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Suitability assessment of urban land use in Dalian, China using PNN and GIS / Ziqian Kang in Natural Hazards, vol 106 n° 1 (March 2021)  

Public [article]  inNatural Hazards > vol 106 n° 1 (March 2021) . - pp 913 - 936 Titre : Suitability assessment of urban land use in Dalian, China using PNN and GIS Type de document : Article/Communication Auteurs : Ziqian Kang, Auteur ; Shuo Wang, Auteur ; Ling Xu, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 913 - 936 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] aire naturelle (écologie) [Termes IGN] analyse multicritère [Termes IGN] bâtiment industriel [Termes IGN] Chine [Termes IGN] classificateur paramétrique [Termes IGN] distribution spatiale [Termes IGN] habitat urbain [Termes IGN] processus de hiérarchisation analytique [Termes IGN] réseau neuronal artificiel [Termes IGN] système d'information géographique [Termes IGN] utilisation du sol [Termes IGN] zone urbaine Résumé : (auteur) The suitability assessment of land use is crucial to avoid wasting land resources. However, the traditional methods with subjective weights are prone to reduce the reasonability and reliability of assessment. For filling this knowledge gap, the probability neural network (PNN) coupled with GIS was adopted to evaluate the land use suitability in this paper. According to the applications of the urban land resource, the land use was divided into three types (resident, industry and ecological reserve). Thus, the three different assessment criteria systems were built for the three land use types. The result of residential land use indicated that the most suitable, suitable and normal suitable residential land were 401, 272 and 12,406 km2 and mainly located in Changhai, Lvshun and Pulandian accordingly. The most suitable land for industry was in Ganjingzi, Jinzhou and Wafangdian and accounted for 22% of the total area. While the most suitable land for ecological reserve was in Pulandian and Zhuanghe with the area of 1967 km2. The results indicated that the south of Dalian was suitable for the residential land use, north of Dalian was suitable for the ecological land use and the central was suitable for industrial land use. The results were coincided to the actual spatial distribution of land use. The proposed PNN coupled with GIS assessment method in suitability of land use is conducted to provide a more reasonable assessment result that can be used by managers and regulators. Numéro de notice : A2021-419 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1007/s11069-020-04500-z Date de publication en ligne : 04/01/2021 En ligne : https://doi.org/10.1007/s11069-020-04500-z Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97769 [article]

Morphologically decoupled structured sparsity for rotation-invariant hyperspectral image analysis / Saurabh Prasad in IEEE Transactions on geoscience and remote sensing, vol 55 n° 8 (August 2017)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 55 n° 8 (August 2017) . - pp 4355 - 4366 Titre : Morphologically decoupled structured sparsity for rotation-invariant hyperspectral image analysis Type de document : Article/Communication Auteurs : Saurabh Prasad, Auteur ; Demetrio Labate, Auteur ; Mishan Cui, Auteur ; Yuhang Zhang, Auteur Année de publication : 2017 Article en page(s) : pp 4355 - 4366 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] classificateur paramétrique [Termes IGN] classification spectrale [Termes IGN] décomposition d'image [Termes IGN] image hyperspectrale [Termes IGN] morphologie mathématique [Termes IGN] primitive géométrique [Termes IGN] réflectance spectrale Résumé : (Auteur) Hyperspectral imagery has emerged as a popular sensing modality for a variety of applications, and sparsity-based methods were shown to be very effective to deal with challenges coming from high dimensionality in most hyperspectral classification problems. In this paper, we challenge the conventional approach to hyperspectral classification that typically builds sparsity-based classifiers directly on spectral reflectance features or features derived directly from the data. We assert that hyperspectral image (HSI) processing can benefit very significantly by decoupling data into geometrically distinct components since the resulting decoupled components are much more suitable for sparse representation-based classifiers. Specifically, we apply morphological separation to decouple data into texture and cartoon-like components, which are sparsely represented using local discrete cosine bases and multiscale shearlets, respectively. In addition to providing a structured sparse representation, this approach allows us to build classifiers with invariance properties specific to each geometrically distinct component of the data. The experimental results using real-world HSI data sets demonstrate the efficacy of the proposed framework for classifying multichannel imagery under a variety of adverse conditions - in particular, small training sample size, additive noise, and rotational variabilities between training and test samples. Numéro de notice : A2017-496 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2017.2691607 En ligne : http://dx.doi.org./10.1109/TGRS.2017.2691607 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=86437 [article]

A spectral–structural bag-of-features scene classifier for very high spatial resolution remote sensing imagery / Bei Zhao in ISPRS Journal of photogrammetry and remote sensing, vol 116 (June 2016)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 116 (June 2016) . - pp 73 – 85 Titre : A spectral–structural bag-of-features scene classifier for very high spatial resolution remote sensing imagery Type de document : Article/Communication Auteurs : Bei Zhao, Auteur ; Yanfei Zhong, Auteur ; Liangpei Zhang, Auteur Année de publication : 2016 Article en page(s) : pp 73 – 85 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] classificateur paramétrique [Termes IGN] classification automatique [Termes IGN] classification dirigée [Termes IGN] exitance spectrale [Termes IGN] image à très haute résolution [Termes IGN] mécanique statistique [Termes IGN] modèle logique de données Résumé : (auteur) Land-use classification of very high spatial resolution remote sensing (VHSR) imagery is one of the most challenging tasks in the field of remote sensing image processing. However, the land-use classification is hard to be addressed by the land-cover classification techniques, due to the complexity of the land-use scenes. Scene classification is considered to be one of the expected ways to address the land-use classification issue. The commonly used scene classification methods of VHSR imagery are all derived from the computer vision community that mainly deal with terrestrial image recognition. Differing from terrestrial images, VHSR images are taken by looking down with airborne and spaceborne sensors, which leads to the distinct light conditions and spatial configuration of land cover in VHSR imagery. Considering the distinct characteristics, two questions should be answered: (1) Which type or combination of information is suitable for the VHSR imagery scene classification? (2) Which scene classification algorithm is best for VHSR imagery? In this paper, an efficient spectral–structural bag-of-features scene classifier (SSBFC) is proposed to combine the spectral and structural information of VHSR imagery. SSBFC utilizes the first- and second-order statistics (the mean and standard deviation values, MeanStd) as the statistical spectral descriptor for the spectral information of the VHSR imagery, and uses dense scale-invariant feature transform (SIFT) as the structural feature descriptor. From the experimental results, the spectral information works better than the structural information, while the combination of the spectral and structural information is better than any single type of information. Taking the characteristic of the spatial configuration into consideration, SSBFC uses the whole image scene as the scope of the pooling operator, instead of the scope generated by a spatial pyramid (SP) commonly used in terrestrial image classification. The experimental results show that the whole image as the scope of the pooling operator performs better than the scope generated by SP. In addition, SSBFC codes and pools the spectral and structural features separately to avoid mutual interruption between the spectral and structural features. The coding vectors of spectral and structural features are then concatenated into a final coding vector. Finally, SSBFC classifies the final coding vector by support vector machine (SVM) with a histogram intersection kernel (HIK). Compared with the latest scene classification methods, the experimental results with three VHSR datasets demonstrate that the proposed SSBFC performs better than the other classification methods for VHSR image scenes. Numéro de notice : A2016-579 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2016.03.004 En ligne : https://doi.org/10.1016/j.isprsjprs.2016.03.004 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=81718 [article]

ATLAS: A three-layered approach to facade parsing / Markus Mathias in International journal of computer vision, vol 118 n° 1 (May 2016)  

Public [article]  inInternational journal of computer vision > vol 118 n° 1 (May 2016) . - pp 22 – 48 Titre : ATLAS: A three-layered approach to facade parsing Type de document : Article/Communication Auteurs : Markus Mathias, Auteur ; Anđelo Martinović, Auteur ; Luc Van Gool, Auteur Année de publication : 2016 Article en page(s) : pp 22 – 48 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques [Termes IGN] analyse d'image numérique [Termes IGN] analyse syntaxique [Termes IGN] appariement sémantique [Termes IGN] classificateur paramétrique [Termes IGN] couche [Termes IGN] façade [Termes IGN] intégration de données [Termes IGN] méta connaissance [Termes IGN] reconstruction 2D du bâti [Termes IGN] test de performance Résumé : (auteur) We propose a novel approach for semantic segmentation of building facades. Our system consists of three distinct layers, representing different levels of abstraction in facade images: segments, objects and architectural elements. In the first layer, the facade is segmented into regions, each of which is assigned a probability distribution over semantic classes. We evaluate different state-of-the-art segmentation and classification strategies to obtain the initial probabilistic semantic labeling. In the second layer, we investigate the performance of different object detectors and show the benefit of using such detectors to improve our initial labeling. The generic approaches of the first two layers are then specialized for the task of facade labeling in the third layer. There, we incorporate additional meta-knowledge in the form of weak architectural principles, which enforces architectural plausibility and consistency on the final reconstruction. Rigorous tests performed on two existing datasets of building facades demonstrate that we outperform the current state of the art, even when using outputs from lower layers of the pipeline. Finally, we demonstrate how the output of the highest layer can be used to create a procedural building reconstruction. Numéro de notice : A2016--150 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1007%2Fs11263-015-0868-z En ligne : https://doi.org/10.1007/s11263-015-0868-z Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=85918 [article]

Classification of submerged aquatic vegetation in Black River using hyperspectral image analysis / Roshan Pande-Chhetri in Geomatica, vol 68 n° 3 (September 2014)  

Public [article]  inGeomatica > vol 68 n° 3 (September 2014) . - pp 169 - 182 Titre : Classification of submerged aquatic vegetation in Black River using hyperspectral image analysis Type de document : Article/Communication Auteurs : Roshan Pande-Chhetri, Auteur ; Amr Abd-Elrahman, Auteur ; Charles Jacoby, Auteur Année de publication : 2014 Article en page(s) : pp 169 - 182 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] classificateur paramétrique [Termes IGN] classification par maximum de vraisemblance [Termes IGN] classification par réseau neuronal [Termes IGN] image hyperspectrale [Termes IGN] macrophyte [Termes IGN] profondeur [Termes IGN] réflexion (rayonnement) [Termes IGN] surface de l'eau Résumé : (Auteur) Le contrôle de la végétation aquatique est un élément important de la gestion des ressources en eau en raison des services écologiques rendus par ces habitats. L'imagerie hyperspectrale dense sur le plan spectral peut être un outil efficace pour cartographier et classifier les communautés macrophytes. L'identification de la végétation submergée dans les régions aquatiques est compliquée par les variations des propriétés optiques des constituants de l'eau, de la géométrie des capteurs d'eau et d'ensoleillement, de la profondeur de l'eau et de la complexité spectrale/structurale des plantes. Plusieurs études ont tenté de détecter la végétation aquatique dans les eaux côtières; mais peu d’études ont ciblé des rivières peu profondes aux eaux noires teintées contaminées par des matières organiques dissoutes du groupe chromophore (CDOM). La présente étude examine les méthodes pour analyser l'imagerie hyperspectrale aéroportée et pour détecter et classifier la végétation aquatique dans un système fluvial d'eaux noires. Les images ont été normalisées afin de tenir compte de la réflexion de la surface de l'eau et de la profondeur changeante de l'eau avant leur analyse par le classificateur à vraisemblance maximale (ML) et trois autres classificateurs non paramétriques: le réseau de neurones formels (ANN), la machine à vecteurs de support (SVM) et un appareil de cartographie angulaire spectral (SAM). L'analyse de l’évaluation de la qualité a indiqué une amélioration générale de la détection et de la classification lorsque les classificateurs non paramétriques étaient appliqués aux images normalisées et à profondeur constante. Une précision maximale de classification d'environ 69% a été atteinte lorsque le classificateur ANN était appliqué aux images normalisées et des précisions maximales de détection de 93% et de 92% ont été atteintes lorsque les classificateurs SAM et SVM étaient appliqués aux images à profondeur constante, respectivement. Numéro de notice : A2014-621 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.5623/cig2014-302 En ligne : https://doi.org/10.5623/cig2014-302 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=74999 [article]

Supervised change detection in satellite imagery using super pixels and relevance feedback / Surender Varma Gadhiraju in Geomatica, vol 68 n° 1 (March 2014)  

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Multiple-entity based classification of airborne laser scanning data in urban areas / S. Xu in ISPRS Journal of photogrammetry and remote sensing, vol 88 (February 2014)  

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Détection et identification de zones de végétation arborée: utilisation conjointe d'images satellite RapidEye et de données BDOrtho / François Tassin (2012)

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Applications of remote sensing and geographic information systems for urban land-cover change studies in Mongolia / D. Amarsaikhan in Geocarto international, vol 24 n° 4 (August - September 2009)  

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Feature reduction using a singular value decomposition for the iterative guided spectral class rejection hybrid classifier / R. Philipps in ISPRS Journal of photogrammetry and remote sensing, vol 64 n° 1 (January - February 2009)  

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A Polygonal approach for automation in extraction of serial modular roofs / Y. Avrahami in Photogrammetric Engineering & Remote Sensing, PERS, vol 74 n° 11 (November 2008)  

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Fusion of support vector machines for classification of multisensor data / Björn Waske in IEEE Transactions on geoscience and remote sensing, vol 45 n° 12 Tome 1 (December 2007)  

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Conversion altimétrique des hauteurs ellipsoïdales par GPS / A. Zeggai in XYZ, n° 109 (décembre 2006 - février 2007)

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Some issues in the classification of DAIS hyperspectral data / M. Pal in International Journal of Remote Sensing IJRS, vol 27 n°12-13-14 (July 2006)  

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On the integration of object-based models and field-based models in GIS / K. Kjenstad in International journal of geographical information science IJGIS, vol 20 n° 5 (may 2006)  

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