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Unsupervised feature learning for land-use scene recognition / Jiayuan Fan in IEEE Transactions on geoscience and remote sensing, vol 55 n° 4 (April 2017)
[article]
Titre : Unsupervised feature learning for land-use scene recognition Type de document : Article/Communication Auteurs : Jiayuan Fan, Auteur ; Tao Chen, Auteur ; Shijian Lu, Auteur Année de publication : 2017 Article en page(s) : pp 2250 - 2261 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] algorithme d'apprentissage
[Termes IGN] analyse discriminante
[Termes IGN] codage
[Termes IGN] image proche infrarouge
[Termes IGN] image RVB
[Termes IGN] invariant
[Termes IGN] pouvoir de résolution géométrique
[Termes IGN] reconnaissance automatique
[Termes IGN] Singapour
[Termes IGN] utilisation du solRésumé : (Auteur) This paper proposes a novel unsupervised feature learning algorithm for land-use scene recognition on very high resolution remote sensing imagery. The proposed technique utilizes a multipath sparse coding architecture in order to capture multiple aspects of discriminative structures within complex remote sensing sceneries. Unlike the previous sparse coding and bag-of-visual-words-based techniques that rely on the handcrafted feature descriptors such as scale-invariant feature transform, the proposed technique extracts dense low-level features from the raw data, including the visual (RGB) data and near-infrared (NIR) data, using image patches of varying sizes at different layers. The proposed technique has been evaluated on three data sets, including the 21-category UC Merced landuse RGB data set with a 1-ft spatial resolution, the 9-category ground scene RGB-NIR data set, and the 10-category Singapore land-use RGB-NIR data set with a 0.5-m spatial resolution. The experimental results show that the proposed technique outperforms the state-of-the-art methods. Numéro de notice : A2107-174 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2016.2640186 En ligne : https://doi.org/10.1109/TGRS.2016.2640186 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=84723
in IEEE Transactions on geoscience and remote sensing > vol 55 n° 4 (April 2017) . - pp 2250 - 2261[article]Classifying natural-language spatial relation terms with random forest algorithm / Shihong Du in International journal of geographical information science IJGIS, vol 31 n° 3-4 (March-April 2017)
[article]
Titre : Classifying natural-language spatial relation terms with random forest algorithm Type de document : Article/Communication Auteurs : Shihong Du, Auteur ; Xiaonan Wang, Auteur ; Chen-Chieh Feng, Auteur ; Xiuyuan Zhang, Auteur Année de publication : 2017 Article en page(s) : pp 542 - 568 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique
[Termes IGN] apprentissage dirigé
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] intelligence artificielle
[Termes IGN] interface en langage naturel
[Termes IGN] langage naturel (informatique)
[Termes IGN] méthode robuste
[Termes IGN] recherche d'information géographique
[Termes IGN] relation spatiale
[Termes IGN] relation topologique
[Termes IGN] similitude sémantiqueRésumé : (Auteur) The exponential growth of natural language text data in social media has contributed a rich data source for geographic information. However, incorporating such data source for GIS analysis faces tremendous challenges as existing GIS data tend to be geometry based while natural language text data tend to rely on natural language spatial relation (NLSR) terms. To alleviate this problem, one critical step is to translate geometric configurations into NLSR terms, but existing methods to date (e.g. mean value or decision tree algorithm) are insufficient to obtain a precise translation. This study addresses this issue by adopting the random forest (RF) algorithm to automatically learn a robust mapping model from a large number of samples and to evaluate the importance of each variable for each NLSR term. Because the semantic similarity of the collected terms reduces the classification accuracy, different grouping schemes of NLSR terms are used, with their influences on classification results being evaluated. The experiment results demonstrate that the learned model can accurately transform geometric configurations into NLSR terms, and that recognizing different groups of terms require different sets of variables. More importantly, the results of variable importance evaluation indicate that the importance of topology types determined by the 9-intersection model is weaker than metric variables in defining NLSR terms, which contrasts to the assertion of ‘topology matters, metric refines’ in existing studies. Numéro de notice : A2017-078 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2016.1212356 En ligne : http://dx.doi.org/10.1080/13658816.2016.1212356 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=84340
in International journal of geographical information science IJGIS > vol 31 n° 3-4 (March-April 2017) . - pp 542 - 568[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 079-2017021 RAB Revue Centre de documentation En réserve L003 Disponible 079-2017022 RAB Revue Centre de documentation En réserve L003 Disponible Cognitively plausible representations for the alignment of sketch and geo-referenced maps / Sahib Jan in Journal of Spatial Information Science (JoSIS), n° 14 (March 2017)
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Titre : Cognitively plausible representations for the alignment of sketch and geo-referenced maps Type de document : Article/Communication Auteurs : Sahib Jan, Auteur ; Angela Schwering, Auteur ; Carl Schultz, Auteur ; Malumbo Chaka Chipofya, Auteur Année de publication : 2017 Article en page(s) : pp 31 - 59 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] croquis topographique
[Termes IGN] représentation cartographique 2D
[Termes IGN] réseau de contraintes
[Vedettes matières IGN] CartologieRésumé : (Auteur) In many geo-spatial applications, freehand sketch maps are considered as an intuitive way to collect user-generated spatial information. The task of automatically mapping information from such hand-drawn sketch maps to geo-referenced maps is known as the alignment task. Researchers have proposed various qualitative representations to capture distorted and generalized spatial information in sketch maps. However, thus far the effectiveness of these representations has not been evaluated in the context of an alignment task. This paper empirically evaluates a set of cognitively plausible representations for alignment using real sketch maps collected from two different study areas with the corresponding geo-referenced maps. Firstly, the representations are evaluated in a single-aspect alignment approach by demonstrating the alignment of maps for each individual sketch aspect. Secondly, representations are evaluated across multiple sketch aspects using more than one representation in the alignment task. The evaluations demonstrated the suitability of the chosen representation for aligning user-generated content with geo-referenced maps in a real-world scenario. Numéro de notice : A2017-818 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.5311/JOSIS.2017.14.294 En ligne : https://doi.org/10.5311/JOSIS.2017.14.294 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=89303
in Journal of Spatial Information Science (JoSIS) > n° 14 (March 2017) . - pp 31 - 59[article]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]Extracting target spectrum for hyperspectral target detection : an adaptive weighted learning method using a self-completed background dictionary / Yubin Niu in IEEE Transactions on geoscience and remote sensing, vol 55 n° 3 (March 2017)
[article]
Titre : Extracting target spectrum for hyperspectral target detection : an adaptive weighted learning method using a self-completed background dictionary Type de document : Article/Communication Auteurs : Yubin Niu, Auteur ; Bin Wang, Auteur Année de publication : 2017 Article en page(s) : pp 1604 - 1617 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] apprentissage automatique
[Termes IGN] classification spectrale
[Termes IGN] détection de cible
[Termes IGN] image hyperspectraleRésumé : (Auteur) The accuracy of target spectra determines the performances of hyperspectral target detection (TD) algorithms. However, given the inherent spectral variability and subpixel problem in hyperspectral imagery (HSI), the target spectra obtained from a standard spectral library or pixels from images directly are in most cases different from those of the real target spectra, resulting in low detection accuracy. The problem caused by inaccurate prior target information led to recognition of a new hotspot on HSI. In this paper, an adaptive weighted learning method (AWLM) using a self-completed background dictionary (SCBD) is specifically developed to extract the accurate target spectrum for hyperspectral TD. AWLM is derived from the idea of dictionary learning algorithms, learning the specific target spectrum with target-proportion-related adaptive weights. A strategy to construct SCBD is proposed to guarantee the convergence of AWLM to the accurate target spectrum. Utilizing the extracted target spectrum with higher accuracy, conventional TD algorithms can also achieve satisfactory detection results. Experimental results on both simulated and real hyperspectral data demonstrate that the proposed method has an advantage in extracting accurate target spectrum, enabling better and more robust detection results using conventional detectors than state-of-the-art methods that also aim at the problem of inaccurate prior target information of HSI. Numéro de notice : A2017-158 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2016.2628085 En ligne : https://doi.org/10.1109/TGRS.2016.2628085 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=84695
in IEEE Transactions on geoscience and remote sensing > vol 55 n° 3 (March 2017) . - pp 1604 - 1617[article]A hybrid genetic algorithm with local optimiser improves calibration of a vegetation change cellular automata model / Rachel Whitsed in International journal of geographical information science IJGIS, vol 31 n° 3-4 (March-April 2017)PermalinkPredicting the encoding of secondary diagnoses. 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