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Bayesian transfer learning for object detection in optical remote sensing images / Changsheng Zhou in IEEE Transactions on geoscience and remote sensing, vol 58 n° 11 (November 2020)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 58 n° 11 (November 2020) . - pp 7705 - 7719 Titre : Bayesian transfer learning for object detection in optical remote sensing images Type de document : Article/Communication Auteurs : Changsheng Zhou, Auteur ; Jiangshe Zhang, Auteur ; Junmin Liu, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 7705 - 7719 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes descripteurs IGN] apprentissage profond [Termes descripteurs IGN] chaîne de traitement [Termes descripteurs IGN] détection d'objet [Termes descripteurs IGN] distribution de Fisher [Termes descripteurs IGN] jeu de données localisées [Termes descripteurs IGN] théorème de Bayes Résumé : (auteur) In the literature of object detection in optical remote sensing images, a popular pipeline is first modifying an off-the-shelf deep neural network, then initializing the modified network by pretrained weights on a source data set, and finally fine-tuning the network on a target data set. The procedure works well in practice but might not make full use of underlying knowledge implied by pretrained weights. In this article, we propose a novel method, referred to as Fisher regularization, for efficient knowledge transferring. Based on Bayes’ theorem, the method stores underlying knowledge into a Fisher information matrix and fine-tunes parameters based on the knowledge. The proposed method would not introduce extra parameters and is less sensitive to hyperparameters than classical weight decay. Experiments on NWPUVHR-10 and DOTA data sets show that the proposed method is effective and works well with different object detectors. Numéro de notice : A2020-679 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2020.2983201 date de publication en ligne : 14/04/2020 En ligne : https://doi.org/10.1109/TGRS.2020.2983201 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96182 [article]

Ship detection in SAR images via local contrast of Fisher vectors / Xueqian Wang in IEEE Transactions on geoscience and remote sensing, vol 58 n° 9 (September 2020)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 58 n° 9 (September 2020) . - pp 6467 - 6479 Titre : Ship detection in SAR images via local contrast of Fisher vectors Type de document : Article/Communication Auteurs : Xueqian Wang, Auteur ; Gang Li, Auteur ; Xiao-Ping Zhang, Auteur ; You He, Auteur Année de publication : 2020 Article en page(s) : pp 6467 - 6479 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications [Termes descripteurs IGN] algorithme de superpixels [Termes descripteurs IGN] contraste local [Termes descripteurs IGN] détection d'objet [Termes descripteurs IGN] détection de cible [Termes descripteurs IGN] distribution de Fisher [Termes descripteurs IGN] fouillis d'échos [Termes descripteurs IGN] image radar moirée [Termes descripteurs IGN] navire [Termes descripteurs IGN] processus gaussien [Termes descripteurs IGN] rapport signal sur bruit Résumé : (auteur) Existing superpixel-based detection algorithms for ship targets in synthetic aperture radar (SAR) images are often derived from the local contrast of intensities (i.e., the local contrast of the first-order information of superpixels) leading to deteriorating performance in low signal-to-clutter ratio (SCR) cases due to the low contrast between the intensities of targets and the clutter. In this article, we propose a new superpixel-based detector to improve the performance of ship target detection in SAR images via the local contrast of fisher vectors (LCFVs). The new LCFV-based detector exploits multiorder features of the superpixels based on the Gaussian mixture model (GMM) and accordingly improves the discrimination capability between the ship targets and the sea clutter, especially in low SCR cases. Experimental results demonstrate that the proposed LCFV-based detection algorithm provides better detection performance than the commonly used detection algorithms. Numéro de notice : A2020-530 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2020.2976880 date de publication en ligne : 18/03/2020 En ligne : https://doi.org/10.1109/TGRS.2020.2976880 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95713 [article]

Can ensemble techniques improve coral reef habitat classification accuracy using multispectral data? / Mohammad Shawkat Hossain in Geocarto international, vol 35 n° 11 ([01/08/2020])  

Public [article]  inGeocarto international > vol 35 n° 11 [01/08/2020] . - pp 214 - 1232 Titre : Can ensemble techniques improve coral reef habitat classification accuracy using multispectral data? Type de document : Article/Communication Auteurs : Mohammad Shawkat Hossain, Auteur ; Aidy M. Muslim, Auteur ; Muhammad Izuan Nadzri, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 214 - 1232 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes descripteurs IGN] biodiversité [Termes descripteurs IGN] carte bathymétrique [Termes descripteurs IGN] Chine, mer de [Termes descripteurs IGN] classification barycentrique [Termes descripteurs IGN] classification hypercube [Termes descripteurs IGN] classification par maximum de vraisemblance [Termes descripteurs IGN] distribution de Fisher [Termes descripteurs IGN] fond marin [Termes descripteurs IGN] image multibande [Termes descripteurs IGN] Malaisie [Termes descripteurs IGN] précision de la classification [Termes descripteurs IGN] récif corallien [Termes descripteurs IGN] réflectance spectrale Résumé : (auteur) Remote sensing has potential in studies of the benthic habitat and extracting the reflectance from the data of multispectral sensors, but traditional image classification techniques cannot provide coral habitat maps with adequate accuracy. This study tested five traditional and three ensemble classification techniques on QuickBird for mapping the benthic composition of coral reefs on the Lang Tengah Island (Malaysia). The common techniques, minimum distance, maximum likelihood, K-nearest neighbour, Fisher and parallelepiped techniques were compared with ensemble classifiers, such as majority voting (MV), simple averaging, and mode combination. The per-class accuracy of the habitat detection improved in the ensemble classifiers; in particular, the MV classifier achieved 95%, 65%, 75% and 95% accuracies for coral, sparse coral, coral rubble and sand, respectively. Ensembles increased the accuracy of the habitat mapping classification by 28%, relative to conventional techniques. Thus, the ensemble techniques can be preferred over the traditional for benthic habitat mapping. Numéro de notice : A2020-459 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2018.1557263 date de publication en ligne : 12/02/2019 En ligne : https://doi.org/10.1080/10106049.2018.1557263 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95566 [article]

Spatially sensitive statistical shape analysis for pedestrian recognition from LIDAR data / Michalis A. Savelonas in Computer Vision and image understanding, vol 171 (June 2018)  

Public [article]  inComputer Vision and image understanding > vol 171 (June 2018) . - pp 1 - 9 Titre : Spatially sensitive statistical shape analysis for pedestrian recognition from LIDAR data Type de document : Article/Communication Auteurs : Michalis A. Savelonas, Auteur ; Ioannis Pratikakis, Auteur ; Theoharis Theoharis, Auteur ; Georgios Thanellas, Auteur ; et al., Auteur Année de publication : 2018 Article en page(s) : pp 1 - 9 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes descripteurs IGN] analyse comparative [Termes descripteurs IGN] analyse de sensibilité [Termes descripteurs IGN] analyse spatiale [Termes descripteurs IGN] classification barycentrique [Termes descripteurs IGN] classification par séparateurs à vaste marge [Termes descripteurs IGN] codage [Termes descripteurs IGN] détection de piéton [Termes descripteurs IGN] discrétisation spatiale [Termes descripteurs IGN] distribution de Fisher [Termes descripteurs IGN] données lidar [Termes descripteurs IGN] échantillonnage de données [Termes descripteurs IGN] image à basse résolution [Termes descripteurs IGN] reconnaissance de formes Résumé : (auteur) Range-based pedestrian recognition is instrumental towards the development of autonomous driving and driving assistance systems. This work introduces encoding methods for pedestrian recognition, based on statistical shape analysis of 3D LIDAR data. The proposed approach has two variants, based on the encoding of local shape descriptors either in a spatially agnostic or spatially sensitive fashion. The latter method derives more detailed cues, by enriching the ‘gross’ information reflected by overall statistics of local shape descriptors, with ‘fine-grained’ information reflected by statistics associated with spatial clusters. Experiments on artificial LIDAR datasets, which include challenging samples, as well as on a large scale dataset of real LIDAR data, lead to the conclusion that both variants of the proposed approach (i) obtain high recognition accuracy, (ii) are robust against low-resolution sampling, (iii) are robust against increasing distance, and (iv) are robust against non-standard shapes and poses. On the other hand, the spatially-sensitive variant is more robust against partial occlusion and bad clustering. Numéro de notice : A2018-586 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.cviu.2018.06.001 date de publication en ligne : 15/06/2018 En ligne : https://www.sciencedirect.com/science/article/pii/S1077314218300766 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92439 [article]

Investigation of automatic feature weighting methods (Fisher, Chi-square and Relief-F) for landslide susceptibility mapping / Emrehan Kutlug Sahin in Geocarto international, vol 32 n° 9 (September 2017)  

Public [article]  inGeocarto international > vol 32 n° 9 (September 2017) . - pp 956 - 977 Titre : Investigation of automatic feature weighting methods (Fisher, Chi-square and Relief-F) for landslide susceptibility mapping Type de document : Article/Communication Auteurs : Emrehan Kutlug Sahin, Auteur ; Cengizhan Ipbuker, Auteur ; Taskin Kavzoglu, Auteur Année de publication : 2017 Article en page(s) : pp 956 - 977 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Cartographie thématique [Termes descripteurs IGN] analyse comparative [Termes descripteurs IGN] cartographie des risques [Termes descripteurs IGN] distribution de Fisher [Termes descripteurs IGN] effondrement de terrain [Termes descripteurs IGN] khi carré [Termes descripteurs IGN] pondération [Termes descripteurs IGN] processus d'analyse hiérarchisée [Termes descripteurs IGN] risque naturel [Termes descripteurs IGN] surveillance géologique [Termes descripteurs IGN] test de performance [Termes descripteurs IGN] vulnérabilité Résumé : (Auteur) In landslide susceptibility mapping, factor weights have been usually determined by expert judgements. A novel methodology for weighting landslide causative factors by integrating statistical feature weighting algorithms was proposed. The primary focus of this study is to investigate the effectiveness of automatic feature weighting algorithms, namely Fisher, Chi-square and Relief-F algorithms. Analytic hierarchy process (AHP) method was used as a benchmark method to compare the performances of the weighting algorithms. All weighted factors were tested using factor-weighted overlay method, and quality of these maps was assessed using overall accuracy, area under the ROC curve (AUC) and success rate curve. In addition, Wilcoxon’s signed-rank test was applied to evaluate statistical differences between both estimated overall accuracies and AUCs, respectively. Results showed that the weights determined by feature weighting methods outperformed the conventional AHP method by about 6% and this level of differences was found to be statistically significant. Numéro de notice : A2017-458 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2016.1170892 date de publication en ligne : 11/04/2016 En ligne : http://dx.doi.org/10.1080/10106049.2016.1170892 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=86383 [article]

A cost-effective semisupervised classifier approach with kernels / M. Murat Dundar in IEEE Transactions on geoscience and remote sensing, vol 42 n° 1 (January 2004)

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Theory of errors and generalized matrix inverses / A. Bjerhammar (1973)

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