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Unsupervised multi-view CNN for salient view selection and 3D interest point detection / Ran Song in International journal of computer vision, vol 130 n° 5 (May 2022)  

Public [article]  inInternational journal of computer vision > vol 130 n° 5 (May 2022) . - pp 1210 - 1227 Titre : Unsupervised multi-view CNN for salient view selection and 3D interest point detection Type de document : Article/Communication Auteurs : Ran Song, Auteur ; Wei Zhang, Auteur ; Yitian Zhao, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 1210 - 1227 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] apprentissage profond [Termes IGN] classification non dirigée [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] détection d'objet [Termes IGN] objet 3D [Termes IGN] point d'intérêt [Termes IGN] saillance Résumé : (auteur) We present an unsupervised 3D deep learning framework based on a ubiquitously true proposition named by us view-object consistency as it states that a 3D object and its projected 2D views always belong to the same object class. To validate its effectiveness, we design a multi-view CNN instantiating it for salient view selection and interest point detection of 3D objects, which quintessentially cannot be handled by supervised learning due to the difficulty of collecting sufficient and consistent training data. Our unsupervised multi-view CNN, namely UMVCNN, branches off two channels which encode the knowledge within each 2D view and the 3D object respectively and also exploits both intra-view and inter-view knowledge of the object. It ends with a new loss layer which formulates the view-object consistency by impelling the two channels to generate consistent classification outcomes. The UMVCNN is then integrated with a global distinction adjustment scheme to incorporate global cues into salient view selection. We evaluate our method for salient view section both qualitatively and quantitatively, demonstrating its superiority over several state-of-the-art methods. In addition, we showcase that our method can be used to select salient views of 3D scenes containing multiple objects. We also develop a method based on the UMVCNN for 3D interest point detection and conduct comparative evaluations on a publicly available benchmark, which shows that the UMVCNN is amenable to different 3D shape understanding tasks. Numéro de notice : A2022-415 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1007/s11263-022-01592-x Date de publication en ligne : 16/03/2022 En ligne : https://doi.org/10.1007/s11263-022-01592-x Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100771 [article]

Wood decay detection in Norway spruce forests based on airborne hyperspectral and ALS data / Michele Dalponte in Remote sensing, vol 14 n° 8 (April-2 2022)  

Public [article]  inRemote sensing > vol 14 n° 8 (April-2 2022) . - n° 1892 Titre : Wood decay detection in Norway spruce forests based on airborne hyperspectral and ALS data Type de document : Article/Communication Auteurs : Michele Dalponte, Auteur ; Alvar J. I. Kallio, Auteur ; Hans Ole Ørka, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 1892 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques [Termes IGN] bois sur pied [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] dépérissement [Termes IGN] données d'entrainement (apprentissage automatique) [Termes IGN] données lidar [Termes IGN] image hyperspectrale [Termes IGN] image infrarouge [Termes IGN] Norvège [Termes IGN] Perceptron multicouche [Termes IGN] Picea abies [Termes IGN] régression linéaire [Termes IGN] régression logistique [Termes IGN] santé des forêts [Termes IGN] semis de points Résumé : (auteur) Wood decay caused by pathogenic fungi in Norway spruce forests causes severe economic losses in the forestry sector, and currently no efficient methods exist to detect infected trees. The detection of wood decay could potentially lead to improvements in forest management and could help in reducing economic losses. In this study, airborne hyperspectral data were used to detect the presence of wood decay in the trees in two forest areas located in Etnedal (dataset I) and Gran (dataset II) municipalities, in southern Norway. The hyperspectral data used consisted of images acquired by two sensors operating in the VNIR and SWIR parts of the spectrum. Corresponding ground reference data were collected in Etnedal using a cut-to-length harvester while in Gran, field measurements were collected manually. Airborne laser scanning (ALS) data were used to detect the individual tree crowns (ITCs) in both sites. Different approaches to deal with pixels inside each ITC were considered: in particular, pixels were either aggregated to a unique value per ITC (i.e., mean, weighted mean, median, centermost pixel) or analyzed in an unaggregated way. Multiple classification methods were explored to predict rot presence: logistic regression, feed forward neural networks, and convolutional neural networks. The results showed that wood decay could be detected, even if with accuracy varying among the two datasets. The best results on the Etnedal dataset were obtained using a convolution neural network with the first five components of a principal component analysis as input (OA = 65.5%), while on the Gran dataset, the best result was obtained using LASSO with logistic regression and data aggregated using the weighted mean (OA = 61.4%). In general, the differences among aggregated and unaggregated data were small. Numéro de notice : A2022-352 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE/INFORMATIQUE Nature : Article DOI : 10.3390/rs14081892 Date de publication en ligne : 14/04/2022 En ligne : https://doi.org/10.3390/rs14081892 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100541 [article]

Assessing surface drainage conditions at the street and neighborhood scale: A computer vision and flow direction method applied to lidar data / Cheng-Chun Lee in Computers, Environment and Urban Systems, vol 93 (April 2022)  

Public [article]  inComputers, Environment and Urban Systems > vol 93 (April 2022) . - n° 101755 Titre : Assessing surface drainage conditions at the street and neighborhood scale: A computer vision and flow direction method applied to lidar data Type de document : Article/Communication Auteurs : Cheng-Chun Lee, Auteur ; Nasir G. Gharaibeh, Auteur Année de publication : 2022 Article en page(s) : n° 101755 Note générale : bibliogrphie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] drainage [Termes IGN] écoulement des eaux [Termes IGN] Houston (Texas) [Termes IGN] inondation [Termes IGN] lidar mobile [Termes IGN] modèle numérique de surface [Termes IGN] ruissellement [Termes IGN] segmentation sémantique Résumé : (auteur) Surface drainage at the neighborhood and street scales plays an important role in conveying stormwater and mitigating urban flooding. Surface drainage at the local scale is often ignored due to the lack of up-to-date fine-scale topographical information. This paper addresses this issue by providing a novel method for evaluating surface drainage at the neighborhood and street scales based on mobile lidar (light detection and ranging) measurements. The developed method derives topographical properties and runoff accumulation by applying a semantic segmentation (SS) model (a computer vision technique) and a flow direction model (a hydrology technique) to lidar data. Fifty lidar images representing 50 street blocks were used to train, validate, and test the SS model. Based on the test dataset, the SS model has 80.3% IoU and 88.5% accuracy. The results suggest that the proposed method can effectively evaluate surface drainage conditions at both the neighborhood and street scales and identify problematic low points that could be susceptible to water ponding. Municipalities and property owners can use this information to take targeted corrective maintenance actions. Numéro de notice : A2022-120 Affiliation des auteurs : non IGN Thématique : IMAGERIE/URBANISME Nature : Article DOI : 10.1016/j.compenvurbsys.2021.101755 Date de publication en ligne : 13/01/2022 En ligne : https://doi.org/10.1016/j.compenvurbsys.2021.101755 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99661 [article]

Comparison of neural networks and k-nearest neighbors methods in forest stand variable estimation using airborne laser data / Andras Balazs in ISPRS Open Journal of Photogrammetry and Remote Sensing, vol 4 (April 2022)  

Public [article]  inISPRS Open Journal of Photogrammetry and Remote Sensing > vol 4 (April 2022) . - n° 100012 Titre : Comparison of neural networks and k-nearest neighbors methods in forest stand variable estimation using airborne laser data Type de document : Article/Communication Auteurs : Andras Balazs, Auteur ; Eero Liski, Auteur ; Sakari Tuominen, Auteur Année de publication : 2022 Article en page(s) : n° 100012 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] algorithme génétique [Termes IGN] bois sur pied [Termes IGN] classification barycentrique [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] covariance [Termes IGN] diamètre à hauteur de poitrine [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] Finlande [Termes IGN] hauteur des arbres [Termes IGN] inventaire forestier étranger (données) [Termes IGN] peuplement forestier [Termes IGN] réseau neuronal artificiel [Termes IGN] semis de points [Termes IGN] volume en bois Résumé : (auteur) In the remote sensing of forests, point cloud data from airborne laser scanning contains high-value information for predicting the volume of growing stock and the size of trees. At the same time, laser scanning data allows a very high number of potential features that can be extracted from the point cloud data for predicting the forest variables. In some methods, the features are first extracted by user-defined algorithms and the best features are selected based on supervised learning, whereas both tasks can be carried out automatically by deep learning methods typically based on deep neural networks. In this study we tested k-nearest neighbor method combined with genetic algorithm (k-NN), artificial neural network (ANN), 2-dimensional convolutional neural network (2D-CNN) and 3-dimensional CNN (3D-CNN) for estimating the following forest variables: volume of growing stock, stand mean height and mean diameter. The results indicate that there were no major differences in the accuracy of the tested methods, but the ANN and 3D-CNN generally resulted in the lowest RMSE values for the predicted forest variables and the highest R2 values between the predicted and observed forest variables. The lowest RMSE scores were 20.3% (3D-CNN), 6.4% (3D-CNN) and 11.2% (ANN) and the highest R2 results 0.90 (3D-CNN), 0.95 (3D-CNN) and 0.85 (ANN) for volume of growing stock, stand mean height and mean diameter, respectively. Covariances of all response variable combinations and all predictions methods were lower than corresponding covariances of the field observations. ANN predictions had the highest covariances for mean height vs. mean diameter and total growing stock vs. mean diameter combinations and 3D-CNN for mean height vs. total growing stock. CNNs have distinct theoretical advantage over the other methods in complex recognition or classification tasks, but the utilization of their full potential may possibly require higher point density clouds than applied here. Thus, the relatively low density of the point clouds data may have been a contributing factor to the somewhat inconclusive ranking of the methods in this study. The input data and computer codes are available at: https://github.com/balazsan/ALS_NNs. Numéro de notice : A2022-265 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.ophoto.2022.100012 Date de publication en ligne : 12/03/2022 En ligne : https://doi.org/10.1016/j.ophoto.2022.100012 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100263 [article]

A convolution neural network for forest leaf chlorophyll and carotenoid estimation using hyperspectral reflectance / Shuo Shi in International journal of applied Earth observation and geoinformation, vol 108 (April 2022)  

Public [article]  inInternational journal of applied Earth observation and geoinformation > vol 108 (April 2022) . - n° 102719 Titre : A convolution neural network for forest leaf chlorophyll and carotenoid estimation using hyperspectral reflectance Type de document : Article/Communication Auteurs : Shuo Shi, Auteur ; Lu Xu, Auteur ; Wei Gong, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 102719 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] chlorophylle [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] écosystème forestier [Termes IGN] feuille (végétation) [Termes IGN] modèle de transfert radiatif [Termes IGN] processus gaussien [Termes IGN] réflectance spectrale [Termes IGN] régression Résumé : (auteur) Forest leaf chlorophyll (Cab) and carotenoid (Cxc) are key functional indicators for the state of the forest ecosystem. Current machine learning models based on hyperspectral reflectance are widely applied to estimate leaf Cab and Cxc contents at leaf scale. However, these models have certain accuracy for non-independent datasets but have poor generalization for independent datasets when they are used to estimate leaf Cab and Cxc contents. This fact limits that hyperspectral remote sensing completely replaces destructive measurements for leaf Cab and Cxc contents. Thus, the development of an estimation model with high accuracy and satisfactory generalization is necessary. Convolutional neural networks (CNNs) have certain accuracy and generalization in many domains, and have the potential to solve above-mentioned problem. Therefore, this study developed a CNN using one-dimensional hyperspectral reflectance, which aimed to improve the model's accuracy and generalization in leaf Cab and Cxc content estimation at leaf scale. The proposed CNN was developed by three steps. First, in consideration of the correlation between leaf Cab and Cxc contents in natural leaves, 2500 physical data with leaf reflectance and corresponding Cab and Cxc contents were generated by leaf radiative transfer model and multivariable gaussian distribution function. Then, the proposed CNN was built by five strategies based on the architecture of the AlexNet. Finally, five-fold cross validation was performed with 70% of the physical data to determine the best strategy to develop the proposed CNN. These were executed to ensure the proposed CNN with the maximum accuracy and generalization. In addition, the accuracy and generalization of the proposed CNN were tested using a non-independent dataset and an independent dataset, respectively. The proposed CNN was also compared with back propagation neural network (BPNN), support vector regression (SVR) and gaussian process regression (GPR). Results showed that the best CNN could be developed with one input, five convolutional, three max-pooling and three fully-connected layers. Comprehensively considering the model's accuracy and generalization, the proposed CNN was the best model for leaf Cab and Cxc content estimation compared with BPNN, SVR and GPR. This study provides a development strategy of CNN estimation model using one-dimensional hyperspectral reflectance at leaf scale. The proposed CNN could further promote the practical application of hyperspectral remote sensing in leaf Cab and Cxc content estimation. Numéro de notice : A2022-231 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.1016/j.jag.2022.102719 Date de publication en ligne : 16/02/2022 En ligne : https://doi.org/10.1016/j.jag.2022.102719 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100119 [article]

Deep generative model for spatial–spectral unmixing with multiple endmember priors / Shuaikai Shi in IEEE Transactions on geoscience and remote sensing, vol 60 n° 4 (April 2022)  

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Deep learning for archaeological object detection on LiDAR: New evaluation measures and insights / Marco Fiorucci in Remote sensing, vol 14 n° 7 (April-1 2022)  

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Enriching the metadata of map images: a deep learning approach with GIS-based data augmentation / Yingjie Hu in International journal of geographical information science IJGIS, vol 36 n° 4 (April 2022)  

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Exploring scientific literature by textual and image content using DRIFT / Ximena Pocco in Computers and graphics, vol 103 (April 2022)  

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Meta-learning based hyperspectral target detection using siamese network / Yulei Wang in IEEE Transactions on geoscience and remote sensing, vol 60 n° 4 (April 2022)  

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Research on machine intelligent perception of urban geographic location based on high resolution remote sensing images / Jun Chen in Photogrammetric Engineering & Remote Sensing, PERS, vol 88 n° 4 (April 2022)  

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Spatially oriented convolutional neural network for spatial relation extraction from natural language texts / Qinjun Qiu in Transactions in GIS, vol 26 n° 2 (April 2022)  

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Neural map style transfer exploration with GANs / Sidonie Christophe in International journal of cartography, vol 8 n° 1 (March 2022)  

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Traffic sign three-dimensional reconstruction based on point clouds and panoramic images / Minye Wang in Photogrammetric record, vol 37 n° 177 (March 2022)  

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Ultrahigh-resolution boreal forest canopy mapping: Combining UAV imagery and photogrammetric point clouds in a deep-learning-based approach / Linyuan Li in International journal of applied Earth observation and geoinformation, vol 107 (March 2022)  

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