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Binary patterns encoded convolutional neural networks for texture recognition and remote sensing scene classification / Rama Rao Nidamanuri in ISPRS Journal of photogrammetry and remote sensing, vol 138 (April 2018)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 138 (April 2018) . - pp 74 - 85 Titre : Binary patterns encoded convolutional neural networks for texture recognition and remote sensing scene classification Type de document : Article/Communication Auteurs : Rama Rao Nidamanuri, Auteur ; Fahad Shahbaz Khan, Auteur ; Joost van de Weijer, Auteur ; Matthieu Molinier, Auteur ; Jorma Laaksonen, Auteur Année de publication : 2018 Article en page(s) : pp 74 - 85 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse texturale [Termes IGN] apprentissage profond [Termes IGN] classification [Termes IGN] image RVB [Termes IGN] motif binaire local [Termes IGN] réseau neuronal convolutif [Termes IGN] texture d'image Résumé : (Auteur) Designing discriminative powerful texture features robust to realistic imaging conditions is a challenging computer vision problem with many applications, including material recognition and analysis of satellite or aerial imagery. In the past, most texture description approaches were based on dense orderless statistical distribution of local features. However, most recent approaches to texture recognition and remote sensing scene classification are based on Convolutional Neural Networks (CNNs). The de facto practice when learning these CNN models is to use RGB patches as input with training performed on large amounts of labeled data (ImageNet). In this paper, we show that Local Binary Patterns (LBP) encoded CNN models, codenamed TEX-Nets, trained using mapped coded images with explicit LBP based texture information provide complementary information to the standard RGB deep models. Additionally, two deep architectures, namely early and late fusion, are investigated to combine the texture and color information. To the best of our knowledge, we are the first to investigate Binary Patterns encoded CNNs and different deep network fusion architectures for texture recognition and remote sensing scene classification. We perform comprehensive experiments on four texture recognition datasets and four remote sensing scene classification benchmarks: UC-Merced with 21 scene categories, WHU-RS19 with 19 scene classes, RSSCN7 with 7 categories and the recently introduced large scale aerial image dataset (AID) with 30 aerial scene types. We demonstrate that TEX-Nets provide complementary information to standard RGB deep model of the same network architecture. Our late fusion TEX-Net architecture always improves the overall performance compared to the standard RGB network on both recognition problems. Furthermore, our final combination leads to consistent improvement over the state-of-the-art for remote sensing scene classification. Numéro de notice : A2018-121 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2018.01.023 Date de publication en ligne : 15/02/2018 En ligne : https://doi.org/10.1016/j.isprsjprs.2018.01.023 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=89590 [article]

Exemplaires(3)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
081-2018041	RAB	Revue	Centre de documentation	En réserve L003	Disponible
081-2018043	DEP-EXM	Revue	LASTIG	Dépôt en unité	Exclu du prêt
081-2018042	DEP-EAF	Revue	Nancy	Dépôt en unité	Exclu du prêt

Active learning-based optimized training library generation for object-oriented image classification / Rajeswari Balasubramaniam in IEEE Transactions on geoscience and remote sensing, vol 56 n° 1 (January 2018)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 56 n° 1 (January 2018) . - pp 575 - 585 Titre : Active learning-based optimized training library generation for object-oriented image classification Type de document : Article/Communication Auteurs : Rajeswari Balasubramaniam, Auteur ; Srivalsan Namboodiri, Auteur ; Rama Rao Nidamanuri, Auteur ; Rama Krishna Sai Subrahmanyam Gorthi, Auteur Année de publication : 2018 Article en page(s) : pp 575 - 585 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse d'image orientée objet [Termes IGN] apprentissage dirigé [Termes IGN] classification par séparateurs à vaste marge [Termes IGN] image aérienne [Termes IGN] image multibande Résumé : (Auteur) In this paper, we introduce an active learning (AL)-based object training library generation for a multiclassifier object-oriented image analysis (OOIA) system. While several AL approaches do exist for pixel-based training library generation and for hyperspectral image classification, there is no standard training library generation strategy for OOIA of very high spatial resolution images. Given a sufficient number of training samples, supervised classification is the method of choice for image classification. However, this strategy becomes computationally expensive with the increase in the number of classes or the number of images to be classified. The above-mentioned issue is solved in this proposed method, where an optimized training library of objects (superpixels) is generated based on a batch mode AL approach. A softmax classifier is used as a detector in this method, which helps in determining the right samples to be chosen for library updation. To this end, we construct a multiclassifier system with max-voting decision to classify an image at pixel level. This algorithm was applied on three different very high-resolution airborne data sets, each with varying complexity in terms of variations in geographical context, sensors, illumination, and view angles. Our method has empirically outperformed the traditional OOIA by producing equivalent accuracy with a training library that is orders of magnitude smaller. In addition, the most distinctive ability of the algorithm is experienced in the most heterogeneous data set, where its performance in terms of accuracy is around twice the performance of the traditional method in the same situation. The generality of this classification strategy is proved through its performance on multispectral images and for cross-domain application. Finally, the robustness of this method is identified by comparing its performance with an alternative AL approach-self-learning-based semisupervised SVM. The capability of the proposed method to handle highly heterogeneous data is identified as the primary reason for its robustness. Numéro de notice : A2018-188 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2017.2751568 Date de publication en ligne : 29/09/2017 En ligne : https://doi.org/10.1109/TGRS.2017.2751568 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=89847 [article]

Multiple spectral similarity metrics for surface materials identification using hyperspectral data / Rama Rao Nidamanuri in Geocarto international, vol 31 n° 7 - 8 (July - August 2016)  

Public [article]  inGeocarto international > vol 31 n° 7 - 8 (July - August 2016) . - pp 845 - 859 Titre : Multiple spectral similarity metrics for surface materials identification using hyperspectral data Type de document : Article/Communication Auteurs : Rama Rao Nidamanuri, Auteur Année de publication : 2016 Article en page(s) : pp 845 - 859 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] classification par séparateurs à vaste marge [Termes IGN] classification spectrale [Termes IGN] image hyperspectrale [Termes IGN] limite de résolution spectrale [Termes IGN] pouvoir de résolution spectrale [Termes IGN] similitude spectrale Résumé : (Auteur) Modern hyperspectral imaging and non-imaging spectroradiometer has the capability to acquire high-resolution spectral reflectance data required for surface materials identification and mapping. Spectral similarity metrics, due to their mathematical simplicity and insensitiveness to the number of reference labelled spectra, have been increasingly used for material mapping by labelling reflectance spectra in hyperspectral data labelling. For a particular hyperspectral data set, the accuracy of spectral labelling depends considerably upon the degree of unambiguous spectral matching achieved by the spectral similarity metric used. In this work, we propose a new methodology for quantifying spectral similarity for hyperspectral data labelling for surface materials identification. Developed adopting the multiple classifier system architecture, the proposed methodology unifies into a single framework the differential performances of eight different spectral similarity metrics for the quantification of spectral matching for surface materials. The proposed methodology has been implemented on two types of hyperspectral data viz. image (airborne hyperspectral images) and non-image (library spectra) for numerous surface materials identification. Further, the performance of the proposed methodology has been compared with the support vector machines (SVM) approach, and with all the base spectral similarity metrics. The results indicate that, for the hyperspectral images, the performance of the proposed methodology is comparable with that of the SVM. For the library spectra, the proposed methodology shows a consistently higher (increase of about 30% when compared to SVM) classification accuracy. The proposed methodology has the potential to serve as a general library search method for materials identification using hyperspectral data. Numéro de notice : A2016-457 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2015.1086903 Date de publication en ligne : 30/09/2015 En ligne : http://dx.doi.org/10.1080/10106049.2015.1086903 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=81381 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
059-2016041	RAB	Revue	Centre de documentation	En réserve L003	Disponible

Object-oriented semantic labelling of spectral–spatial LiDAR point cloud for urban land cover classification and buildings detection / Anandakumar M. Ramiya in Geocarto international, vol 31 n° 1 - 2 (January - February 2016)  

Public [article]  inGeocarto international > vol 31 n° 1 - 2 (January - February 2016) . - pp 121 - 139 Titre : Object-oriented semantic labelling of spectral–spatial LiDAR point cloud for urban land cover classification and buildings detection Type de document : Article/Communication Auteurs : Anandakumar M. Ramiya, Auteur ; Rama Rao Nidamanuri, Auteur ; R. Krishnan, Auteur Année de publication : 2016 Article en page(s) : pp 121 - 139 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques [Termes IGN] classificateur [Termes IGN] détection de partie cachée [Termes IGN] détection du bâti [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] image multibande [Termes IGN] milieu urbain [Termes IGN] occupation du sol [Termes IGN] reconstruction 3D du bâti [Termes IGN] semis de points Résumé : (Auteur) The urban land cover mapping and automated extraction of building boundaries is a crucial step in generating three-dimensional city models. This study proposes an object-based point cloud labelling technique to semantically label light detection and ranging (LiDAR) data captured over an urban scene. Spectral data from multispectral images are also used to complement the geometrical information from LiDAR data. Initial object primitives are created using a modified colour-based region growing technique. Multiple classifier system is then applied on the features extracted from the segments for classification and also for reducing the subjectivity involved in the selection of classifier and improving the precision of the results. The proposed methodology produces two outputs: (i) urban land cover classes and (ii) buildings masks which are further reconstructed and vectorized into three-dimensional buildings footprints. Experiments carried out on three airborne LiDAR datasets show that the proposed technique successfully discriminates urban land covers and detect urban buildings. Numéro de notice : A2016-106 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2015.1034195 Date de publication en ligne : 06/05/2015 En ligne : http://www.tandfonline.com/doi/full/10.1080/10106049.2015.1034195 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=80001 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
059-2016011	RAB	Revue	Centre de documentation	En réserve L003	Disponible

Spectral identification of materials by reflectance spectral library search / Rama Rao Nidamanuri in Geocarto international, vol 29 n° 5 - 6 (August - October 2014)  

Public [article]  inGeocarto international > vol 29 n° 5 - 6 (August - October 2014) . - pp 609-624 Titre : Spectral identification of materials by reflectance spectral library search Type de document : Article/Communication Auteurs : Rama Rao Nidamanuri, Auteur ; A. M. Ramiya, Auteur Année de publication : 2014 Article en page(s) : pp 609-624 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Télédétection [Termes IGN] appariement spectral [Termes IGN] image hyperspectrale [Termes IGN] réflectance spectrale [Termes IGN] signature spectrale Résumé : (auteur) Spectral library search is emerging as a viable approach for material identification and mapping by reusing spectral knowledge gained from hyperspectral remote sensing across space and time. The potential of retrieving meaningful spectral material identifications in the presence of reflectance of spectra of various material types and with various similarity metrics has been assessed in this study. Test reflectance spectra of various vegetation, minerals, soils and urban material types are identified by searching through the composite reflectance spectral library obtained by combining various institutional reflectance spectral libraries. The accuracy of material identifications under various conditions: (i) in the presence of identical, similar and dissimilar spectra; (ii) in the presence of only identical and dissimilar spectra; and (iii) in the presence of only dissimilar spectra has been assessed with several similarity metrics. Results indicate the possibility of obtaining 100% accurate material identifications by library search if the spectral library contains identical spectra. However, the presence of a large number of similar spectra, despite the presence of identical spectra, is found to increase false positives, thereby reducing the accuracy of retrievals to 82% at best. Further, the accuracy of material identifications in the presence of similar spectra is similarity metric-dependent and varied from about 52% (obtained from Binary Encoding) to 82% (obtained from Normalized Spectral Similarity Score). Overall, results support the possibility of using independent reflectance spectral libraries for material identification while calling for robust spectral similarity metrics. Numéro de notice : A2014-418 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2013.821175 En ligne : https://doi.org/10.1080/10106049.2013.821175 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=73953 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
059-2014031	RAB	Revue	Centre de documentation	En réserve L003	Disponible

Spectral material mapping using hyperspectral imagery : a review of spectral matching and library search methods / Sennaraj Vishnu in Geocarto international, vol 28 n° 1-2 (February - May 2013)  

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