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Investigating the impact of pan sharpening on the accuracy of land cover mapping in Landsat OLI imagery / Komeil Rokni in Geodesy and cartography, vol 49 n° 1 (January 2023)
[article]
Titre : Investigating the impact of pan sharpening on the accuracy of land cover mapping in Landsat OLI imagery Type de document : Article/Communication Auteurs : Komeil Rokni, Auteur Année de publication : 2023 Article en page(s) : pp 12 - 18 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] algorithme de Gram-Schmidt
[Termes IGN] carte d'occupation du sol
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] image Landsat-OLI
[Termes IGN] image multibande
[Termes IGN] image panchromatique
[Termes IGN] Kappa de Cohen
[Termes IGN] matrice de confusion
[Termes IGN] pansharpening (fusion d'images)
[Termes IGN] précision de la classificationRésumé : (auteur) Pan Sharpening is normally applied to sharpen a multispectral image with low resolution by using a panchromatic image with a higher resolution, to generate a high resolution multispectral image. The present study aims at assessing the power of Pan Sharpening on improvement of the accuracy of image classification and land cover mapping in Landsat 8 OLI imagery. In this respect, different Pan Sharpening algorithms including Brovey, Gram-Schmidt, NNDiffuse, and Principal Components were applied to merge the Landsat OLI panchromatic band (15 m) with the Landsat OLI multispectral: visible and infrared bands (30 m), to generate a new multispectral image with a higher spatial resolution (15 m). Subsequently, the support vector machine approach was utilized to classify the original Landsat and resulting Pan Sharpened images to generate land cover maps of the study area. The outcomes were then compared through the generation of confusion matrix and calculation of kappa coefficient and overall accuracy. The results indicated superiority of NNDiffuse algorithm in Pan Sharpening and improvement of classification accuracy in Landsat OLI imagery, with an overall accuracy and kappa coefficient of about 98.66% and 0.98, respectively. Furthermore, the result showed that the Gram-Schmidt and Principal Components algorithms also slightly improved the accuracy of image classification compared to original Landsat image. The study concluded that image Pan Sharpening is useful to improve the accuracy of image classification in Landsat OLI imagery, depending on the Pan Sharpening algorithm used for this purpose. Numéro de notice : A2023-142 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.3846/gac.2023.15308 Date de publication en ligne : 17/02/2023 En ligne : https://doi.org/10.3846/gac.2023.15308 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102712
in Geodesy and cartography > vol 49 n° 1 (January 2023) . - pp 12 - 18[article]
Titre : Learnable Earth Parser: Discovering 3D Prototypes in Aerial Scans Type de document : Article/Communication Auteurs : Romain Loiseau , Auteur ; Elliot Vincent, Auteur ; Mathieu Aubry, Auteur ; Loïc Landrieu , Auteur Editeur : Ithaca [New York - Etats-Unis] : ArXiv - Université Cornell Année de publication : 2023 Importance : 18 p. Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] information complexe
[Termes IGN] scène 3D
[Termes IGN] semis de points
[Termes IGN] zone urbaineRésumé : (auteur) We propose an unsupervised method for parsing large 3D scans of real-world scenes into interpretable parts. Our goal is to provide a practical tool for analyzing 3D scenes with unique characteristics in the context of aerial surveying and mapping, without relying on application-specific user annotations. Our approach is based on a probabilistic reconstruction model that decomposes an input 3D point cloud into a small set of learned prototypical shapes. Our model provides an interpretable reconstruction of complex scenes and leads to relevant instance and semantic segmentations. To demonstrate the usefulness of our results, we introduce a novel dataset of seven diverse aerial LiDAR scans. We show that our method outperforms state-of-the-art unsupervised methods in terms of decomposition accuracy while remaining visually interpretable. Our method offers significant advantage over existing approaches, as it does not require any manual annotations, making it a practical and efficient tool for 3D scene analysis. Our code and dataset are available at https://imagine.enpc.fr/~loiseaur/learnable-earth-parser Numéro de notice : P2023-005 Affiliation des auteurs : UGE-LASTIG+Ext (2020- ) Thématique : IMAGERIE/INFORMATIQUE Nature : Preprint nature-HAL : Préprint DOI : sans En ligne : https://hal.science/hal-04135416 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=103347 A method for remote sensing image classification by combining Pixel Neighbourhood Similarity and optimal feature combination / Kaili Zhang in Geocarto international, vol 38 n° 1 ([01/01/2023])
[article]
Titre : A method for remote sensing image classification by combining Pixel Neighbourhood Similarity and optimal feature combination Type de document : Article/Communication Auteurs : Kaili Zhang, Auteur ; Yonggang Chen, Auteur ; Wentao Wang, Auteur ; et al., Auteur Année de publication : 2023 Article en page(s) : n° 2158948 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse spatiale
[Termes IGN] analyse spectrale
[Termes IGN] classification Spectral angle mapper
[Termes IGN] classification spectrale
[Termes IGN] corrélation automatique de points homologues
[Termes IGN] données vectorielles
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] pixel
[Termes IGN] précision de la classification
[Termes IGN] signature texturale
[Termes IGN] similitude spectrale
[Termes IGN] voisinage (relation topologique)Résumé : (auteur) In the study of remote sensing image classification, feature extraction and selection is an effective method to distinguish different classification targets. Constructing a high-quality spectral-spatial feature and feature combination has been a worthwhile topic for improving classification accuracy. In this context, this study constructed a spectral-spatial feature, namely the Pixel Neighbourhood Similarity (PNS) index. Meanwhile, the PNS index and 19 spectral, textural and terrain features were involved in the Correlation-based Feature Selection (CFS) algorithm for feature selection to generate a feature combination (PNS-CFS). To explore how PNS and PNS-CFS improve the classification accuracy of land types. The results show that: (1) The PNS index exhibited clear boundaries between different land types. The performance quality of PNS was relatively highest compared to other spectral-spatial features, namely the Vector Similarity (VS) index, the Change Vector Intensity (CVI) index and the Correlation (COR) index. (2) The Overall Accuracy (OA) of the PNS-CFS was 94.66% and 93.59% in study areas 1 and 2, respectively. These were 7.48% and 6.02% higher than the original image data (ORI) and 7.27% and 2.39% higher than the single-dimensional feature combination (SIN-CFS). Compared to the feature combinations of VS, CVI, and COR indices (VS-CFS, CVI-COM, COR-COM), PNS-CFS had the relatively highest performance and classification accuracy. The study demonstrated that the PNS index and PNS-CFS have a high potential for image classification. Numéro de notice : A2023-059 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2022.2158948 Date de publication en ligne : 03/01/2023 En ligne : https://doi.org/10.1080/10106049.2022.2158948 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102397
in Geocarto international > vol 38 n° 1 [01/01/2023] . - n° 2158948[article]Multi-information PointNet++ fusion method for DEM construction from airborne LiDAR data / Hong Hu in Geocarto international, vol 38 n° 1 ([01/01/2023])
[article]
Titre : Multi-information PointNet++ fusion method for DEM construction from airborne LiDAR data Type de document : Article/Communication Auteurs : Hong Hu, Auteur ; Guanghe Zhang, Auteur ; Jianfeng Ao, Auteur ; et al., Auteur Année de publication : 2023 Article en page(s) : n° 2153929 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques
[Termes IGN] apprentissage profond
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] filtrage de points
[Termes IGN] image RVB
[Termes IGN] Kappa de Cohen
[Termes IGN] modèle numérique de surface
[Termes IGN] Perceptron multicouche
[Termes IGN] segmentation
[Termes IGN] semis de pointsRésumé : (auteur) Airborne light detection and ranging (LiDAR) is a popular technology in remote sensing that can significantly improve the efficiency of digital elevation model (DEM) construction. However, it is challenging to identify the real terrain features in complex areas using LiDAR data. To solve this problem, this work proposes a multi-information fusion method based on PointNet++ to improve the accuracy of DEM construction. The RGB data and normalized coordinate information of the point cloud was added to increase the number of channels on the input side of the PointNet++ neural network, which can improve the accuracy of the classification during feature extraction. Low and high density point clouds obtained from the International Society for Photogrammetry and Remote Sensing (ISPRS) and the United States Geological Survey (USGS) were used to test this proposed method. The results suggest that the proposed method improves the Kappa coefficient by 8.81% compared to PointNet++. The type I error was reduced by 2.13%, the type II error was reduced by 8.29%, and the total error was reduced by 2.52% compared to the conventional algorithm. Therefore, it is possible to conclude that the proposed method can obtain DEMs with higher accuracy. Numéro de notice : A2023-056 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1080/10106049.2022.2153929 Date de publication en ligne : 23/12/2022 En ligne : https://doi.org/10.1080/10106049.2022.2153929 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102389
in Geocarto international > vol 38 n° 1 [01/01/2023] . - n° 2153929[article]PSMNet-FusionX3 : LiDAR-guided deep learning stereo dense matching on aerial images / Teng Wu (2023)
Titre : PSMNet-FusionX3 : LiDAR-guided deep learning stereo dense matching on aerial images Type de document : Article/Communication Auteurs : Teng Wu , Auteur ; Bruno Vallet , Auteur ; Marc Pierrot-Deseilligny , Auteur Editeur : Computer vision foundation CVF Année de publication : 2023 Conférence : CVPR 2023, IEEE Conference on Computer Vision and Pattern Recognition workshops 18/06/2023 22/06/2023 Vancouver Colombie britannique - Canada OA Proceedings Importance : pp 6526 - 6535 Note générale : bibliographie
voir aussi https://openaccess.thecvf.com/content/CVPR2023W/PCV/supplemental/Wu_PSMNet-FusionX3_LiDAR-Guided_Deep_CVPRW_2023_supplemental.pdfLangues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques
[Termes IGN] appariement dense
[Termes IGN] apprentissage profond
[Termes IGN] chaîne de traitement
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] image aérienne à axe vertical
[Termes IGN] scène 3D
[Termes IGN] Triangulated Irregular NetworkRésumé : (auteur) Dense image matching (DIM) and LiDAR are two complementary techniques for recovering the 3D geometry of real scenes. While DIM provides dense surfaces, they are often noisy and contaminated with outliers. Conversely, LiDAR is more accurate and robust, but less dense and more expensive compared to DIM. In this work, we investigate learning-based methods to refine surfaces produced by photogrammetry with sparse LiDAR point clouds. Unlike the current state-of-the-art approaches in the computer vision community, our focus is on aerial acquisitions typical in photogrammetry. We propose a densification pipeline that adopts a PSMNet backbone with triangulated irregular network interpolation based expansion, feature enhancement in cost volume, and conditional cost volume normalization, i.e. PSMNet-FusionX3. Our method works better on low density and is less sensitive to distribution, demonstrating its effectiveness across a range of LiDAR point cloud densities and distributions, including analyses of dataset shifts. Furthermore, we have made both our aerial (image and disparity) dataset and code available for public use. Further information can be found at https://github.com/ whuwuteng/PSMNet-FusionX3. Numéro de notice : C2023-006 Affiliation des auteurs : UGE-LASTIG (2020- ) Thématique : IMAGERIE/INFORMATIQUE Nature : Communication DOI : sans En ligne : https://openaccess.thecvf.com/content/CVPR2023W/PCV/papers/Wu_PSMNet-FusionX3_Li [...] Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=103277 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)PermalinkTree species classification in a typical natural secondary forest using UAV-borne LiDAR and hyperspectral data / Ying Quan in GIScience and remote sensing, vol 60 n° 1 (2023)PermalinkAbove ground biomass estimation from UAV high resolution RGB images and LiDAR data in a pine forest in Southern Italy / Mauro Maesano in iForest, biogeosciences and forestry, vol 15 n° 6 (December 2022)PermalinkAutomatic registration method of multi-source point clouds based on building facades matching in urban scenes / Yumin Tan in Photogrammetric Engineering & Remote Sensing, PERS, vol 88 n° 12 (December 2022)PermalinkAutomatic registration of point cloud and panoramic images in urban scenes based on pole matching / Yuan Wang in International journal of applied Earth observation and geoinformation, vol 115 (December 2022)PermalinkBathymetry and benthic habitat mapping in shallow waters from Sentinel-2A imagery: A case study in Xisha islands, China / Wei Huang in IEEE Transactions on geoscience and remote sensing, vol 60 n° 12 (December 2022)PermalinkBayesian hyperspectral image super-resolution in the presence of spectral variability / Fei Ye in IEEE Transactions on geoscience and remote sensing, vol 60 n° 12 (December 2022)PermalinkFusion of SAR and multi-spectral time series for determination of water table depth and lake area in peatlands / Katrin Krzepek in PFG – Journal of Photogrammetry, Remote Sensing and Geoinformation Science, vol 90 n° 6 (December 2022)PermalinkGeoreferencing accuracy assessment of historical aerial photos using a custom-built online georeferencing tool / Su Zhang in ISPRS International journal of geo-information, vol 11 n° 12 (December 2022)PermalinkHyperspectral imagery and urban areas: results of the HYEP project / Christiane Weber in Revue Française de Photogrammétrie et de Télédétection, n° 224 (2022)Permalink