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Examining the sensitivity of spatial scale in cellular automata Markov chain simulation of land use change / Hao Wu in International journal of geographical information science IJGIS, Vol 33 n° 5-6 (May - June 2019)  

Public [article]  inInternational journal of geographical information science IJGIS > Vol 33 n° 5-6 (May - June 2019) . - pp 1040 - 1061 Titre : Examining the sensitivity of spatial scale in cellular automata Markov chain simulation of land use change Type de document : Article/Communication Auteurs : Hao Wu, Auteur ; Zhen Li, Auteur ; Keith C. Clarke, Auteur ; Wenzhong Shi, Auteur ; et al., Auteur Année de publication : 2019 Article en page(s) : pp 1040 - 1061 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes descripteurs IGN] analyse de sensibilité [Termes descripteurs IGN] analyse diachronique [Termes descripteurs IGN] automate cellulaire [Termes descripteurs IGN] chaîne de Markov [Termes descripteurs IGN] changement d'occupation du sol [Termes descripteurs IGN] changement d'utilisation du sol [Termes descripteurs IGN] image Landsat-TM [Termes descripteurs IGN] modèle de Markov [Termes descripteurs IGN] simulation Résumé : (Auteur) Understanding the spatial scale sensitivity of cellular automata is crucial for improving the accuracy of land use change simulation. We propose a framework based on a response surface method to comprehensively explore spatial scale sensitivity of the cellular automata Markov chain (CA-Markov) model, and present a hybrid evaluation model for expressing simulation accuracy that merges the strengths of the Kappa coefficient and of Contagion index. Three Landsat-Thematic Mapper remote sensing images of Wuhan in 1987, 1996, and 2005 were used to extract land use information. The results demonstrate that the spatial scale sensitivity of the CA-Markov model resulting from individual components and their combinations are both worthy of attention. The utility of our proposed hybrid evaluation model and response surface method to investigate the sensitivity has proven to be more accurate than the single Kappa coefficient method and more efficient than traditional methods. The findings also show that the CA-Markov model is more sensitive to neighborhood size than to cell size or neighborhood type considering individual component effects. Particularly, the bilateral and trilateral interactions between neighborhood and cell size result in a more remarkable scale effect than that of a single cell size. Numéro de notice : A2019-443 Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2019.1568441 date de publication en ligne : 18/02/2019 En ligne : https://doi.org/10.1080/13658816.2019.1568441 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92777 [article]

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Dirichlet process based active learning and discovery of unknown classes for hyperspectral image classification / Hao Wu in IEEE Transactions on geoscience and remote sensing, vol 54 n° 8 (August 2016)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 54 n° 8 (August 2016) . - pp 4882 - 4895 Titre : Dirichlet process based active learning and discovery of unknown classes for hyperspectral image classification Type de document : Article/Communication Auteurs : Hao Wu, Auteur ; Saurabh Prasad, Auteur Année de publication : 2016 Article en page(s) : pp 4882 - 4895 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes descripteurs IGN] apprentissage automatique [Termes descripteurs IGN] classe inconnue [Termes descripteurs IGN] classification [Termes descripteurs IGN] image hyperspectrale [Termes descripteurs IGN] problème de Dirichlet Résumé : (Auteur) Active learning is an area of significant ongoing research interest for the classification of remotely sensed data, where obtaining efficient training data is both time consuming and expensive. The goal of active learning is to achieve high classification performance by querying as few samples as possible from a large unlabeled data pool. Traditional active learning frameworks all assume the existence of labeled samples for all classes of interest. However, in real-world applications, the unlabeled data pool may contain data from unknown classes that we are not aware of in advance, and a quick detection of them is useful for enriching our training set. In this scenario, traditional active learning methods may not effectively and rapidly detect the unknown classes. We proposed an active learning framework which provides robust classification performance with minimum manual labeling effort while simultaneously discovering unknown (missing) classes. The discovery of unknown classes is particularly suited to an active learning framework where an annotator is in the loop. A Dirichlet process mixture model is utilized in our proposed method to cluster the labeled and unlabeled samples as a whole. If unknown classes exist, they will emerge as new clusters which are different from other existing clusters occupied by known classes, and then, the proposed query strategy will give priority to querying samples in the new clusters. We present experimental results with hyperspectral data to show that our method provides better classification performance compared to existing active learning methods with or without unknown classes. Numéro de notice : A2016-892 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern En ligne : http://dx.doi.org/10.1109/TGRS.2016.2552507 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=83072 [article]

Double constrained NMF for hyperspectral unmixing / Xiaoqiang Lu in IEEE Transactions on geoscience and remote sensing, vol 52 n° 5 tome 1 (May 2014)

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 52 n° 5 tome 1 (May 2014) . - pp 2746 - 2758 Titre : Double constrained NMF for hyperspectral unmixing Type de document : Article/Communication Auteurs : Xiaoqiang Lu, Auteur ; Hao Wu, Auteur ; Yuan Yuan, Auteur Année de publication : 2014 Article en page(s) : pp 2746 - 2758 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes descripteurs IGN] analyse des mélanges spectraux [Termes descripteurs IGN] contrainte spectrale [Termes descripteurs IGN] factorisation [Termes descripteurs IGN] factorisation de matrice non-négative [Termes descripteurs IGN] image hyperspectrale Résumé : (Auteur) Given only the collected hyperspectral data, unmixing aims at obtaining the latent constituent materials and their corresponding fractional abundances. Recently, many nonnegative matrix factorization (NMF)-based algorithms have been developed to deal with this issue. Considering that the abundances of most materials may be sparse, the sparseness constraint is intuitively introduced into NMF. Although sparse NMF algorithms have achieved advanced performance in unmixing, the result is still susceptible to unstable decomposition and noise corruption. To reduce the aforementioned drawbacks, the structural information of the data is exploited to guide the unmixing. Since similar pixel spectra often imply similar substance constructions, clustering can explicitly characterize this similarity. Through maintaining the structural information during the unmixing, the resulting fractional abundances by the proposed algorithm can well coincide with the real distributions of constituent materials. Moreover, the additional clustering-based regularization term also lessens the interference of noise to some extent. The experimental results on synthetic and real hyperspectral data both illustrate the superiority of the proposed method compared with other state-of-the-art algorithms. Numéro de notice : A2014-263 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=33166 [article]

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Temporal logic and operation relations based knowledge representation for land cover change web services / Jun Chen in ISPRS Journal of photogrammetry and remote sensing, vol 83 (September 2013)

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 83 (September 2013) . - pp 140 - 150 Titre : Temporal logic and operation relations based knowledge representation for land cover change web services Type de document : Article/Communication Auteurs : Jun Chen, Auteur ; Hao Wu, Auteur ; Songnian Li, Auteur ; et al., Auteur Année de publication : 2013 Article en page(s) : pp 140 - 150 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique web [Termes descripteurs IGN] analyse spatio-temporelle [Termes descripteurs IGN] base de connaissances [Termes descripteurs IGN] détection de changement [Termes descripteurs IGN] occupation du sol [Termes descripteurs IGN] OWL [Termes descripteurs IGN] service web géographique Résumé : (Auteur) Providing land cover spatio-temporal information and geo-computing through web service is a new challenge for supporting global change research, earth system simulation and many other societal benefit areas. This requires an integrated knowledge representation and web implementation of static land cover and change information, as well as the related operations for geo-computing. The temporal logic relations among land cover snapshots and increments were examined with a matrix-based three-step analysis. Twelve temporal logic relations were identified and five basic spatial operations were formalized with set operators, which were all used to develop algorithms for deriving implicit change information. A knowledge representation for land cover change information was then developed based on these temporal logic and operation relations. A prototype web-service system was further implemented based on OWL-DL. Both online access and conversion of land cover spatio-temporal information can be facilitated with such a web service system. Numéro de notice : A2013-492 Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32630 [article]

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Manifold regularized sparse NMF for hyperspectral unmixing / Xiaqiang Lu in IEEE Transactions on geoscience and remote sensing, vol 51 n° 5 Tome 1 (May 2013)

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 51 n° 5 Tome 1 (May 2013) . - pp 2815 - 2826 Titre : Manifold regularized sparse NMF for hyperspectral unmixing Type de document : Article/Communication Auteurs : Xiaqiang Lu, Auteur ; Hao Wu, Auteur ; Pingkun Yan, Auteur ; et al., Auteur Année de publication : 2013 Article en page(s) : pp 2815 - 2826 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes descripteurs IGN] analyse des mélanges spectraux [Termes descripteurs IGN] factorisation [Termes descripteurs IGN] factorisation de matrice non-négative [Termes descripteurs IGN] image hyperspectrale [Termes descripteurs IGN] matrice creuse Résumé : (Auteur) Hyperspectral unmixing is one of the most important techniques in analyzing hyperspectral images, which decomposes a mixed pixel into a collection of constituent materials weighted by their proportions. Recently, many sparse nonnegative matrix factorization (NMF) algorithms have achieved advanced performance for hyperspectral unmixing because they overcome the difficulty of absence of pure pixels and sufficiently utilize the sparse characteristic of the data. However, most existing sparse NMF algorithms for hyperspectral unmixing only consider the Euclidean structure of the hyperspectral data space. In fact, hyperspectral data are more likely to lie on a low-dimensional submanifold embedded in the high-dimensional ambient space. Thus, it is necessary to consider the intrinsic manifold structure for hyperspectral unmixing. In order to exploit the latent manifold structure of the data during the decomposition, manifold regularization is incorporated into sparsity-constrained NMF for unmixing in this paper. Since the additional manifold regularization term can keep the close link between the original image and the material abundance maps, the proposed approach leads to a more desired unmixing performance. The experimental results on synthetic and real hyperspectral data both illustrate the superiority of the proposed method compared with other state-of-the-art approaches. Numéro de notice : A2013-258 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32396 [article]

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