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Polarimetric Synthetic Aperture Radar / Irena Hajnsek (2021)

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Titre :	Polarimetric Synthetic Aperture Radar : principles and application
Type de document :	Monographie
Auteurs :	Irena Hajnsek, Auteur ; Yves-Louis Desnos, Auteur
Editeur :	Berlin, Heidelberg, Vienne, New York, ... : Springer
Année de publication :	2021
Collection :	Remote sensing and digital image processing num. 25
Importance :	294 p.
ISBN/ISSN/EAN :	978-3-030-56504-6
Note générale :	Bibliographie
Langues :	Anglais (eng)
Descripteur :	[Vedettes matières IGN] Traitement d'image radar et applications [Termes IGN] agriculture [Termes IGN] bande X [Termes IGN] cryosphère [Termes IGN] détection d'arbres [Termes IGN] détection du bâti [Termes IGN] image ALOS-PALSAR [Termes IGN] image Radarsat [Termes IGN] image Terra [Termes IGN] occupation du sol [Termes IGN] polarimétrie radar [Termes IGN] radar à antenne synthétique [Termes IGN] zone humide
Index. décimale :	35.22 Télédétection en hyperfréquence - Traitement d'image radar
Résumé :	(Editeur) This open access book focuses on the practical application of electromagnetic polarimetry principles in Earth remote sensing with an educational purpose. In the last decade, the operations from fully polarimetric synthetic aperture radar such as the Japanese ALOS/PalSAR, the Canadian Radarsat-2 and the German TerraSAR-X and their easy data access for scientific use have developed further the research and data applications at L,C and X band. As a consequence, the wider distribution of polarimetric data sets across the remote sensing community boosted activity and development in polarimetric SAR applications, also in view of future missions. Numerous experiments with real data from spaceborne platforms are shown, with the aim of giving an up-to-date and complete treatment of the unique benefits of fully polarimetric synthetic aperture radar data in five different domains: forest, agriculture, cryosphere, urban and oceans.
Note de contenu :	- Basic Principles of SAR Polarimetry - Forest Applications - Agriculture and Wetland Applications - Cryosphere Applications - Urban Applications - Ocean Applications
Numéro de notice :	26542
Affiliation des auteurs :	non IGN
Thématique :	FORET/IMAGERIE/URBANISME
Nature :	Recueil / ouvrage collectif
DOI :	10.1007/978-3-030-56504-6
En ligne :	http://doi.org/10.1007/978-3-030-56504-6
Format de la ressource électronique :	URL
Permalink :	https://documentation.ensg.eu/index.php?lvl=notice_display&id=97766

Coral habitat mapping: a comparison between maximum likelihood, Bayesian and Dempster–Shafer classifiers / Mohammad Shawkat Hossain in Geocarto international, vol 36 n° 11 ([01/07/2021])

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[article]
inGeocarto international > vol 36 n° 11 [01/07/2021] . - pp 1217 - 1235

Titre :	Coral habitat mapping: a comparison between maximum likelihood, Bayesian and Dempster–Shafer classifiers
Type de document :	Article/Communication
Auteurs :	Mohammad Shawkat Hossain, Auteur ; Aidy M. Muslim, Auteur ; Muhammad Izuan Nadzri, Auteur ; et al., Auteur
Année de publication :	2021
Article en page(s) :	pp 1217 - 1235
Note générale :	bibliographie
Langues :	Anglais (eng)
Descripteur :	[Vedettes matières IGN] Applications de télédétection [Termes IGN] carte thématique [Termes IGN] classification bayesienne [Termes IGN] classification de Dempster-Shafer [Termes IGN] classification par maximum de vraisemblance [Termes IGN] classification pixellaire [Termes IGN] fond marin [Termes IGN] Google Earth [Termes IGN] habitat d'espèce [Termes IGN] image Quickbird [Termes IGN] Malaisie [Termes IGN] précision infrapixellaire [Termes IGN] récif corallien
Résumé :	(auteur) This study deals with the mixed-pixel problem of detecting benthic habitat class membership and evaluates two soft classifiers for coral habitat mapping on Lang Tengah island (Malaysia). A comparison was made between the Bayesian and Dempster–Shafer (D–S) with a traditional maximum likelihood (ML). The heterogeneous pattern of reef environment, established by field observation, four classes of coral habitats containing various combinations of live coral, dead coral with algae, rubble coral and sand. Posterior probability and belief maps, generated by Bayesian and D–S, respectively, were evaluated by visual inspection and final coral habitat distribution maps were validated via accuracy assessment estimates. The accuracy validation tests agreed with the visual inspection of the probability, uncertainty and coral distribution maps. The Bayesian algorithm performed better, with a 34.7–68.5% improvement in accuracy compared to D–S and ML, respectively. Probability maps demonstrate the advantages of the soft classifier over the hard classifier for coral mapping.
Numéro de notice :	A2021-435
Affiliation des auteurs :	non IGN
Thématique :	IMAGERIE
Nature :	Article
nature-HAL :	ArtAvecCL-RevueIntern
DOI :	10.1080/10106049.2019.1637466
date de publication en ligne :	10/07/2019
En ligne :	https://doi.org/10.1080/10106049.2019.1637466
Format de la ressource électronique :	URL article
Permalink :	https://documentation.ensg.eu/index.php?lvl=notice_display&id=97803

[article]

Detecting high-temperature anomalies from Sentinel-2 MSI images / Yongxue Liu in ISPRS Journal of photogrammetry and remote sensing, vol 177 (July 2021)

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[article]
inISPRS Journal of photogrammetry and remote sensing > vol 177 (July 2021) . - pp 174 - 193

Titre :	Detecting high-temperature anomalies from Sentinel-2 MSI images
Type de document :	Article/Communication
Auteurs :	Yongxue Liu, Auteur ; Zhi Weifeng, Auteur ; Bihua Xu, Auteur ; et al., Auteur
Année de publication :	2021
Article en page(s) :	pp 174 - 193
Note générale :	Bibliographie
Langues :	Anglais (eng)
Descripteur :	[Vedettes matières IGN] Applications de télédétection [Termes IGN] analyse comparative [Termes IGN] anomalie thermique [Termes IGN] éruption volcanique [Termes IGN] image aérienne [Termes IGN] image Landsat-OLI [Termes IGN] image proche infrarouge [Termes IGN] image Sentinel-MSI [Termes IGN] image thermique [Termes IGN] incendie [Termes IGN] réflectance spectrale [Termes IGN] risque technologique [Termes IGN] série temporelle [Termes IGN] température au sol
Résumé :	(Auteur) High-temperature anomalies (HTAs) of the earth's surface, such as fires, volcanic activities, and industrial heat sources, have a profound impact on Earth's system. Sentinel-2 Multispectral Instrument (MSI) provides spatially-specific information for precisely measuring the location and extent of HTAs at a fine scale. However, detecting HTAs from MSI images remains challenging because the emitted radiance of an HTA in the short-wave infrared (SWIR) bands can be easily mixed with the reflected solar radiance background in the daytime; and an increasing number of atypical cases in MSI images need to be treated with the enhanced spatial resolution. A generic HTA detection approach that handles both anthropogenic and natural HTAs will broaden the scope of MSI applications. In this study, (i) we highlight two spectral characteristics of HTAs in the far-SWIR, near-SWIR, and NIR bands (i.e., (ρfar-SWIR - ρnear-SWIR)/ρNIR ≥ 0.45 and (ρfar-SWIR -ρnear-SWIR) ≥ ρnear-SWIR - ρNIR) that can effectively enhance HTAs from background geo-features, based on the reflectance spectra in airborne imaging spectrometer data. (ii) We propose a tri-spectral thermal anomaly index (TAI) that jointly uses the two high-temperature-sensitive SWIR bands and the high-temperature-insensitive NIR band to enhance HTAs, based on the above characteristics and a comprehensive sampling of different types of HTAs from 1,974 MSI images. (iii) We develop a TAI-based approach for MSI images to detect HTAs in general. The proposed approach was applied to detect different types of HTAs, including different biomass burnings, active volcanoes, and industrial HTAs, over a wide range of land-cover scenarios. Validations and comparisons demonstrate the proposed approach is reliable and performs better than the existing state-of-the-art HTA detection approaches. Evaluations on two types of small industrial HTAs, including operating kilns and enclosed landfill gas flares, show that the HTA detection probability of the TAI-based approach from time-series MSI images is ~ 84.91% and 88.23%, respectively. Further investigations show that the TAI-based approach also has good transferability in detecting HTAs from multispectral images acquired by Landsat-family satellites.
Numéro de notice :	A2021-372
Affiliation des auteurs :	non IGN
Thématique :	IMAGERIE
Nature :	Article
nature-HAL :	ArtAvecCL-RevueIntern
DOI :	10.1016/j.isprsjprs.2021.05.008
date de publication en ligne :	23/05/2021
En ligne :	https://doi.org/10.1016/j.isprsjprs.2021.05.008
Format de la ressource électronique :	URL article
Permalink :	https://documentation.ensg.eu/index.php?lvl=notice_display&id=97808

[article]

A hierarchical deep learning framework for the consistent classification of land use objects in geospatial databases / Chun Yang in ISPRS Journal of photogrammetry and remote sensing, vol 177 (July 2021)

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[article]
inISPRS Journal of photogrammetry and remote sensing > vol 177 (July 2021) . - pp 38 - 56

Titre :	A hierarchical deep learning framework for the consistent classification of land use objects in geospatial databases
Type de document :	Article/Communication
Auteurs :	Chun Yang, Auteur ; Franz Rottensteiner, Auteur ; Christian Heipke, Auteur
Année de publication :	2021
Article en page(s) :	pp 38 - 56
Note générale :	Bibliographie
Langues :	Anglais (eng)
Descripteur :	[Vedettes matières IGN] Bases de données localisées [Termes IGN] Allemagne [Termes IGN] apprentissage profond [Termes IGN] approche hiérarchique [Termes IGN] classification automatique d'objets [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] image aérienne [Termes IGN] jointure [Termes IGN] objet géographique [Termes IGN] occupation du sol [Termes IGN] optimisation (mathématiques) [Termes IGN] utilisation du sol
Résumé :	(Auteur) Land use as contained in geospatial databases constitutes an essential input for different applications such as urban management, regional planning and environmental monitoring. In this paper, a hierarchical deep learning framework is proposed to verify the land use information. For this purpose, a two-step strategy is applied. First, given high-resolution aerial images, the land cover information is determined. To achieve this, an encoder-decoder based convolutional neural network (CNN) is proposed. Second, the pixel-wise land cover information along with the aerial images serves as input for another CNN to classify land use. Because the object catalogue of geospatial databases is frequently constructed in a hierarchical manner, we propose a new CNN-based method aiming to predict land use in multiple levels hierarchically and simultaneously. A so called Joint Optimization (JO) is proposed where predictions are made by selecting the hierarchical tuple over all levels which has the maximum joint class scores, providing consistent results across the different levels. The conducted experiments show that the CNN relying on JO outperforms previous results, achieving an overall accuracy up to 92.5%. In addition to the individual experiments on two test sites, we investigate whether data showing different characteristics can improve the results of land cover and land use classification, when processed together. To do so, we combine the two datasets and undertake some additional experiments. The results show that adding more data helps both land cover and land use classification, especially the identification of underrepresented categories, despite their different characteristics.
Numéro de notice :	A2021-370
Affiliation des auteurs :	non IGN
Thématique :	GEOMATIQUE/IMAGERIE
Nature :	Article
nature-HAL :	ArtAvecCL-RevueIntern
DOI :	10.1016/j.isprsjprs.2021.04.022
date de publication en ligne :	13/05/2021
En ligne :	https://doi.org/10.1016/j.isprsjprs.2021.04.022
Format de la ressource électronique :	URL Article
Permalink :	https://documentation.ensg.eu/index.php?lvl=notice_display&id=97774

[article]

Vectorized indoor surface reconstruction from 3D point cloud with multistep 2D optimization / Jiali Han in ISPRS Journal of photogrammetry and remote sensing, vol 177 (July 2021)

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[article]
inISPRS Journal of photogrammetry and remote sensing > vol 177 (July 2021) . - pp 57 - 74

Titre :	Vectorized indoor surface reconstruction from 3D point cloud with multistep 2D optimization
Type de document :	Article/Communication
Auteurs :	Jiali Han, Auteur ; Mengqi Rong, Auteur ; Hanqing Jiang, Auteur ; et al., Auteur
Année de publication :	2021
Article en page(s) :	pp 57 - 74
Note générale :	Bibliographie
Langues :	Anglais (eng)
Descripteur :	[Vedettes matières IGN] Lasergrammétrie [Termes IGN] champ aléatoire de Markov [Termes IGN] données lidar [Termes IGN] espace intérieur [Termes IGN] maillage [Termes IGN] programmation linéaire [Termes IGN] Ransac (algorithme) [Termes IGN] reconstruction 3D [Termes IGN] reconstruction d'objet [Termes IGN] segmentation sémantique [Termes IGN] semis de points [Termes IGN] vectorisation
Résumé :	(Auteur) Vectorized reconstruction from indoor point cloud has attracted increasing attention in recent years due to its high regularity and low memory consumption. Compared with aerial mapping of outdoor urban environments, indoor point cloud generated by LiDAR scanning or image-based 3D reconstruction usually contain more clutter and missing areas, which greatly increase the difficulty of vectorized reconstruction. In this paper, we propose an effective multistep pipeline to reconstruct vectorized models from indoor point cloud without the Manhattan or Atlanta world assumptions. The core idea behind our method is the combination of a sequence of 2D segment or cell assembly problems that are defined as global optimizations while reducing the reconstruction complexity and enhancing the robustness to different scenes. The proposed method includes a semantic segmentation stage and a reconstruction stage. First, we segment the permanent structures of indoor scenes, including ceilings, floors, walls and cylinders, from the input data, and then, we reconstruct these structures in sequence. The floorplan is first generated by detecting wall planes and selecting optimal subsets of projected wall segments with Integer Linear Programming (ILP), followed by constructing a 2D arrangement and recovering the ceiling and floor structures by Markov Random Field (MRF) labeling on the arrangement. Finally, the wall structures are modeled by lifting each edge of the arrangement to a proper height by means of another global optimization. Merging the respective results yields the final model. The experimental results show that the proposed method could obtain accurate and compact vectorized models on both precise LiDAR data and defect-laden MVS data compared with other state-of-the-art approaches.
Numéro de notice :	A2021-371
Affiliation des auteurs :	non IGN
Thématique :	IMAGERIE
Nature :	Article
nature-HAL :	ArtAvecCL-RevueIntern
DOI :	10.1016/j.isprsjprs.2021.04.019
date de publication en ligne :	15/05/2021
En ligne :	https://doi.org/10.1016/j.isprsjprs.2021.04.019
Format de la ressource électronique :	URL Article
Permalink :	https://documentation.ensg.eu/index.php?lvl=notice_display&id=97779

[article]

Altimétrie laser et surveillance / Laurent Polidori in Géomètre, n° 2192 (juin 2021)

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[article]
inGéomètre > n° 2192 (juin 2021) . - pp 18-18

Titre :	Altimétrie laser et surveillance
Type de document :	Article/Communication
Auteurs :	Laurent Polidori, Auteur
Année de publication :	2021
Article en page(s) :	pp 18-18
Langues :	Français (fre)
Descripteur :	[Vedettes matières IGN] Lasergrammétrie [Termes IGN] altimétrie satellitaire par laser [Termes IGN] biomasse forestière [Termes IGN] calotte glaciaire [Termes IGN] changement climatique [Termes IGN] données IceSat-Glas [Termes IGN] écosystème forestier [Termes IGN] fonte des glaces [Termes IGN] précision altimétrique [Termes IGN] surveillance forestière
Résumé :	(Auteur) Un laser en orbite observe les forêts tropicales et les calottes polaires pour mesurer le réchauffement climatique.
Numéro de notice :	A2021-376
Affiliation des auteurs :	non IGN
Thématique :	FORET/IMAGERIE
Nature :	Article
nature-HAL :	ArtSansCL
DOI :	sans
date de publication en ligne :	08/06/2021
Permalink :	https://documentation.ensg.eu/index.php?lvl=notice_display&id=97862

[article]

An incremental isomap method for hyperspectral dimensionality reduction and classification / Yi Ma in Photogrammetric Engineering & Remote Sensing, PERS, vol 87 n° 6 (June 2021)

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[article]
inPhotogrammetric Engineering & Remote Sensing, PERS > vol 87 n° 6 (June 2021) . - pp 445 - 455

Titre :	An incremental isomap method for hyperspectral dimensionality reduction and classification
Type de document :	Article/Communication
Auteurs :	Yi Ma, Auteur ; Zezhong Zheng, Auteur ; Yutang Ma, Auteur ; et al., Auteur
Année de publication :	2021
Article en page(s) :	pp 445 - 455
Note générale :	Bibliographie
Langues :	Anglais (eng)
Descripteur :	[Vedettes matières IGN] Traitement d'image optique [Termes IGN] algorithme d'apprentissage [Termes IGN] classification barycentrique [Termes IGN] classification par séparateurs à vaste marge [Termes IGN] échantillonnage de données [Termes IGN] image AVIRIS [Termes IGN] image hyperspectrale [Termes IGN] squelettisation [Termes IGN] utilisation du sol
Résumé :	(Auteur) Many manifold learning algorithms conduct an eigen vector analysis on a data-similarity matrix with a size of N×N, where N is the number of data points. Thus, the memory complexity of the analysis is no less than O(N2). We present in this article an incremental manifold learning approach to handle large hyperspectral data sets for land use identification. In our method, the number of dimensions for the high-dimensional hyperspectral-image data set is obtained with the training data set. A local curvature variation algorithm is utilized to sample a subset of data points as landmarks. Then a manifold skeleton is identified based on the landmarks. Our method is validated on three AVIRIS hyperspectral data sets, outperforming the comparison algorithms with a k–nearest-neighbor classifier and achieving the second best performance with support vector machine.
Numéro de notice :	A2021-375
Affiliation des auteurs :	non IGN
Thématique :	IMAGERIE
Nature :	Article
nature-HAL :	ArtAvecCL-RevueIntern
DOI :	10.14358/PERS.87.7.445
date de publication en ligne :	01/06/2021
En ligne :	https://doi.org/10.14358/PERS.87.7.445
Format de la ressource électronique :	URL article
Permalink :	https://documentation.ensg.eu/index.php?lvl=notice_display&id=97829

[article]

Application of feature selection methods and machine learning algorithms for saltmarsh biomass estimation using Worldview-2 imagery / Sikdar M. M. Rasel in Geocarto international, vol 36 n° 10 ([01/06/2021])

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[article]
inGeocarto international > vol 36 n° 10 [01/06/2021] . - pp 1075-1099

Titre :	Application of feature selection methods and machine learning algorithms for saltmarsh biomass estimation using Worldview-2 imagery
Type de document :	Article/Communication
Auteurs :	Sikdar M. M. Rasel, Auteur ; Hsing-Chung Chang, Auteur ; Timothy J. Ralph, Auteur ; et al., Auteur
Année de publication :	2021
Article en page(s) :	pp 1075-1099
Note générale :	Bibliographie
Langues :	Anglais (eng)
Descripteur :	[Vedettes matières IGN] Traitement d'image optique [Termes IGN] apprentissage automatique [Termes IGN] bande spectrale [Termes IGN] biomasse [Termes IGN] classification par forêts aléatoires [Termes IGN] classification par séparateurs à vaste marge [Termes IGN] image multibande [Termes IGN] image Worldview [Termes IGN] marais salé [Termes IGN] modèle de simulation [Termes IGN] Normalized Difference Vegetation Index [Termes IGN] régression [Termes IGN] variable
Résumé :	(Auteur) Assessing large scale plant productivity of coastal marshes is essential to understand the resilience of these systems to climate change. Two machine learning approaches, random forest (RF) and support vector machine (SVM) regression were tested to estimate biomass of a common saltmarshes species, salt couch grass (Sporobolus virginicus). Reflectance and vegetation indices derived from 8 bands of Worldview-2 multispectral data were used for four experiments to develop the biomass model. These four experiments were, Experiment-1: 8 bands of Worldview-2 image, Experiment-2: Possible combination of all bands of Worldview-2 for Normalized Difference Vegetation Index (NDVI) type vegetation indices, Experiment-3: Combination of bands and vegetation indices, Experiment-4: Selected variables derived from experiment-3 using variable selection methods. The main objectives of this study are (i) to recommend an affordable low cost data source to predict biomass of a common saltmarshes species, (ii) to suggest a variable selection method suitable for multispectral data, (iii) to assess the performance of RF and SVM for the biomass prediction model. Cross-validation of parameter optimizations for SVM showed that optimized parameter of ɛ-SVR failed to provide a reliable prediction. Hence, ν-SVR was used for the SVM model. Among the different variable selection methods, recursive feature elimination (RFE) selected a minimum number of variables (only 4) with an RMSE of 0.211 (kg/m2). Experiment-4 (only selected bands) provided the best results for both of the machine learning regression methods, RF (R2= 0.72, RMSE= 0.166 kg/m2) and SVR (R2= 0.66, RMSE = 0.200 kg/m2) to predict biomass. When a 10-fold cross validation of the RF model was compared with a 10-fold cross validation of SVR, a significant difference (p =
Numéro de notice :	A2021-367
Affiliation des auteurs :	non IGN
Thématique :	FORET/IMAGERIE/INFORMATIQUE
Nature :	Article
nature-HAL :	ArtAvecCL-RevueIntern
DOI :	10.1080/10106049.2019.1624988
date de publication en ligne :	11/06/2019
En ligne :	https://doi.org/10.1080/10106049.2019.1624988
Format de la ressource électronique :	URL Article
Permalink :	https://documentation.ensg.eu/index.php?lvl=notice_display&id=97729

[article]

A combined drought monitoring index based on multi-sensor remote sensing data and machine learning / Hongzhu Han in Geocarto international, vol 36 n° 10 ([01/06/2021])

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[article]
inGeocarto international > vol 36 n° 10 [01/06/2021] . - pp 1161-1177

Titre :	A combined drought monitoring index based on multi-sensor remote sensing data and machine learning
Type de document :	Article/Communication
Auteurs :	Hongzhu Han, Auteur ; Jianjun Bai, Auteur ; Jianwu Yan, Auteur ; et al., Auteur
Année de publication :	2021
Article en page(s) :	pp 1161-1177
Note générale :	Bibliographie
Langues :	Anglais (eng)
Descripteur :	[Vedettes matières IGN] Traitement d'image optique [Termes IGN] apprentissage automatique [Termes IGN] Chensi (Chine) [Termes IGN] classification par forêts aléatoires [Termes IGN] évapotranspiration [Termes IGN] humidité du sol [Termes IGN] image Terra-MODIS [Termes IGN] image TRMM-MI [Termes IGN] indice d'humidité [Termes IGN] indice de végétation [Termes IGN] précipitation [Termes IGN] sécheresse [Termes IGN] surveillance météorologique [Termes IGN] température au sol
Résumé :	(Auteur) The occurrence of drought is related to complicated interactions between many factors, such as precipitation, temperature, evapotranspiration and vegetation. In this study, the relationships between drought and precipitation, temperature, vegetation and evapotranspiration were investigated with a random forest (RF), and a new combined drought monitoring index (CDMI) was constructed. The effectiveness of the CDMI in monitoring drought in Shaanxi Province was verified by the in situ 1 ∼ 12-month standardized precipitation index (SPI); relative soil moisture (RSM) and four other commonly used remote sensing drought monitoring indices. The results show that CDMI is more correlated with the SPI and RSM than the four indices. Moreover, the spatial distributions of drought for the CDMI and RSM are similar. Therefore, the CDMI can be used to monitor droughts in Shaanxi Province, and machine learning can explore the relationships between various factors and establish a drought index without knowledge of the causal mechanisms of these factors.
Numéro de notice :	A2021-369
Affiliation des auteurs :	non IGN
Thématique :	FORET/IMAGERIE/INFORMATIQUE
Nature :	Article
nature-HAL :	ArtAvecCL-RevueIntern
DOI :	10.1080/10106049.2019.1633423
date de publication en ligne :	27/06/2019
En ligne :	https://doi.org/10.1080/10106049.2019.1633423
Format de la ressource électronique :	URL Article
Permalink :	https://documentation.ensg.eu/index.php?lvl=notice_display&id=97734

[article]

Domain adaptive transfer attack-based segmentation networks for building extraction from aerial images / Younghwan Na in IEEE Transactions on geoscience and remote sensing, vol 59 n° 6 (June 2021)

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[article]
inIEEE Transactions on geoscience and remote sensing > vol 59 n° 6 (June 2021) . - pp 5171 - 5182

Titre :	Domain adaptive transfer attack-based segmentation networks for building extraction from aerial images
Type de document :	Article/Communication
Auteurs :	Younghwan Na, Auteur ; Jun Hee Kim, Auteur ; Kyungsu Lee, Auteur ; et al., Auteur
Année de publication :	2021
Article en page(s) :	pp 5171 - 5182
Note générale :	bibliographie
Langues :	Anglais (eng)
Descripteur :	[Vedettes matières IGN] Traitement d'image optique [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] détection du bâti [Termes IGN] entropie [Termes IGN] image aérienne [Termes IGN] segmentation sémantique
Résumé :	(auteur) Semantic segmentation models based on convolutional neural networks (CNNs) have gained much attention in relation to remote sensing and have achieved remarkable performance for the extraction of buildings from high-resolution aerial images. However, the issue of limited generalization for unseen images remains. When there is a domain gap between the training and test data sets, the CNN-based segmentation models trained by a training data set fail to segment buildings for the test data set. In this article, we propose segmentation networks based on a domain adaptive transfer attack (DATA) scheme for building extraction from aerial images. The proposed system combines the domain transfer and the adversarial attack concepts. Based on the DATA scheme, the distribution of the input images can be shifted to that of the target images while turning images into adversarial examples against a target network. Defending adversarial examples adapted to the target domain can overcome the performance degradation due to the domain gap and increase the robustness of the segmentation model. Cross-data set experiments and ablation study are conducted for three different data sets: the Inria aerial image labeling data set, the Massachusetts building data set, and the WHU East Asia data set. Compared with the performance of the segmentation network without the DATA scheme, the proposed method shows improvements in the overall intersection over union (IoU). Moreover, it is verified that the proposed method outperforms even when compared with feature adaptation (FA) and output space adaptation (OSA).
Numéro de notice :	A2021-427
Affiliation des auteurs :	non IGN
Thématique :	IMAGERIE
Nature :	Article
nature-HAL :	ArtAvecCL-RevueIntern
DOI :	10.1109/TGRS.2020.3010055
date de publication en ligne :	30/07/2020
En ligne :	https://doi.org/10.1109/TGRS.2020.3010055
Format de la ressource électronique :	URL article
Permalink :	https://documentation.ensg.eu/index.php?lvl=notice_display&id=97783

[article]

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2014-2021 IGN

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Centre dedocumentationscientifique

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Se connecter

Actualité

L'actu-47

Agenda

Informations pratiques

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L'IGN a pour vocation

Accès directs

2014-2021 IGN

Centre dedocumentation
scientifique