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Remote sensing techniques for water management and climate change monitoring in drought areas: case studies in Egypt and Tunisia / Lifan Ji in European journal of remote sensing, vol 56 n° 1 (2023)  

Public [article]  inEuropean journal of remote sensing > vol 56 n° 1 (2023) . - pp 1 - 16 Titre : Remote sensing techniques for water management and climate change monitoring in drought areas: case studies in Egypt and Tunisia Type de document : Article/Communication Auteurs : Lifan Ji, Auteur ; Yihao Shao, Auteur ; Jianjun Liu, Auteur ; et al., Auteur Année de publication : 2023 Article en page(s) : pp 1 - 16 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] analyse diachronique [Termes IGN] changement climatique [Termes IGN] Egypte [Termes IGN] gestion de l'eau [Termes IGN] humidité du sol [Termes IGN] image optique [Termes IGN] image radar moirée [Termes IGN] image Sentinel-SAR [Termes IGN] indice de végétation [Termes IGN] réseau neuronal artificiel [Termes IGN] stress hydrique [Termes IGN] Tunisie [Termes IGN] zone semi-aride Résumé : (auteur) This study focused on monitoring the water status of vegetation and soil by exploiting the synergy of optical and microwave satellite data with the aim of improving the knowledge of water cycle in cultivated lands in Egyptian Delta and Tunisian areas. Environmental analysis approaches based on optical and synthetic aperture radar data were carried out to set up the basis for future implementation of practical and cost-effective methods for sustainable water use in agriculture. Long-term behaviors of vegetation indices were thus analyzed between 2000 and 2018. By using SAR data from Sentinel-1, an Artificial Neural Network-based algorithm was implemented for estimating soil moisture and monthly maps for 2018 have been generated to be compared with information derived from optical indices. Moreover, a novel drought severity index was developed and applied to available data. The index was obtained by combining vegetation soil difference index, derived from optical data, and soil moisture content derived from SAR data. The proposed index was found capable of complementing optical and microwave sensitivity to drought-related parameters, although ground data are missing for correctly validating the results, by capturing drought patterns and their temporal evolution better than indices based only on microwave or optical data. Numéro de notice : A2023-103 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1080/22797254.2022.2157335 Date de publication en ligne : 06/01/2023 En ligne : https://doi.org/10.1080/22797254.2022.2157335 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102430 [article]

Generalized tensor regression for hyperspectral image classification / Jianjun Liu in IEEE Transactions on geoscience and remote sensing, vol 58 n° 2 (February 2020)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 58 n° 2 (February 2020) . - pp 1244 - 1258 Titre : Generalized tensor regression for hyperspectral image classification Type de document : Article/Communication Auteurs : Jianjun Liu, Auteur ; Zebin Wu, Auteur ; Liang Xiao, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 1244 - 1258 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] bande spectrale [Termes IGN] calcul tensoriel [Termes IGN] classification dirigée [Termes IGN] image hyperspectrale [Termes IGN] image infrarouge [Termes IGN] méthode fondée sur le noyau [Termes IGN] régression [Termes IGN] spectromètre imageur [Termes IGN] tenseur Résumé : (auteur) In this article, we propose a novel tensorial approach, namely, generalized tensor regression, for hyperspectral image classification. First, a simple and effective classifier, i.e., the ridge regression for multivariate labels, is extended to its tensorial version by taking advantages of tensorial representation. Then, the discrimination information of different modes is exploited to further strengthen the capacity of the model. Moreover, the model can be simplified and solved easily. Different from traditional tensorial methods, the proposed model can be utilized to capture not only the intrinsic structure of data in a physical sense but also the generalized relationship of data in a logical sense. Our proposed approach is shown to be effective for different classification purposes on a series of instantiations. Specifically, our experiment results with hyperspectral images collected by the airborne visible/infrared imaging spectrometer, the reflective optics spectrographic imaging system and the ITRES CASI-1500 demonstrate the effectiveness of the proposed approach as compared to other tensor-based classifiers and multiple kernel learning methods. Numéro de notice : A2020-097 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2019.2944989 Date de publication en ligne : 21/10/2019 En ligne : https://doi.org/10.1109/TGRS.2019.2944989 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94670 [article]

Planning unobstructed paths in traffic-aware spatial networks / Shuo Shang in Geoinformatica, vol 19 n° 4 (October - December 2015)  

Public [article]  inGeoinformatica > vol 19 n° 4 (October - December 2015) . - pp 723 - 746 Titre : Planning unobstructed paths in traffic-aware spatial networks Type de document : Article/Communication Auteurs : Shuo Shang, Auteur ; Jianjun Liu, Auteur ; Kai Zheng, Auteur ; et al., Auteur Année de publication : 2015 Article en page(s) : pp 723 - 746 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique [Termes IGN] accessibilité [Termes IGN] calcul d'itinéraire [Termes IGN] gestion des itinéraires [Termes IGN] itinéraire [Termes IGN] objet mobile [Termes IGN] trafic routier [Termes IGN] vitesse Mots-clés libres : traffic-aware spatial network Résumé : (auteur) Route planning and recommendation have received significant attention in recent years. In this light, we study a novel problem of planning unobstructed paths in traffic-aware spatial networks (TAUP queries) to avoid potential traffic congestions. We propose two probabilistic TAUP queries: (1) a time-threshold query like “what is the path from the check-in desk to the flight SK 1217 with the minimum congestion probability to take at most 45 minutes?”, and (2) a probability-threshold query like “what is the fastest path from the check-in desk to the flight SK 1217 whose congestion probability is less than 20 %?”. These queries are mainly motivated by indoor space applications, but are also applicable in outdoor spaces. We believe that these queries are useful in some popular applications, such as planning unobstructed paths for VIP bags in airports and planning convenient routes for travelers. The TAUP queries are challenged by two difficulties: (1) how to model the traffic awareness in spatial networks practically, and (2) how to compute the TAUP queries efficiently under different query settings. To overcome these challenges, we construct a traffic-aware spatial network G t a (V, E) by analyzing uncertain trajectories of moving objects. Based on G t a (V, E), two efficient algorithms are developed to compute the TAUP queries. The performances of TAUP queries are verified by extensive experiments on real and synthetic spatial data. Numéro de notice : A2015--035 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1007/s10707-015-0227-9 Date de publication en ligne : 10/04/2015 En ligne : http://dx.doi.org/10.1007/s10707-015-0227-9 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=81121 [article]

Supervised 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)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 53 n° 3 (March 2015) . - pp 1490 - 1503 Titre : Supervised spectral–spatial hyperspectral image classification with weighted markov random fields Type de document : Article/Communication Auteurs : Le Sun, Auteur ; Zebin Wu, Auteur ; Jianjun Liu, Auteur ; et al., Auteur Année de publication : 2015 Article en page(s) : pp 1490 - 1503 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] champ aléatoire de Markov [Termes IGN] classification dirigée [Termes IGN] classification spectrale [Termes IGN] image hyperspectrale [Termes IGN] pondération [Termes IGN] régression logistique Résumé : (Auteur) This paper presents a new approach for hyperspectral image classification exploiting spectral-spatial information. Under the maximum a posteriori framework, we propose a supervised classification model which includes a spectral data fidelity term and a spatially adaptive Markov random field (MRF) prior in the hidden field. The data fidelity term adopted in this paper is learned from the sparse multinomial logistic regression (SMLR) classifier, while the spatially adaptive MRF prior is modeled by a spatially adaptive total variation (SpATV) regularization to enforce a spatially smooth classifier. To further improve the classification accuracy, the true labels of training samples are fixed as an additional constraint in the proposed model. Thus, our model takes full advantage of exploiting the spatial and contextual information present in the hyperspectral image. An efficient hyperspectral image classification algorithm, named SMLR-SpATV, is then developed to solve the final proposed model using the alternating direction method of multipliers. Experimental results on real hyperspectral data sets demonstrate that the proposed approach outperforms many state-of-the-art methods in terms of the overall accuracy, average accuracy, and kappa (k) statistic. Numéro de notice : A2015-134 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2014.2344442 Date de publication en ligne : 18/08/2014 En ligne : https://doi.org/10.1109/TGRS.2014.2344442 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=75800 [article]

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