Centre dedocumentation
scientifique

Automated terrain feature identification from remote sensing imagery: a deep learning approach / Wenwen Li in International journal of geographical information science IJGIS, vol 34 n° 4 (April 2020)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 34 n° 4 (April 2020) . - pp 637 - 660 Titre : Automated terrain feature identification from remote sensing imagery: a deep learning approach Type de document : Article/Communication Auteurs : Wenwen Li, Auteur ; Chia-Yu Hsu, Auteur Année de publication : 2020 Article en page(s) : pp 637 - 660 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes descripteurs IGN] analyse d'image orientée objet [Termes descripteurs IGN] analyse du paysage [Termes descripteurs IGN] apprentissage profond [Termes descripteurs IGN] base de données d'images [Termes descripteurs IGN] classification par réseau neuronal convolutif [Termes descripteurs IGN] compréhension de l'image [Termes descripteurs IGN] détection automatique [Termes descripteurs IGN] détection d'objet [Termes descripteurs IGN] extraction de traits caractéristiques [Termes descripteurs IGN] intelligence artificielle Résumé : (auteur) Terrain feature detection is a fundamental task in terrain analysis and landscape scene interpretation. Discovering where a specific feature (i.e. sand dune, crater, etc.) is located and how it evolves over time is essential for understanding landform processes and their impacts on the environment, ecosystem, and human population. Traditional induction-based approaches are challenged by their inefficiency for generalizing diverse and complex terrain features as well as their performance for scalable processing of the massive geospatial data available. This paper presents a new deep learning (DL) approach to support automatic detection of terrain features from remotely sensed images. The novelty of this work lies in: (1) a terrain feature database containing 12,000 remotely sensed images (1,000 original images and 11,000 derived images from data augmentation) that supports data-driven model training and new discovery; (2) a DL-based object detection network empowered by ensemble learning and deep and deeper convolutional neural networks to achieve high-accuracy object detection; and (3) fine-tuning the model’s characteristics and behaviors to identify the best combination of hyperparameters and other network factors. The introduction of DL into geospatial applications is expected to contribute significantly to intelligent terrain analysis, landscape scene interpretation, and the maturation of spatial data science. Numéro de notice : A2020-108 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2018.1542697 date de publication en ligne : 07/11/2018 En ligne : https://doi.org/10.1080/13658816.2018.1542697 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94708 [article]

Wavelet and non-parametric statistical based approach for long term land cover trend analysis using time series EVI data / Niraj Priyadarshi in Geocarto international, vol 35 n° 5 ([01/04/2020])  

Public [article]  inGeocarto international > vol 35 n° 5 [01/04/2020] . - pp 512 - 534 Titre : Wavelet and non-parametric statistical based approach for long term land cover trend analysis using time series EVI data Type de document : Article/Communication Auteurs : Niraj Priyadarshi, Auteur ; V.M. Chowdary, Auteur ; Iswar Chandra Das, Auteur ; Jeganathan Chockalingam, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 512 - 534 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes descripteurs IGN] changement d'occupation du sol [Termes descripteurs IGN] Enhanced vegetation index [Termes descripteurs IGN] filtrage du bruit [Termes descripteurs IGN] image MODIS [Termes descripteurs IGN] série temporelle [Termes descripteurs IGN] transformation en ondelettes Résumé : (auteur) Land cover change analysis was carried out using Moderate Resolution Imaging Spectroradiometer (MODIS) Enhanced Vegetation Index (EVI) time series data for the period 2005–2014. MODIS EVI data coupled with Quality Assessment Science Data Sets (QASDS) was de-noised with Savitzky–Golay filter while enhancing quality and preserving the temporal profile of EVI. Wavelet transform (WT) based approach along with Sen slope’s method was used for land cover change and trend analysis. The WT based approach is useful for studying multiscale and non-stationary processes. Mann–Kendall test was performed to confirm the significance of the identified trends. Proposed approach identified 358 locations as change points, where 285 (79.6%) and 73 (20.4%) locations were detected as ‘Change’ and ‘False Change’ with respect to high resolution images. The proposed approach is useful for monitoring land cover changes that provide vital inputs for sustainable management of land resources. Numéro de notice : A2020-143 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2018.1520925 date de publication en ligne : 24/10/2018 En ligne : https://doi.org/10.1080/10106049.2018.1520925 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94769 [article]

Deep learning for geometric and semantic tasks in photogrammetry and remote sensing / Christian Helpke in Geo-spatial Information Science, vol 23 n°1 (March 2020)  

Public [article]  inGeo-spatial Information Science > vol 23 n°1 (March 2020) . - pp 10 - 19 Titre : Deep learning for geometric and semantic tasks in photogrammetry and remote sensing Type de document : Article/Communication Auteurs : Christian Helpke, Auteur ; Franz Rottensteiner, Auteur Année de publication : 2020 Article en page(s) : pp 10 - 19 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes descripteurs IGN] apprentissage automatique [Termes descripteurs IGN] apprentissage profond [Termes descripteurs IGN] approche géométrique [Termes descripteurs IGN] classification par réseau neuronal convolutif [Termes descripteurs IGN] image aérienne [Termes descripteurs IGN] intelligence artificielle [Termes descripteurs IGN] photogrammétrie numérique [Termes descripteurs IGN] télédétection Résumé : (auteur) During the last few years, artificial intelligence based on deep learning, and particularly based on convolutional neural networks, has acted as a game changer in just about all tasks related to photogrammetry and remote sensing. Results have shown partly significant improvements in many projects all across the photogrammetric processing chain from image orientation to surface reconstruction, scene classification as well as change detection, object extraction and object tracking and recognition in image sequences. This paper summarizes the foundations of deep learning for photogrammetry and remote sensing before illustrating, by way of example, different projects being carried out at the Institute of Photogrammetry and GeoInformation, Leibniz University Hannover, in this exciting and fast moving field of research and development. Numéro de notice : A2020-161 Thématique : IMAGERIE Nature : Article DOI : 10.1080/10095020.2020.1718003 date de publication en ligne : 03/02/2020 En ligne : https://doi.org/https://doi.org/10.1080/10095020.2020.1718003 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94821 [article]

Edge-reinforced convolutional neural network for road detection in very-high-resolution remote sensing imagery / Xiaoyan Lu in Photogrammetric Engineering & Remote Sensing, PERS, vol 86 n° 3 (March 2020)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 86 n° 3 (March 2020) . - pp 153 - 160 Titre : Edge-reinforced convolutional neural network for road detection in very-high-resolution remote sensing imagery Type de document : Article/Communication Auteurs : Xiaoyan Lu, Auteur ; Yanfei Zhong, Auteur ; Zhuo Zheng, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 153 - 160 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes descripteurs IGN] accentuation de contours [Termes descripteurs IGN] analyse multiéchelle [Termes descripteurs IGN] classification par réseau neuronal convolutif [Termes descripteurs IGN] extraction de traits caractéristiques [Termes descripteurs IGN] extraction du réseau routier [Termes descripteurs IGN] filtrage du bruit [Termes descripteurs IGN] image à très haute résolution [Termes descripteurs IGN] ombre [Termes descripteurs IGN] segmentation d'image Résumé : (auteur) Road detection in very-high-resolution remote sensing imagery is a hot research topic. However, the high resolution results in highly complex data distributions, which lead to much noise for road detection—for example, shadows and occlusions caused by disturbance on the roadside make it difficult to accurately recognize road. In this article, a novel edge-reinforced convolutional neural network, combined with multiscale feature extraction and edge reinforcement, is proposed to alleviate this problem. First, multiscale feature extraction is used in the center part of the proposed network to extract multiscale context information. Then edge reinforcement, applying a simplified U-Net to learn additional edge information, is used to restore the road information. The two operations can be used with different convolutional neural networks. Finally, two public road data sets are adopted to verify the effectiveness of the proposed approach, with experimental results demonstrating its superiority. Numéro de notice : A2020-145 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.86.3.153 date de publication en ligne : 01/03/2020 En ligne : https://doi.org/10.14358/PERS.86.3.153 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94774 [article]

Heuristic sample learning for complex urban scenes: Application to urban functional-zone mapping with VHR images and POI data / Xiuyuan Zhang in ISPRS Journal of photogrammetry and remote sensing, vol 161 (March 2020)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 161 (March 2020) . - pp 1 - 12 Titre : Heuristic sample learning for complex urban scenes: Application to urban functional-zone mapping with VHR images and POI data Type de document : Article/Communication Auteurs : Xiuyuan Zhang, Auteur ; Shihong Du, Auteur ; Zhijia Zheng, Auteur Année de publication : 2020 Article en page(s) : pp 1 - 12 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes descripteurs IGN] analyse d'image orientée objet [Termes descripteurs IGN] apprentissage dirigé [Termes descripteurs IGN] apprentissage semi-dirigé [Termes descripteurs IGN] cartographie urbaine [Termes descripteurs IGN] Chine [Termes descripteurs IGN] échantillonnage d'image [Termes descripteurs IGN] image à très haute résolution [Termes descripteurs IGN] méthode heuristique [Termes descripteurs IGN] point d'intérêt [Termes descripteurs IGN] scène urbaine Résumé : (Auteur) Urban functional zones are basic units of urban planning and resource allocation, and contribute to a wide range of urban studies and investigations. Existing studies on functional-zone mapping with very-high-resolution (VHR) satellite images focused much on feature representations and classification techniques, but ignored zone sampling which however was fundamental to automatic zone classifications. Functional-zone sampling is much complicated and can hardly be resolved by classical sampling methods, as functional zones are complex urban scenes which consist of heterogeneous land covers and have highly abstract categories. To resolve the issue, this study presents a novel sampling paradigm, i.e., heuristic sample learning (HSL). It first proposes a sparse topic model to select representative functional zones, then uses deep forest to select confusing zones, and finally embraces Chinese restaurant process to label these selected zones. The presented method collects both representative and confusing zone samples and identifies their categories accurately, which makes the functional-zone classification process robust and the classification results accurate. Experiments conducted in Beijing indicate that HSL is effective and efficient for functional-zone sampling and classifications. Compared to traditional manual sampling, HSL reduces the time cost by 55% and improves the classification accuracy by 11.3% on average; furthermore, HSL can reduce the variation in sampling and classification results caused by different proficiency of operators. Accordingly, HSL significantly contributes to functional-zone mapping and plays an important role in urban studies. Numéro de notice : A2020-061 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2020.01.005 date de publication en ligne : 13/01/2020 En ligne : https://doi.org/10.1016/j.isprsjprs.2020.01.005 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94577 [article]

Réservation

Réserver ce document

Exemplaires (3)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
081-2020031	SL	Revue	Centre de documentation	Revues en salle	Disponible
081-2020033	DEP-RECP	Revue	MATIS	Dépôt en unité	Exclu du prêt
081-2020032	DEP-RECF	Revue	Nancy	Dépôt en unité	Exclu du prêt

Poststack seismic data denoising based on 3-D convolutional neural network / Dawei Liu in IEEE Transactions on geoscience and remote sensing, vol 58 n° 3 (March 2020)  

Permalink

Simultaneous intensity bias estimation and stripe noise removal in infrared images using the global and local sparsity constraints / Li Liu in IEEE Transactions on geoscience and remote sensing, vol 58 n° 3 (March 2020)  

Permalink

The application of bidirectional reflectance distribution function data to recognize the spatial heterogeneity of mixed pixels in vegetation remote sensing: a simulation study / Yanan Yan in Photogrammetric Engineering & Remote Sensing, PERS, vol 86 n° 3 (March 2020)  

Permalink

Some thoughts on measuring earthquake deformation using optical imagery / Min Huang in IEEE Transactions on geoscience and remote sensing, vol 58 n° 2 (February 2020)  

Permalink

Comparison of multi-seasonal Landsat 8, Sentinel-2 and hyperspectral images for mapping forest alliances in Northern California / Matthew L. Clark in ISPRS Journal of photogrammetry and remote sensing, vol 159 (January 2020)  

Permalink

Superpixel-enhanced deep neural forest for remote sensing image semantic segmentation / Li Mi in ISPRS Journal of photogrammetry and remote sensing, vol 159 (January 2020)  

Permalink

Validating the use of object-based image analysis to map commonly recognized landform features in the United States / Samantha T. Arundel in Cartography and Geographic Information Science, Vol 46 n° 5 (September 2019)  

Permalink

Pyramid scene parsing network in 3D: Improving semantic segmentation of point clouds with multi-scale contextual information / Hao Fang in ISPRS Journal of photogrammetry and remote sensing, vol 154 (August 2019)  

Permalink

Co‐registration of panoramic mobile mapping images and oblique aerial images / Phillipp Jende in Photogrammetric record, vol 34 n° 166 (June 2019)  

Permalink

RoofN3D: a database for 3D building reconstruction with deep learning / Andreas Wichmann in Photogrammetric Engineering & Remote Sensing, PERS, vol 85 n° 6 (June 2019)  

Permalink