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Dictionary learning-based feature-level domain adaptation for cross-scene hyperspectral image classification / Minchao Ye in IEEE Transactions on geoscience and remote sensing, vol 55 n° 3 (March 2017)
[article]
Titre : Dictionary learning-based feature-level domain adaptation for cross-scene hyperspectral image classification Type de document : Article/Communication Auteurs : Minchao Ye, Auteur ; Yuntao Qian, Auteur ; Jun Zhou, Auteur ; Yuan Yan Tang, Auteur Année de publication : 2017 Article en page(s) : pp 1544 - 1562 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] apprentissage dirigé
[Termes IGN] classification dirigée
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] image hyperspectrale
[Termes IGN] occupation du sol
[Termes IGN] régression logistiqueRésumé : (Auteur) A big challenge of hyperspectral image (HSI) classification is the small size of labeled pixels for training classifier. In real remote sensing applications, we always face the situation that an HSI scene is not labeled at all, or is with very limited number of labeled pixels, but we have sufficient labeled pixels in another HSI scene with the similar land cover classes. In this paper, we try to classify an HSI scene containing no labeled sample or only a few labeled samples with the help of a similar HSI scene having a relative large size of labeled samples. The former scene is defined as the target scene, while the latter one is the source scene. We name this classification problem as cross-scene classification. The main challenge of cross-scene classification is spectral shift, i.e., even for the same class in different scenes, their spectral distributions maybe have significant deviation. As all or most training samples are drawn from the source scene, while the prediction is performed in the target scene, the difference in spectral distribution would greatly deteriorate the classification performance. To solve this problem, we propose a dictionary learning-based feature-level domain adaptation technique, which aligns the spectral distributions between source and target scenes by projecting their spectral features into a shared low-dimensional embedding space by multitask dictionary learning. The basis atoms in the learned dictionary represent the common spectral components, which span a cross-scene feature space to minimize the effect of spectral shift. After the HSIs of two scenes are transformed into the shared space, any traditional HSI classification approach can be used. In this paper, sparse logistic regression (SRL) is selected as the classifier. Especially, if there are a few labeled pixels in the target domain, multitask SRL is used to further promote the classification performance. The experimental results on synthetic and real HSIs show the advantages of the proposed method for cross-scene classification. Numéro de notice : A2017-157 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2016.2627042 En ligne : http://dx.doi.org/10.1109/TGRS.2016.2627042 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=84694
in IEEE Transactions on geoscience and remote sensing > vol 55 n° 3 (March 2017) . - pp 1544 - 1562[article]The use of logistic model tree (LMT) for pixel- and object-based classifications using high-resolution WorldView-2 imagery / Ismail Colkesen in Geocarto international, vol 32 n° 1 (January 2017)
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Titre : The use of logistic model tree (LMT) for pixel- and object-based classifications using high-resolution WorldView-2 imagery Type de document : Article/Communication Auteurs : Ismail Colkesen, Auteur ; Taskin Kavzoglu, Auteur Année de publication : 2017 Article en page(s) : pp 71 - 86 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] algorithme d'apprentissage
[Termes IGN] arbre de décision
[Termes IGN] classification orientée objet
[Termes IGN] classification par arbre de décision
[Termes IGN] classification pixellaire
[Termes IGN] image Worldview
[Termes IGN] régression logistiqueRésumé : (auteur) Logistic model tree (LMT), a new method integrating standard decision tree (DT) induction and linear logistic regression algorithm in a single tree, have been recently proposed as an alternative to DT-based learning algorithms. In this study, the LMT was applied in the context of pixel- and object-based classifications using high-resolution WorldView-2 imagery, and its performance was compared with C4.5, random forest and Adaboost. Results of the study showed that the LMT generally produced more accurate classification results than the other methods for both pixel- and object-based classifications. The improvement in classification accuracy reached to 3% in pixel-based and 5% in object-based classifications. It was also estimated that the LMT algorithm produced the most accurate results considering the allocation and overall disagreement errors. Based on the Wilcoxon’s Signed-Ranks tests, the performance differences between the LMT and the other methods were statistically significant for both pixel- and object-based image classifications. Numéro de notice : A2017-085 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2015.1128486 Date de publication en ligne : 12/01/2016 En ligne : http://dx.doi.org/10.1080/10106049.2015.1128486 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=84410
in Geocarto international > vol 32 n° 1 (January 2017) . - pp 71 - 86[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 059-2017011 RAB Revue Centre de documentation En réserve L003 Disponible How many samples are needed? An investigation of binary logistic regression for selective omission in a road network / Qi Zhou in Cartography and Geographic Information Science, vol 43 n° 5 (November 2016)
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Titre : How many samples are needed? An investigation of binary logistic regression for selective omission in a road network Type de document : Article/Communication Auteurs : Qi Zhou, Auteur ; Zhilin Li, Auteur Année de publication : 2016 Article en page(s) : pp 405 - 416 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] apprentissage dirigé
[Termes IGN] échantillonnage
[Termes IGN] généralisation cartographique automatisée
[Termes IGN] régression logistique
[Termes IGN] réseau routier
[Vedettes matières IGN] GénéralisationRésumé : (Auteur) Selective omission in a road network (or road selection) means to retain more important roads, and it is a necessary operator to transform a road network at a large scale to that at a smaller scale. This study discusses the use of the supervised learning approach to road selection, and investigates how many samples are needed for a good performance of road selection. More precisely, the binary logistic regression is employed and three road network data with different sizes and different target scales are involved for testing. The different percentages and numbers of strokes are randomly chosen for training a logistic regression model, which is further applied into the untrained strokes for validation. The performances of using the different sample sizes are mainly evaluated by an error rate estimate. Significance tests are also employed to investigate whether the use of different sample sizes shows statistically significant differences. The experimental results show that in most cases, the error rate estimate is around 0.1–0.2; more importantly, only a small number (e.g., 50–100) of training samples is needed, which indicates the usability of binary logistic regression for road selection. Numéro de notice : A2016-691 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1080/15230406.2015.1104265 En ligne : https://doi.org/10.1080/15230406.2015.1104265 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=82019
in Cartography and Geographic Information Science > vol 43 n° 5 (November 2016) . - pp 405 - 416[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 032-2016051 RAB Revue Centre de documentation En réserve L003 Disponible Understanding the spatial distribution of elephant (Loxodonta africana) poaching incidences in the mid-Zambezi Valley, Zimbabwe using Geographic Information Systems and remote sensing / Mbulisi Sibanda in Geocarto international, Vol 31 n° 9 - 10 (October - November 2016)
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Titre : Understanding the spatial distribution of elephant (Loxodonta africana) poaching incidences in the mid-Zambezi Valley, Zimbabwe using Geographic Information Systems and remote sensing Type de document : Article/Communication Auteurs : Mbulisi Sibanda, Auteur ; Timothy Dube, Auteur ; Victor M. Bangamwabo, Auteur ; et al., Auteur Année de publication : 2016 Article en page(s) : pp 1006 - 1018 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] aire protégée
[Termes IGN] chasse
[Termes IGN] couvert végétal
[Termes IGN] distribution spatiale
[Termes IGN] habitat animal
[Termes IGN] Mammalia
[Termes IGN] régression logistique
[Termes IGN] surveillance écologique
[Termes IGN] ZimbabweMots-clés libres : braconnage Résumé : (auteur) The objective of this study was to understand the factors that explain the spatial distribution of elephant poaching activities in the areas of the mid-Zambezi Valley, Zimbabwe using geographic information system (GIS) and remotely sensed data integrated with spatial logistic regression. The results showed that significant (α = 0.05) elephant poaching hot spots are located closer to wildlife protected areas. Results further demonstrated that resource availability (water and forage) are the main factors explaining elephant poaching activities in the mid-Zambezi Valley. For example, the majority of poaching activities were found to occur in areas with high vegetation fractional cover (high forage) and close to waterholes. The results also showed that poaching incidences were more prevalent during the dry season. The findings of this study highlight the significance of integrating GIS, remotely sensed data and spatial logistic regression tools for understanding and monitoring elephant poaching activities. This information is critical if poaching activities are to be minimized and it is also important for planning, monitoring and mitigation of poaching activities in similar protected areas across the sub-Saharan Africa. Numéro de notice : A2016-670 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2015.1094529 Date de publication en ligne : 27/10/2015 En ligne : http://dx.doi.org/10.1080/10106049.2015.1094529 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=81902
in Geocarto international > Vol 31 n° 9 - 10 (October - November 2016) . - pp 1006 - 1018[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 059-2016051 RAB Revue Centre de documentation En réserve L003 Disponible Tropical forest canopy cover estimation using satellite imagery and airborne lidar reference data / Lauri Korhonen in Silva fennica, vol 49 n° 5 ([01/10/2015])
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Titre : Tropical forest canopy cover estimation using satellite imagery and airborne lidar reference data Type de document : Article/Communication Auteurs : Lauri Korhonen, Auteur ; Daniela Ali-Sisto, Auteur ; Timo Tokola, Auteur Année de publication : 2015 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] canopée
[Termes IGN] couvert forestier
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] forêt tropicale
[Termes IGN] image ALOS-AVNIR2
[Termes IGN] image optique
[Termes IGN] inventaire forestier étranger (données)
[Termes IGN] Laos
[Termes IGN] placette d'échantillonnage
[Termes IGN] régression logistique
[Vedettes matières IGN] Inventaire forestierRésumé : (auteur) The fusion of optical satellite imagery, strips of lidar data and field plots is a promising approach for the inventory of tropical forests. Airborne lidars also enable an accurate direct estimation of the forest canopy cover (CC), and thus a sample of lidar strips can be used as reference data for creating CC maps which are based on satellite images. In this study, our objective was to validate CC maps obtained from an ALOS AVNIR-2 satellite image wall-to-wall, against a lidar-based CC map of a tropical forest area located in Laos. The reference CC values which were needed for model training were obtained from a sample of four lidar strips. Zero-and-one inflated beta regression (ZOINBR) models were applied to link the spectral vegetation indices derived from the ALOS image with the lidar-based CC estimates. In addition, we compared ZOINBR and logistic regression models in the forest area estimation by using >20% CC as a forest definition. Using a total of 409 217 30 × 30 m population units as validation, our model showed a strong correlation between lidar-based CC and spectral satellite features (root mean square error = 12.8%, R2 = 0.82). In the forest area estimation, a direct classification using logistic regression provided better accuracy than the estimation of CC values as an intermediate step (kappa = 0.61 vs. 0.53). It is important to obtain sufficient training data from both ends of the CC range. The forest area estimation should be done before the CC estimation, rather than vice versa. Numéro de notice : A2015-673 Affiliation des auteurs : non IGN Thématique : FORET Nature : Article DOI : 10.14214/sf.1405 En ligne : http://www.silvafennica.fi/article/1405 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=78293
in Silva fennica > vol 49 n° 5 [01/10/2015][article]Multiclass feature learning for hyperspectral image classification: Sparse and hierarchical solutions / Devis Tuia in ISPRS Journal of photogrammetry and remote sensing, vol 105 (July 2015)PermalinkSupervised spectral–spatial hyperspectral image classification with weighted markov random fields / Le Sun in IEEE Transactions on geoscience and remote sensing, vol 53 n° 3 (March 2015)PermalinkHabitat directive forest type western taiga (*9010) in Estonia : the first description of stand structure according to mapping and monitoring data / Anneli Palo in Baltic forestry, vol 21 n° 1 ([01/02/2015])PermalinkPermalinkGeneralized composite kernel framework for hyperspectral image classification / J. Li in IEEE Transactions on geoscience and remote sensing, vol 51 n° 9 (September 2013)PermalinkAssessing the veracity of methods for extracting place semantics from Flickr tags / William A Mackaness in Transactions in GIS, vol 17 n° 4 (August 2013)PermalinkSemisupervised self-learning for hyperspectral image classification / Immaculada Dopido in IEEE Transactions on geoscience and remote sensing, vol 51 n° 7 Tome 1 (July 2013)PermalinkUnderstorey plant species show long‐range spatial patterns in forest patches according to distance‐to‐edge / Vincent Pellissier in Journal of vegetation science, vol 24 n° 1 (January 2013)PermalinkTracking human impact on current tree species distribution using plant communities / Daniel E. Silva in Journal of vegetation science, vol 23 n° 2 (April 2012)PermalinkPredicting southeastern forest canopy heights and fire fuel models using GLAS data / M. Ashworth in Photogrammetric Engineering & Remote Sensing, PERS, vol 76 n° 8 (August 2010)Permalink