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A 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)
[article]
Titre : A context feature enhancement network for building extraction from high-resolution remote sensing imagery Type de document : Article/Communication Auteurs : Jinzhi Chen, Auteur ; Dejun Zhang, Auteur ; Yiqi Wu, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 2276 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] apprentissage profond
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] détection de contours
[Termes IGN] détection du bâti
[Termes IGN] image à haute résolution
[Termes IGN] structure-from-motionRésumé : (auteur) The complexity and diversity of buildings make it challenging to extract low-level and high-level features with strong feature representation by using deep neural networks in building extraction tasks. Meanwhile, deep neural network-based methods have many network parameters, which take up a lot of memory and time in training and testing. We propose a novel fully convolutional neural network called the Context Feature Enhancement Network (CFENet) to address these issues. CFENet comprises three modules: the spatial fusion module, the focus enhancement module, and the feature decoder module. First, the spatial fusion module aggregates the spatial information of low-level features to obtain buildings’ outline and edge information. Secondly, the focus enhancement module fully aggregates the semantic information of high-level features to filter the information of building-related attribute categories. Finally, the feature decoder module decodes the output of the above two modules to segment the buildings more accurately. In a series of experiments on the WHU Building Dataset and the Massachusetts Building Dataset, our CFENet balances efficiency and accuracy compared to the other four methods we compared, and achieves optimality on all five evaluation metrics: PA, PC, F1, IoU, and FWIoU. This indicates that CFENet can effectively enhance and fuse buildings’ low-level and high-level features, improving building extraction accuracy. Numéro de notice : A2022-385 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.3390/rs14092276 Date de publication en ligne : 09/05/2022 En ligne : https://doi.org/10.3390/rs14092276 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100663
in Remote sensing > vol 14 n° 9 (May-1 2022) . - n° 2276[article]Human cognition based framework for detecting roads from remote sensing images / Naveen Chandra in Geocarto international, vol 37 n° 8 ([01/05/2022])
[article]
Titre : Human cognition based framework for detecting roads from remote sensing images Type de document : Article/Communication Auteurs : Naveen Chandra, Auteur ; Himadri Vaidya, Auteur ; Jayanta Kumar Ghosh, Auteur Année de publication : 2022 Article en page(s) : pp 2365 - 2384 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse comparative
[Termes IGN] analyse d'image numérique
[Termes IGN] classification
[Termes IGN] cognition
[Termes IGN] extraction du réseau routier
[Termes IGN] image à haute résolution
[Termes IGN] interprétation (psychologie)
[Termes IGN] représentation cognitive
[Termes IGN] segmentation d'imageRésumé : (auteur) The complete extraction of roads from remote sensing images (RSIs) is an emergent area of research. It is an interesting topic as it involves diverse procedures for detecting roads. The detection of roads using high-resolution-satellite-images (HRSi) is challenging because of the occurrence of several types of noise such as bridges, vehicles, and crossing lines, etc. The extraction of the correct road network is crucial due to its broad range of applications such as transportation, map updating, navigation, and generating maps. Therefore our paper concentrates on understanding the cognitive processes, reasoning, and knowledge used by the analyst through visual cognition while performing the task of road detection from HRSi. The novel process is performed emulating human cognition within cognitive task analysis which is carried out in five different stages. The suggested cognitive procedure for road extraction is validated with the fifteen HRSi of four different land cover patterns specifically developed-sub-urban (DSUr), developed-urban (DUr), emerging-sub-urban (ESUr), and emerging-urban (EUr). The experimental results and the comparative assessment prove the impact of the presented cognitive method. Numéro de notice : A2022-506 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2020.1810330 Date de publication en ligne : 14/10/2020 En ligne : https://doi.org/10.1080/10106049.2020.1810330 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101027
in Geocarto international > vol 37 n° 8 [01/05/2022] . - pp 2365 - 2384[article]Plastic waste cleanup priorities to reduce marine pollution: A spatiotemporal analysis for Accra and Lagos with satellite data / Susmita Dasgupta in Science of the total environment, vol 839 (May 2022)
[article]
Titre : Plastic waste cleanup priorities to reduce marine pollution: A spatiotemporal analysis for Accra and Lagos with satellite data Type de document : Article/Communication Auteurs : Susmita Dasgupta, Auteur ; Maria Sarraf, Auteur ; David M. Wheeler, Auteur Année de publication : 2022 Article en page(s) : n° 156319 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] analyse spatio-temporelle
[Termes IGN] déchet
[Termes IGN] distribution spatiale
[Termes IGN] enquête
[Termes IGN] géoréférencement
[Termes IGN] Ghana
[Termes IGN] image à haute résolution
[Termes IGN] Lagos
[Termes IGN] littoral
[Termes IGN] matière plastique
[Termes IGN] méthode du maximum de vraisemblance (estimation)
[Termes IGN] pollution des mers
[Termes IGN] variation saisonnièreRésumé : (auteur) Plastic waste, with an estimated lifetime of centuries, accounts for the major share of marine litter. Each year, thousands of fish, sea birds, sea turtles, and other marine species are killed by ingesting or becoming entangled with plastic debris. Reducing marine plastic pollution is particularly challenging for developing countries owing to the wide dispersal of plastic waste disposal and scarce public cleanup resources. To costeffectively reduce marine pollution, resources should target “hotspot” areas, where large volumes of plastic litter have a high likelihood of ending up in the ocean. Using new public information, this study develops a hotspot targeting strategy for Accra and Lagos, which are major sources of marine plastic pollution in West Africa. The same global information sources can support hotspot analyses for many other coastal cities that generate marine plastic waste. The methodology combines georeferenced household survey data on plastic use, measures of seasonal variation in marine plastic pollution from satellite imagery, and a model of plastic waste transport to the ocean that uses information on topography, seasonal rainfall, drainage to rivers, and river transport to the ocean. For cleanup, the results for West Africa assign the highest locational priority to areas with heavy plastic-waste disposal along river channels or in steeply sloped locations with high rainfall runoff potential near rivers. They assign the highest temporal priority to just before the onset of the first-semester rainy season, when runoff from the first rains transports large volumes of plastic waste that have accumulated during the dry season. Numéro de notice : A2022-471 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1016/j.scitotenv.2022.156319 Date de publication en ligne : 28/05/2022 En ligne : https://doi.org/10.1016/j.scitotenv.2022.156319 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100816
in Science of the total environment > vol 839 (May 2022) . - n° 156319[article]Smartphone digital photography for fractional vegetation cover estimation / Gaofei Yin in Photogrammetric Engineering & Remote Sensing, PERS, vol 88 n° 5 (May 2022)
[article]
Titre : Smartphone digital photography for fractional vegetation cover estimation Type de document : Article/Communication Auteurs : Gaofei Yin, Auteur ; Yonghua Qu, Auteur ; Aleixandre Verger, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 303 - 310 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Acquisition d'image(s) et de donnée(s)
[Termes IGN] analyse comparative
[Termes IGN] champ visuel
[Termes IGN] couvert végétal
[Termes IGN] erreur moyenne quadratique
[Termes IGN] forêt alpestre
[Termes IGN] image à haute résolution
[Termes IGN] image hémisphérique
[Termes IGN] objectif grand angulaire
[Termes IGN] téléphone intelligentRésumé : (Auteur) Accurate ground measurements of fractional vegetation cover (FVC) are key for characterizing ecosystem functions and evaluating remote sensing products. The increasing performance of cameras equipped in smartphones opens new opportunities for extensive FVC measurement through citizen science initiatives. However, the wide field of view (FOV) of smartphone cameras constitutes a key source of uncertainty in the estimation of vegetation parameters, which has been largely ignored. We designed a practical method to characterize the FOV of smartphones and improve the FVC estimation. The method was assessed in a mountainous forest based on the comparison with in situ fisheye photographs. After the FOV correction, the agreement of smart-phone and fisheye FVC estimates highly improved: root-mean-square error (RMSE) of 0.103 compared to 0.242 of the original smartphone FVC estimates without considering the FOV effect, mean difference of 0.074 versus 0.213, and coefficient of determination R 2 of 0.719 versus 0.353. Smartphone cameras outperform traditional fisheye cameras: the overexposure and low vertical resolution of fisheye photographs introduced uncertainties in FVCestimation while the insensitivity to exposure and high spatial resolution of smartphone cameras make photograph acquisition and analysis more automatic and accurate. The smartphone FVCestimates highly agree with the GF-1 satellite product: RMSE = 0.066, bias = 0.007, and R 2 = 0.745. This study opens new perspectives for the validation of satellite products. Numéro de notice : A2022-527 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.21-00038R2 Date de publication en ligne : 01/05/2022 En ligne : https://doi.org/10.14358/PERS.21-00038R2 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101375
in Photogrammetric Engineering & Remote Sensing, PERS > vol 88 n° 5 (May 2022) . - pp 303 - 310[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 105-2022052 SL Revue Centre de documentation Revues en salle Disponible 105-2022051 SL Revue Centre de documentation Revues en salle Disponible 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)
[article]
Titre : Research on machine intelligent perception of urban geographic location based on high resolution remote sensing images Type de document : Article/Communication Auteurs : Jun Chen, Auteur ; Cunjian Yang, Auteur ; Zengyang Yu, Auteur Année de publication : 2022 Article en page(s) : pp 223 - 231 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] base de données
[Termes IGN] Chine
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] cognition
[Termes IGN] détection d'objet
[Termes IGN] extraction automatique
[Termes IGN] géolocalisation
[Termes IGN] image à haute résolution
[Termes IGN] intelligence artificielle
[Termes IGN] reconnaissance automatique
[Termes IGN] zone urbaineRésumé : (auteur) Machine intelligent perception (MIP) provides a novel way for human beings to recognize geographical locations automatically. MIP of geographical locations enables computers to describe locations automatically and quantitatively by extracting Earth's surface features and building relationships. The earth surface fingerprint is established here by mining the relationship between spatial objects with stable characteristics extracted from urban high-resolution remote sensing images, which realizes intelligent perception of geographical location innovatively. Mask Region-based Convolutional Neural Network is used to automatically extract the spatial objects such as playgrounds, crossroads, and bridges from the images. Then, the extracted spatial objects are encoded according to the landuse type, distance, and angle of 24 nearest objects to construct urban surface fingerprint database. The urban surface fingerprint database is used to match the geographical location of spatial objects in local images so that the matching algorithm can be used for machine recognition of the geographical location of specific objects in the target image. Taking the main cities in China as the experimental area, the success rate of location perception is 92%. We have made a useful exploration in the field of MIP of geographical location, hoping to promote the development of human cognition of geographical location. Numéro de notice : A2022-285 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.21-00017R3 Date de publication en ligne : 04/04/2022 En ligne : https://doi.org/10.14358/PERS.21-00017R3 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100319
in Photogrammetric Engineering & Remote Sensing, PERS > vol 88 n° 4 (April 2022) . - pp 223 - 231[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 105-2022041 SL Revue Centre de documentation Revues en salle Disponible Extraction from high-resolution remote sensing images based on multi-scale segmentation and case-based reasoning / Jun Xu in Photogrammetric Engineering & Remote Sensing, PERS, vol 88 n° 3 (March 2022)PermalinkAirborne LiDAR and high resolution multispectral data integration in Eucalyptus tree species mapping in an Australian farmscape / Niva Kiran Verma in Geocarto international, vol 37 n° 1 ([01/01/2022])PermalinkSemantic segmentation of high-resolution remote sensing images based on a class feature attention mechanism fused with Deeplabv3+ / Zhimin Wang in Computers & geosciences, vol 158 (January 2022)PermalinkAdaptive feature weighted fusion nested U-Net with discrete wavelet transform for change detection of high-resolution remote sensing images / Congcong Wang in Remote sensing, vol 13 n° 24 (December-2 2021)PermalinkEfficient occluded road extraction from high-resolution remote sensing imagery / Dejun Feng in Remote sensing, vol 13 n° 24 (December-2 2021)PermalinkMSegnet, a practical network for building detection from high spatial resolution images / Bo Yu in Photogrammetric Engineering & Remote Sensing, PERS, vol 87 n° 12 (December 2021)PermalinkParticle swarm optimization based water index (PSOWI) for mapping the water extents from satellite images / Mohammad Hossein Gamshadzaei in Geocarto international, vol 36 n° 20 ([01/12/2021])PermalinkSemi-automatic extraction of rural roads under the constraint of combined geometric and texture features / Hai Tan in ISPRS International journal of geo-information, vol 10 n° 11 (November 2021)PermalinkRecognition of crevasses with high-resolution digital elevation models: Application of geomorphometric modeling and texture analysis / Olga T. Ishalina in Transactions in GIS, vol 25 n° 5 (October 2021)PermalinkAutomatic building detection with polygonizing and attribute extraction from high-resolution images / Samitha Daranagama in ISPRS International journal of geo-information, vol 10 n° 9 (September 2021)PermalinkGeoglam, l'agriculture par satellite / Laurent Polidori in Géomètre, n° 2194 (septembre 2021)PermalinkUnsupervised representation high-resolution remote sensing image scene classification via contrastive learning convolutional neural network / Fengpeng Li in Photogrammetric Engineering & Remote Sensing, PERS, vol 87 n° 8 (August 2021)PermalinkSemiCDNet: A semisupervised convolutional neural network for change detection in high resolution remote-sensing images / Daifeng Peng in IEEE Transactions on geoscience and remote sensing, Vol 59 n° 7 (July 2021)PermalinkA unified framework of bundle adjustment and feature matching for high-resolution satellite images / Xiao Ling in Photogrammetric Engineering & Remote Sensing, PERS, vol 87 n° 7 (July 2021)PermalinkA high-resolution satellite DEM filtering method assisted with building segmentation / Yihui Li in Photogrammetric Engineering & Remote Sensing, PERS, vol 87 n° 6 (June 2021)PermalinkResolution enhancement for large-scale land cover mapping via weakly supervised deep learning / Qiutong Yu in Photogrammetric Engineering & Remote Sensing, PERS, vol 87 n° 6 (June 2021)PermalinkA compilation of snow cover datasets for Svalbard: A multi-sensor, multi-model study / Hannah Vickers in Remote sensing, vol 13 n°10 (May-2 2021)PermalinkLearning from multimodal and multitemporal earth observation data for building damage mapping / Bruno Adriano in ISPRS Journal of photogrammetry and remote sensing, vol 175 (May 2021)PermalinkNumerical modelling for analysis of the effect of different urban green spaces on urban heat load patterns in the present and in the future / Tamás Gál in Computers, Environment and Urban Systems, vol 87 (May 2021)PermalinkA novel class-specific object-based method for urban change detection using high-resolution remote sensing imagery / Ting Bai in Photogrammetric Engineering & Remote Sensing, PERS, vol 87 n° 4 (April 2021)Permalink