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Exploring fuzzy local spatial information algorithms for remote sensing image classification / Anjali Madhu in Remote sensing, vol 13 n° 20 (October-2 2021)  

Public [article]  inRemote sensing > vol 13 n° 20 (October-2 2021) . - n° 4163 Titre : Exploring fuzzy local spatial information algorithms for remote sensing image classification Type de document : Article/Communication Auteurs : Anjali Madhu, Auteur ; Anil Kumar, Auteur ; Peng Jia, Auteur Année de publication : 2021 Article en page(s) : n° 4163 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse de groupement [Termes IGN] classification dirigée [Termes IGN] classification floue [Termes IGN] classification pixellaire [Termes IGN] distance euclidienne [Termes IGN] erreur moyenne quadratique [Termes IGN] Inde [Termes IGN] matrice d'erreur [Termes IGN] occupation du sol [Termes IGN] théorie des possibilités Résumé : (auteur) Fuzzy c-means (FCM) and possibilistic c-means (PCM) are two commonly used fuzzy clustering algorithms for extracting land use land cover (LULC) information from satellite images. However, these algorithms use only spectral or grey-level information of pixels for clustering and ignore their spatial correlation. Different variants of the FCM algorithm have emerged recently that utilize local spatial information in addition to spectral information for clustering. Such algorithms are seen to generate clustering outputs that are more enhanced than the classical spectral-based FCM algorithm. Nonetheless, the scope of integrating spatial contextual information with the conventional PCM algorithm, which has several advantages over the FCM algorithm for supervised classification, has not been explored much. This study proposed integrating local spatial information with the PCM algorithm using simpler but proven approaches from available FCM-based local spatial information algorithms. The three new PCM-based local spatial information algorithms: Possibilistic c-means with spatial constraints (PCM-S), possibilistic local information c-means (PLICM), and adaptive possibilistic local information c-means (ADPLICM) algorithms, were developed corresponding to the available fuzzy c-means with spatial constraints (FCM-S), fuzzy local information c-means (FLICM), and adaptive fuzzy local information c-means (ADFLICM) algorithms. Experiments were conducted to analyze and compare the FCM and PCM classifier variants for supervised LULC classifications in soft (fuzzy) mode. The quantitative assessment of the soft classification results from fuzzy error matrix (FERM) and root mean square error (RMSE) suggested that the new PCM-based local spatial information classifiers produced higher accuracies than the PCM, FCM, or its local spatial variants, in the presence of untrained classes and noise. The promising results from PCM-based local spatial information classifiers suggest that the PCM algorithm, which is known to be naturally robust to noise, when integrated with local spatial information, has the potential to result in more efficient classifiers capable of better handling ambiguities caused by spectral confusions in landscapes. Numéro de notice : A2021-806 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.3390/rs13204163 Date de publication en ligne : 18/10/2021 En ligne : https://doi.org/10.3390/rs13204163 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98864 [article]

Land cover harmonization using Latent Dirichlet Allocation / Zhan Li in International journal of geographical information science IJGIS, vol 35 n° 2 (February 2021)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 35 n° 2 (February 2021) . - pp 348 - 374 Titre : Land cover harmonization using Latent Dirichlet Allocation Type de document : Article/Communication Auteurs : Zhan Li, Auteur ; Joanne C. White, Auteur ; Michael A. Wulder, Auteur Année de publication : 2021 Article en page(s) : pp 348 - 374 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] allocation de Dirichlet latente [Termes IGN] Canada [Termes IGN] carte d'occupation du sol [Termes IGN] chevauchement [Termes IGN] erreur de classification [Termes IGN] harmonisation des données [Termes IGN] matrice d'erreur [Termes IGN] matrice de co-occurrence [Termes IGN] segmentation sémantique [Termes IGN] utilisation du sol Résumé : (auteur) Large-area land cover maps are produced to satisfy different information needs. Land cover maps having partial or complete spatial and/or temporal overlap, different legends, and varying accuracies for similar classes, are increasingly common. To address these concerns and combine two 30-m resolution land cover products, we implemented a harmonization procedure using a Latent Dirichlet Allocation (LDA) model. The LDA model used regionalized class co-occurrences from multiple maps to generate a harmonized class label for each pixel by statistically characterizing land attributes from the class co-occurrences. We evaluated multiple harmonization approaches: using the LDA model alone and in combination with more commonly used information sources for harmonization (i.e. error matrices and semantic affinity scores). The results were compared with the benchmark maps generated using simple legend crosswalks and showed that using LDA outputs with error matrices performed better and increased harmonized map overall accuracy by 6–19% for areas of disagreement between the source maps. Our results revealed the importance of error matrices to harmonization, since excluding error matrices reduced overall accuracy by 4–20%. The LDA-based harmonization approach demonstrated in this paper is quantitative, transparent, portable, and efficient at leveraging the strengths of multiple land cover maps over large areas. Numéro de notice : A2021-027 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2020.1796131 Date de publication en ligne : 27/07/2020 En ligne : https://doi.org/10.1080/13658816.2020.1796131 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96701 [article]

Leveraging class hierarchies with metric-guided prototype learning / Vivien Sainte Fare Garnot (2021)  

Public Titre : Leveraging class hierarchies with metric-guided prototype learning Type de document : Article/Communication Auteurs : Vivien Sainte Fare Garnot , Auteur ; Loïc Landrieu , Auteur Editeur : Ithaca [New York - Etats-Unis] : ArXiv - Université Cornell Année de publication : 2021 Projets : 1-Pas de projet / Conférence : BMVC 2021, 32nd British Machine Vision Conference 22/11/2021 25/11/2021 online Royaume-Uni OA Proceedings Importance : 31 p. Note générale : bibliographie préprint déposé sur ArXiv Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Intelligence artificielle [Termes IGN] apprentissage automatique [Termes IGN] classification [Termes IGN] matrice d'erreur [Termes IGN] prototype [Termes IGN] segmentation sémantique Résumé : (auteur) Not all errors are created equal. This is especially true for many key machine learning applications. In the case of classification tasks, the severity of errors can be summarized under the form of a cost matrix, which assesses the gravity of confusing each pair of classes. When the target classes are organized into a hierarchical structure, this matrix defines a metric. We propose to integrate this metric in a new and versatile classification layer in order to model the disparity of errors. Our method relies on jointly learning a feature-extracting network and a set of class representations, or prototypes, which incorporate the error metric into their relative arrangement in the embedding space. Our approach allows for consistent improvement of the severity of the network's errors with regard to the cost matrix. Furthermore, when the induced metric contains insight on the data structure, our approach improves the overall precision as well. Experiments on four different public datasets -- from agricultural time series classification to depth image semantic segmentation -- validate our approach. Numéro de notice : C2021-027 Affiliation des auteurs : UGE-LASTIG (2020- ) Autre URL associée : vers ArXiv Thématique : IMAGERIE/INFORMATIQUE Nature : Poster nature-HAL : Poster-avec-CL DOI : 10.48550/arXiv.2007.03047 En ligne : https://www.bmvc2021-virtualconference.com/assets/papers/0084.pdf Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98983

The weight matrix determination of systematic bias calibration for a laser altimeter / Ma Yue in Photogrammetric Engineering & Remote Sensing, PERS, vol 82 n° 11 (November 2016)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 82 n° 11 (November 2016) . - pp 847 - 852 Titre : The weight matrix determination of systematic bias calibration for a laser altimeter Type de document : Article/Communication Auteurs : Ma Yue, Auteur ; Li Song, Auteur ; Lu Xiushan, Auteur ; et al., Auteur Année de publication : 2016 Article en page(s) : pp 847 - 852 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] données ICEsat [Termes IGN] erreur de mesure [Termes IGN] étalonnage [Termes IGN] géolocalisation [Termes IGN] incertitude de mesurage [Termes IGN] matrice [Termes IGN] matrice d'erreur Résumé : (Auteur) The geolocation accuracy of satellite laser altimeters is significantly influenced by on-orbit misalignment and ranging biases. Few researchers have investigated the weight matrix determination method, which plays a critical role in bias estimation. In this article, a systematic misalignment and ranging bias model was deduced. Based on the least squares criterion, a bias calibration method was designed for use with solid natural surfaces; and the weight matrix was defined according to the ranging uncertainty theory. Referring to the Geoscience Laser Altimeter System (glas) parameters, the established model and method were verified using programming simulations, which indicated with a misalignment of tens of arc-seconds in the pitch and roll directions and a ranging bias of several centimeters, by using the weight matrix, the estimation accuracies of the misalignment and ranging bias increased by 0.22 and 2 cm, respectively. Consequently, the geolocation accuracy increased by approximately 0.64 m horizontally and 3 cm vertically for a 1° sloping surface. Numéro de notice : A2016-944 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article DOI : 10.14358/PERS.82.11.847 En ligne : http://dx.doi.org/10.14358/PERS.82.11.847 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=83436 [article]

A measure of average error variance of line features / Eryong Liu in Cartography and Geographic Information Science, Vol 43 n° 4 (September 2016)  

Public [article]  inCartography and Geographic Information Science > Vol 43 n° 4 (September 2016) . - pp 321 - 327 Titre : A measure of average error variance of line features Type de document : Article/Communication Auteurs : Eryong Liu, Auteur ; Wenzhong Shi, Auteur Année de publication : 2016 Article en page(s) : pp 321 - 327 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique [Termes IGN] erreur de positionnement [Termes IGN] erreur moyenne [Termes IGN] interpolation linéaire [Termes IGN] ligne caractéristique [Termes IGN] matrice d'erreur [Termes IGN] matrice de covariance [Termes IGN] objet géographique linéaire [Termes IGN] propagation d'erreur [Termes IGN] qualité des données Résumé : (Auteur) This article presents a new development in measuring the positional error of line features in Geographic Information Systems (GIS), in the form of a new measure for estimating the average error variance of line features, including line segment, polyline, polygon, and curved lines. This average error measure is represented in the form of a covariance matrix derived by an analytical approach. Corresponding error indicators are derived from this matrix. The error of line features mainly results from two factors: (1) an error propagated from the original component points of line features and (2) a model error of interpolation between these points. In this study, a method of average error estimation has been derived regarding the first type error of line features that are interpolated by either linear or cubic interpolation methods. The main contribution of the research is the provision of an error measure to assess the quality of spatial data in application settings. The proposed error models for estimating average error variance of line features in a GIS are illustrated by both simulated and practical experiments. The results show that the line accuracy from a linear interpolation is better than a line interpolated using a cubic model. Numéro de notice : A2016-417 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1080/15230406.2015.1077738 En ligne : https://doi.org/10.1080/15230406.2015.1077738 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=81312 [article]

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