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Effect of training class label noise on classification performances for land cover mapping with satellite image time series / Charlotte Pelletier in Remote sensing, vol 9 n° 2 (February 2017)  

Public [article]  inRemote sensing > vol 9 n° 2 (February 2017) . - pp 1 - 24 Titre : Effect of training class label noise on classification performances for land cover mapping with satellite image time series Type de document : Article/Communication Auteurs : Charlotte Pelletier, Auteur ; Silvia Valero, Auteur ; Jordi Inglada, Auteur ; Nicolas Champion , Auteur ; Claire Marais-Sicre, Auteur ; Gérard Dedieu, Auteur Année de publication : 2017 Projets : 1-Pas de projet / Article en page(s) : pp 1 - 24 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes descripteurs IGN] base de données d'occupation du sol [Termes descripteurs IGN] classification par forêts aléatoires [Termes descripteurs IGN] classification par séparateurs à vaste marge [Termes descripteurs IGN] image Landsat-8 [Termes descripteurs IGN] image SPOT 4 [Termes descripteurs IGN] série temporelle Résumé : (auteur) Supervised classification systems used for land cover mapping require accurate reference databases. These reference data come generally from different sources such as field measurements, thematic maps, or aerial photographs. Due to misregistration, update delay, or land cover complexity, they may contain class label noise, i.e., a wrong label assignment. This study aims at evaluating the impact of mislabeled training data on classification performances for land cover mapping. Particularly, it addresses the random and systematic label noise problem for the classification of high resolution satellite image time series. Experiments are carried out on synthetic and real datasets with two traditional classifiers: Support Vector Machines (SVM) and Random Forests (RF). A synthetic dataset has been designed for this study, simulating vegetation profiles over one year. The real dataset is composed of Landsat-8 and SPOT-4 images acquired during one year in the south of France. The results show that both classifiers are little influenced for low random noise levels up to 25%–30%, but their performances drop down for higher noise levels. Different classification configurations are tested by increasing the number of classes, using different input feature vectors, and changing the number of training instances. Algorithm complexities are also analyzed. The RF classifier achieves high robustness to random and systematic label noise for all the tested configurations; whereas the SVM classifier is more sensitive to the kernel choice and to the input feature vectors. Finally, this work reveals that the cross-validation procedure is impacted by the presence of class label noise. Numéro de notice : A2017-896 Affiliation des auteurs : LaSTIG MATIS+Ext (2012-2019) Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : doi.org/10.3390/rs9020173 date de publication en ligne : 18/02/2017 En ligne : https://doi.org/10.3390/rs9020173 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=91880 [article]

New iterative learning strategy to improve classification systems by using outlier detection techniques / Charlotte Pelletier (2017)  

Public Titre : New iterative learning strategy to improve classification systems by using outlier detection techniques Type de document : Article/Communication Auteurs : Charlotte Pelletier, Auteur ; Silvia Valero, Auteur ; Jordi Inglada, Auteur ; Gérard Dedieu, Auteur ; Nicolas Champion , Auteur Editeur : New York : Institute of Electrical and Electronics Engineers IEEE Année de publication : 2017 Conférence : IGARSS 2017, IEEE International Geoscience And Remote Sensing Symposium 23/07/2017 28/07/2017 Fort Worth Texas - Etats-Unis Proceedings IEEE Importance : pp 3676 - 3679 Format : 21 x 30 cm Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes descripteurs IGN] apprentissage automatique [Termes descripteurs IGN] classification dirigée [Termes descripteurs IGN] classification par forêts aléatoires [Termes descripteurs IGN] détection d'anomalie [Termes descripteurs IGN] itération [Termes descripteurs IGN] valeur aberrante Résumé : (auteur) The supervised classification of satellite image time series allows obtaining reliable land cover maps over large areas. However, their quality depends on the reference datasets used for training the classifier. In remote sensing, reference data may lack of timeliness and accuracy which leads to the presence of mislabeled data degrading the classification performances. This work presents an iterative learning framework to deal with noisy instances, that can be seen as outliers. Several outlier detection strategies, based on the well-known Random Forests (RF) ensemble classifier, are proposed, evaluated quantitatively, and then compared with traditional methods. Experimental results have been carried out by using synthetic and real datasets representing annual vegetation profiles. Numéro de notice : C2017-042 Affiliation des auteurs : IGN+Ext (2012-2019) Thématique : IMAGERIE Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : 10.1109/IGARSS.2017.8127796 date de publication en ligne : 04/12/2017 En ligne : https://doi.org/10.1109/IGARSS.2017.8127796 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=91925

Assessing the robustness of Random Forests to map land cover with high resolution satellite image time series over large areas / Charlotte Pelletier in Remote sensing of environment, vol 187 (15 December 2016)  

Public [article]  inRemote sensing of environment > vol 187 (15 December 2016) . - pp 156 - 168 Titre : Assessing the robustness of Random Forests to map land cover with high resolution satellite image time series over large areas Type de document : Article/Communication Auteurs : Charlotte Pelletier, Auteur ; Silvia Valero, Auteur ; Jordi Inglada, Auteur ; Nicolas Champion , Auteur ; Gérard Dedieu, Auteur Année de publication : 2016 Article en page(s) : pp 156 - 168 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes descripteurs IGN] caractérisation [Termes descripteurs IGN] carte d'occupation du sol [Termes descripteurs IGN] classification par forêts aléatoires [Termes descripteurs IGN] classification par séparateurs à vaste marge [Termes descripteurs IGN] France (administrative) [Termes descripteurs IGN] image Landsat-8 [Termes descripteurs IGN] image Sentinel [Termes descripteurs IGN] image SPOT 4 [Termes descripteurs IGN] méthode robuste [Termes descripteurs IGN] précision de la classification [Termes descripteurs IGN] série temporelle Résumé : (Auteur) New remote sensing sensors will acquire High spectral, spatial and temporal Resolution Satellite Image Time Series (HR-SITS). These new data are of great interest to map land cover thanks to the combination of the three high resolutions that will allow a depiction of scene dynamics. However, their efficient exploitation involves new challenges, especially for adapting traditional classification schemes to data complexity. More specifically, it requires: (1) to determine which classifier algorithms can handle the amount and the variability of data; (2) to evaluate the stability of classifier parameters; (3) to select the best feature set used as input data in order to find the good trade-off between classification accuracy and computational time; and (4) to establish the classifier accuracy over large areas. This work aims at studying these different issues, and more especially at demonstrating the ability of state-of-the-art classifiers, such as Random Forests (RF) or Support Vector Machines (SVM), to classify HR-SITS. For this purpose, several studies are carried out by using SPOT-4 and Landsat-8 HR-SITS in the south of France. Firstly, the choice of the classifier is discussed by comparing RF and SVM algorithms on HR-SITS. Both classifiers show their ability to tackle the classification problem with an Overall Accuracy (OA) of 83.3 % for RF and 77.1 % for SVM. But RF have some advantages such as a small training time, and an easy parameterization. Secondly, the stability of RF parameters is appraised. RF parameters appear to cause little influence on the classification accuracy, about 1% OA difference between the worst and the best parameter configuration. Thirdly, different input data – composed of spectral bands with or without spectral and/or temporal features – are proposed in order to enhance the characterization of land cover. The addition of features improves the classification accuracy, but the gain in OA is weak compared with the increase in the computational cost. Eventually, the classifier accuracy is assessed on a larger area where the landscape variabilities affect the classification performances. Numéro de notice : A2016--109 Affiliation des auteurs : IGN+Ext (2012-2019) Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.rse.2016.10.010 date de publication en ligne : 15/10/2016 En ligne : http://doi.org/10.1016/j.rse.2016.10.010 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=84726 [article]

An assessment of image features and random forest for land cover mapping over large areas using high resolution Satellite Image Time Series / Charlotte Pelletier (2016)  

Public Titre : An assessment of image features and random forest for land cover mapping over large areas using high resolution Satellite Image Time Series Type de document : Article/Communication Auteurs : Charlotte Pelletier, Auteur ; Silvia Valero, Auteur ; Jordi Inglada, Auteur ; Gérard Dedieu, Auteur ; Nicolas Champion , Auteur Congrès : IGARSS 2016, International Geoscience And Remote Sensing Symposium (10 - 15 juillet 2016; Pékin, Chine) , Commanditaire Editeur : New York : Institute of Electrical and Electronics Engineers IEEE Année de publication : 2016 Note générale : bibliographie Langues : Français (fre) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes descripteurs IGN] carte d'occupation du sol [Termes descripteurs IGN] classification par forêts aléatoires [Termes descripteurs IGN] image à haute résolution [Termes descripteurs IGN] image Landsat-8 [Termes descripteurs IGN] image SPOT 4 [Termes descripteurs IGN] série temporelle Résumé : (auteur) New high resolution Satellite Image Time Series (SITS) are becoming crucial to land cover mapping over large areas. Their high temporal resolution will allow to better depict scene dynamics. However, it will also increase the amount of data to process. The classification of these data involves therefore new challenges such as: (1) selecting the best feature set to use as input data, (2) dealing with data variability coming from landscape diversity, and (3) establishing the robustness of existing classifiers over large areas. This work aims at addressing these questions through three different studies. Experimental results are obtained by using SPOT-4 and Landsat-8 SITS. Numéro de notice : C2016-034 Affiliation des auteurs : MATIS+Ext (1993-2011) Thématique : IMAGERIE Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : 10.1109/IGARSS.2016.7729863 date de publication en ligne : 03/11/2016 En ligne : https://doi.org/10.1109/IGARSS.2016.7729863 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=91791

Automatic detection of clouds and shadows using high resolution satellite image time series / Nicolas Champion (2016)    

Public contenu dans 23th ISPRS international congress, 3. Commission III / Lena Halounova (2016)   Titre : Automatic detection of clouds and shadows using high resolution satellite image time series Type de document : Article/Communication Auteurs : Nicolas Champion , Auteur Editeur : International Society for Photogrammetry and Remote Sensing ISPRS Année de publication : 2016 Collection : International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, ISSN 1682-1750 num. XLI-B3 Projets : 1-Pas de projet / Conférence : ISPRS 2016, Commission 3, 23th international congress 12/07/2016 19/07/2016 Prague République tchèque Archives Commission 3 Importance : pp 475 - 479 Format : 21 x 30 cm Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes descripteurs IGN] détection d'ombre [Termes descripteurs IGN] détection des nuages [Termes descripteurs IGN] image Landsat-8 [Termes descripteurs IGN] image Pléiades-HR [Termes descripteurs IGN] orthoimage [Termes descripteurs IGN] réflectance de surface [Termes descripteurs IGN] séquence d'images [Termes descripteurs IGN] série temporelle Résumé : (auteur) Detecting clouds and their shadows is one of the primaries steps to perform when processing satellite images because they may alter the quality of some products such as large-area orthomosaics. The main goal of this paper is to present the automatic method developed at IGN-France for detecting clouds and shadows in a sequence of satellite images. In our work, surface reflectance orthoimages are used. They were processed from initial satellite images using a dedicated software. The cloud detection step consists of a region-growing algorithm. Seeds are firstly extracted. For that purpose and for each input ortho-image to process, we select the other ortho-images of the sequence that intersect it. The pixels of the input ortho-image are secondly labelled seeds if the difference of reflectance (in the blue channel) with overlapping ortho-images is bigger than a given threshold. Clouds are eventually delineated using a region-growing method based on a radiometric and homogeneity criterion. Regarding the shadow detection, our method is based on the idea that a shadow pixel is darker when comparing to the other images of the time series. The detection is basically composed of three steps. Firstly, we compute a synthetic ortho-image covering the whole study area. Its pixels have a value corresponding to the median value of all input reflectance ortho-images intersecting at that pixel location. Secondly, for each input ortho-image, a pixel is labelled shadows if the difference of reflectance (in the NIR channel) with the synthetic ortho-image is below a given threshold. Eventually, an optional region-growing step may be used to refine the results. Note that pixels labelled clouds during the cloud detection are not used for computing the median value in the first step; additionally, the NIR input data channel is used to perform the shadow detection, because it appeared to better discriminate shadow pixels. The method was tested on times series of Landsat 8 and Pléiades-HR images and our first experiments show the feasibility to automate the detection of shadows and clouds in satellite image sequences. Numéro de notice : C2016-038 Affiliation des auteurs : IGN (2012-2019) Thématique : IMAGERIE Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : 10.5194/isprs-archives-XLI-B3-475-2016 date de publication en ligne : 09/06/2016 En ligne : http://dx.doi.org/10.5194/isprs-archives-XLI-B3-475-2016 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=91849

Documents numériques

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Pinastéréo, estimation de la hauteur dominante et de la biomasse forestière dans le massif des Landes de Gascogne à partir d'images stéréoscopiques Pléiades / Thierry Bélouard in Revue Française de Photogrammétrie et de Télédétection, n° 209 (Janvier 2015)

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Extracting polygonal building footprints from digital surface models: A fully-automatic global optimization framework / Mathieu Brédif in ISPRS Journal of photogrammetry and remote sensing, vol 77 (March 2013)  

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Automatic cloud detection from multi-temporal satellite images: towards the use of Pléiades time series / Nicolas Champion (2012)  

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Détection de changement 2D à partir d’imagerie satellitaire : Application à la mise à jour des bases de données géographiques / Nicolas Champion (2011)    

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2D building change detection from high resolution satelliteimagery: A two-step hierarchical method based on 3D invariant primitives / Nicolas Champion in Pattern recognition letters, vol 31 n° 10 (15 July 2010)  

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Detection of unregistered buildings for updating 2D databases / Nicolas Champion (01/12/2009)

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A test of automatic building change detection approaches / Nicolas Champion (01/12/2009) 

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Automatic estimation of fine terrain models from multiple high-resolution satellite images / Nicolas Champion (07/11/2009)

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Automatic GIS updating from high resolution satellite images / Nicolas Champion (22/05/2009) 

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Automatic revision of 2D building databases from high resolution satellite imagery : a 3D photogrammetric approach / Nicolas Champion (2009)    

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