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An informal road detection neural network for societal impact in developing countries / Inger Fabris-Rotelli in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol V-4-2022 (2022 edition)
[article]
Titre : An informal road detection neural network for societal impact in developing countries Type de document : Article/Communication Auteurs : Inger Fabris-Rotelli, Auteur ; Abraham Wannenburg, Auteur ; Gao Maribe, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 267 - 274 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image
[Termes IGN] Afrique du sud (état)
[Termes IGN] apprentissage profond
[Termes IGN] données étiquetées d'entrainement
[Termes IGN] extraction du réseau routier
[Termes IGN] image satellite
[Termes IGN] impact social
[Termes IGN] pays en développement
[Termes IGN] réseau neuronal artificielRésumé : (auteur) Roads found in informal settlements arise out of convenience, and are often not recorded or maintained by authorities. This complicates service delivery, sustainable development and crisis mitigation, including management and tracking of COVID-19. We, therefore, aim to extract informal roads in remote sensing images. Existing techniques aiming at the extraction of formal roads are not suitable for the problem due to the complex physical and spectral properties of informal roads. The only existing approaches for informal roads, namely (Nobrega et al., 2006, Thiede et al., 2020), do not consider neural networks as a solution. Neural networks show promise in overcoming these complexities. However, they require a large amount of data to learn, which is currently not available due to the expensive and time-consuming nature of collecting such data. This paper implements a neural network to extract informal roads from a data set digitised by this research group. Data quality is assessed by calculating validity completeness, homogeneity and the V-measure, a measure of consistency, in order to evaluate the overall usability of the dataset for neural network informal road detection. We implement the GANs-UNet model that obtained the highest F1-score in a 2020 review paper (Abdollahi et al., 2020) on the state-of-the-art deep learning models used to extract formal roads. The results indicate that the model is able to extract informal roads successfully in the presence of appropriate training data. Numéro de notice : A2022-424 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Article DOI : 10.5194/isprs-annals-V-4-2022-267-2022 Date de publication en ligne : 18/05/2022 En ligne : https://doi.org/10.5194/isprs-annals-V-4-2022-267-2022 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100729
in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences > vol V-4-2022 (2022 edition) . - pp 267 - 274[article]Application oriented quality evaluation of Gaofen-7 optical stereo satellite imagery / Jiaojiao Tian in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol V-1-2022 (2022 edition)
[article]
Titre : Application oriented quality evaluation of Gaofen-7 optical stereo satellite imagery Type de document : Article/Communication Auteurs : Jiaojiao Tian, Auteur ; Xiangyu Zhuo, Auteur ; Xiangtian Yuan, Auteur ; Corentin Henry, Auteur ; Pablo d' Angelo, Auteur ; Thomas Krauss, Auteur Année de publication : 2022 Article en page(s) : pp 145 - 152 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] Allemagne
[Termes IGN] détection du bâti
[Termes IGN] extraction du réseau routier
[Termes IGN] image Gaofen
[Termes IGN] image optique
[Termes IGN] orientation d'image
[Termes IGN] reconstruction 3D
[Termes IGN] scène urbaine
[Termes IGN] segmentationRésumé : (auteur) GaoFen-7 (GF-7) satellite mission is further expanding the very high resolution 3D mapping application. Carrying the first civilian Chinese sub-meter resolution stereo satellite sensors, GF-7 satellite was launched on November 7, 2019. With 0.65 meter resolution on backward view and 0.8 meter resolution forward view, GF-7 has been designed to meet the demand of natural resource monitoring, land surveying, and other mapping applications in China. The use of GF-7 for 3D city reconstruction is unfortunately restricted by the fixed large stereo view angle of forward and backward cameras with +26 and −5 degrees respectively which is not optimal for dense stereo matching in urban regions. In this paper, we intensively evaluate the quality of the GF-7 datasets by performing a series of urban monitoring applications, including road detection, building extraction and 3D reconstruction. In addition, we propose a 3D reconstruction workflow which uses the land cover classification result to refine the stereo matching result. Six sub-urban regions are selected from the available datasets in the middle of Germany. The results show that basic elements in urban scenes like buildings and roads could be detected from GF-7 datasets with high accuracy. With the proposed workflow, a 3D city model with a visually observed good quality can be delivered. Numéro de notice : A2022-442 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Article DOI : 10.5194/isprs-annals-V-1-2022-145-2022 Date de publication en ligne : 17/05/2022 En ligne : https://doi.org/10.5194/isprs-annals-V-1-2022-145-2022 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100776
in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences > vol V-1-2022 (2022 edition) . - pp 145 - 152[article]K-means clustering based on omnivariance attribute for building detection from airborne lidar data / Renato César Dos santos in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol V-2-2022 (2022 edition)
[article]
Titre : K-means clustering based on omnivariance attribute for building detection from airborne lidar data Type de document : Article/Communication Auteurs : Renato César Dos santos, Auteur ; Mauricio Galo, Auteur ; A.F. Habib, Auteur Année de publication : 2022 Article en page(s) : pp 111 - 118 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] classification par nuées dynamiques
[Termes IGN] détection du bâti
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] morphologie mathématique
[Termes IGN] semis de pointsRésumé : (auteur) Building detection is an important process in urban applications. In the last decades, 3D point clouds derived from airborne LiDAR have been widely explored. In this paper, we propose a building detection method based on K-means clustering and the omnivariance attribute derived from eigenvalues. The main contributions lie on the automatic detection without the need for training and optimal neighborhood definition for local attribute estimation. Additionally, one refinement step based on mathematical morphology (MM) operators to minimize the classification errors (commission and omission errors) is proposed. The experiments were conducted in three study areas. In general, the results indicated the potential of proposed method, presenting an average Fscore around 97%. Numéro de notice : A2022-431 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.5194/isprs-annals-V-2-2022-111-2022 Date de publication en ligne : 17/05/2022 En ligne : https://doi.org/10.5194/isprs-annals-V-2-2022-111-2022 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100737
in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences > vol V-2-2022 (2022 edition) . - pp 111 - 118[article]A new method to detect targets in hyperspectral images based on principal component analysis / Shahram Sharifi Hashjin in Geocarto international, vol 37 n° 9 ([15/05/2022])
[article]
Titre : A new method to detect targets in hyperspectral images based on principal component analysis Type de document : Article/Communication Auteurs : Shahram Sharifi Hashjin, Auteur ; Safa Khazai, Auteur Année de publication : 2022 Article en page(s) : pp 2679 - 2697 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse de groupement
[Termes IGN] analyse des mélanges spectraux
[Termes IGN] analyse en composantes principales
[Termes IGN] détection de cible
[Termes IGN] estimation de cohérence
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] image hyperspectraleRésumé : (auteur) Target detection (TD) is a major task in hyperspectral image (HSI) processing which, due to the high spectral resolution, requires dealing with the curse of dimensionality. The integrated feature extraction and selection is a well-known solution for dimensionality reduction of HSIs. In this study, a new method is presented to improve the performance of TD algorithms based on principal component analysis (PCA) feature space. In this method, using the implantation of the target spectrum (TS) in the HSI and following the simulated targets in the PCA feature space, the best principal components (PCs) are selected. Then, using the mixing and unmixing coefficients of the PCs, a new TS and a new image in the PCA feature space are created. Afterwards, using the new spectrum of the target, the TD algorithm is run on the new HSI. The performance of the proposed method is compared to nine counterpart algorithms on Hymap and Hyperion HSI. All the comparisons are performed using adaptive coherence estimator (ACE) TD algorithm. Experimental results illustrate that the proposed method, compared to its counterparts, yields superior performance based on the false alarm rate (FAR) measure. It gives an average FAR value of about 16, which is approximately 9% better than that of its best counterparts. Numéro de notice : A2022-568 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2020.1831625 Date de publication en ligne : 01/12/2020 En ligne : https://doi.org/10.1080/10106049.2020.1831625 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101251
in Geocarto international > vol 37 n° 9 [15/05/2022] . - pp 2679 - 2697[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 059-2022091 RAB Revue Centre de documentation En réserve L003 Disponible Research on automatic identification method of terraces on the Loess plateau based on deep transfer learning / Mingge Yu in Remote sensing, vol 14 n° 10 (May-2 2022)
[article]
Titre : Research on automatic identification method of terraces on the Loess plateau based on deep transfer learning Type de document : Article/Communication Auteurs : Mingge Yu, Auteur ; Xiaoping Rui, Auteur ; Weiyi Xie, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 2446 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] apprentissage profond
[Termes IGN] Chine
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] détection automatique
[Termes IGN] échantillonnage
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] image à haute résolution
[Termes IGN] image panchromatique
[Termes IGN] image Worldview
[Termes IGN] modèle de simulation
[Termes IGN] surface cultivée
[Termes IGN] terrasseRésumé : (auteur) Rapid, accurate extraction of terraces from high-resolution images is of great significance for promoting the application of remote-sensing information in soil and water conservation planning and monitoring. To solve the problem of how deep learning requires a large number of labeled samples to achieve good accuracy, this article proposes an automatic identification method for terraces that can obtain high precision through small sample datasets. Firstly, a terrace identification source model adapted to multiple data sources is trained based on the WorldView-1 dataset. The model can be migrated to other types of images for terracing extraction as a pre-trained model. Secondly, to solve the small sample problem, a deep transfer learning method for accurate pixel-level extraction of high-resolution remote-sensing image terraces is proposed. Finally, to solve the problem of insufficient boundary information and splicing traces during prediction, a strategy of ignoring edges is proposed, and a prediction model is constructed to further improve the accuracy of terrace identification. In this paper, three regions outside the sample area are randomly selected, and the OA, F1 score, and MIoU averages reach 93.12%, 91.40%, and 89.90%, respectively. The experimental results show that this method, based on deep transfer learning, can accurately extract terraced field surfaces and segment terraced field boundaries. Numéro de notice : A2022-402 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.3390/rs14102446 Date de publication en ligne : 19/05/2022 En ligne : https://doi.org/10.3390/rs14102446 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100705
in Remote sensing > vol 14 n° 10 (May-2 2022) . - n° 2446[article]3D lidar point-cloud projection operator and transfer machine learning for effective road surface features detection and segmentation / Heyang Thomas Li in The Visual Computer, vol 38 n° 5 (May 2022)PermalinkA context feature enhancement network for building extraction from high-resolution remote sensing imagery / Jinzhi Chen in Remote sensing, vol 14 n° 9 (May-1 2022)PermalinkA continuous change tracker model for remote sensing time series reconstruction / Yangjian Zhang in Remote sensing, vol 14 n° 9 (May-1 2022)PermalinkEfficient convolutional neural architecture search for LiDAR DSM classification / Aili Wang in IEEE Transactions on geoscience and remote sensing, vol 60 n° 5 (May 2022)PermalinkFramework for automatic coral reef extraction using Sentinel-2 image time series / Qizhi Zhang in Marine geodesy, vol 45 n° 3 (May 2022)PermalinkFusion of optical, radar and waveform LiDAR observations for land cover classification / Huiran Jin in ISPRS Journal of photogrammetry and remote sensing, vol 187 (May 2022)PermalinkHuman cognition based framework for detecting roads from remote sensing images / Naveen Chandra in Geocarto international, vol 37 n° 8 ([01/05/2022])PermalinkRevising cadastral data on land boundaries using deep learning in image-based mapping / Bujar Fetai in ISPRS International journal of geo-information, vol 11 n° 5 (May 2022)PermalinkDetermination of building flood risk maps from LiDAR mobile mapping data / Yu Feng in Computers, Environment and Urban Systems, vol 93 (April 2022)PermalinkExploring scientific literature by textual and image content using DRIFT / Ximena Pocco in Computers and graphics, vol 103 (April 2022)Permalink