Auteurs : Priyadarshi Upadhyay, Auteur ; Sanjay Kumar Ghosh, Auteur ; Anil Kumar, Auteur Année de publication : 2016 Article en page(s) : pp 278 - 295 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] analyse diachronique [Termes IGN] blé (céréale) [Termes IGN] bruit rose [Termes IGN] classification automatique [Termes IGN] croissance végétale [Termes IGN] image Terra-MODIS [Termes IGN] indice de végétation [Termes IGN] surveillance agricole Résumé : (Auteur) In this study, temporal MODIS-Terra MOD13Q1 data have been used for identification of wheat crop uniquely, using the noise clustering (NC) soft classification approach. This research also optimises the selection of date combination and vegetation index for classification of wheat crop. First, a separability analysis is used to optimise the date combination for each case of number of dates and vegetation index. Then, these scenes have undergone for NC soft classification. The resolution parameter (δ) was optimised for the NC classifier and found to be a value of 1.6 × 104 for wheat crop identification. Classified outputs were analysed by receiver operating characteristics (ROC) analysis for sub-pixel detection. Highest area under the ROC curve was found for soil-adjusted vegetation index corresponding to the three different phenological stages data sets. From this study, the data sets corresponding to the Sowing, Flowering and Maturity phenological stages of wheat crop were found more suitable to identify it uniquely. Numéro de notice : A2016-159 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2015.1047415 Date de publication en ligne : 26/05/2015 En ligne : http://www.tandfonline.com/doi/full/10.1080/10106049.2015.1047415 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=80381 [article]

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059-2016021	RAB	Revue	Centre de documentation	En réserve L003	Disponible