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A comparative study on deep-learning methods for dense image matching of multi-angle and multi-date remote sensing stereo-images / Hessah Albanwan in Photogrammetric record, vol 37 n° 180 (December 2022)  

Public [article]  inPhotogrammetric record > vol 37 n° 180 (December 2022) . - pp 385 - 409 Titre : A comparative study on deep-learning methods for dense image matching of multi-angle and multi-date remote sensing stereo-images Type de document : Article/Communication Auteurs : Hessah Albanwan, Auteur ; Rongjun Qin, Auteur Année de publication : 2022 Article en page(s) : pp 385 - 409 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse comparative [Termes IGN] appariement d'images [Termes IGN] apprentissage profond [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] couple stéréoscopique [Termes IGN] modèle stéréoscopique [Termes IGN] précision géométrique (imagerie) Résumé : (auteur) Deep-learning (DL) stereomatching methods gained great attention in remote sensing satellite datasets. However, most of these existing studies conclude assessments based only on a few/single stereo-images lacking a systematic evaluation on how robust DL methods are on satellite stereo-images with varying radiometric and geometric configurations. This paper provides an evaluation of four DL stereomatching methods through hundreds of multi-date multi-site satellite stereopairs with varying geometric configurations, against the traditional well-practiced Census-semi-global matching (SGM), to comprehensively understand their accuracy, robustness, generalisation capabilities, and their practical potential. The DL methods include a learning-based cost metric through convolutional neural networks (MC-CNN) followed by SGM, and three end-to-end (E2E) learning models using Geometry and Context Network (GCNet), Pyramid Stereo Matching Network (PSMNet), and LEAStereo. Our experiments show that E2E algorithms can achieve upper limits of geometric accuracies, while may not generalise well for unseen data. The learning-based cost metric and Census-SGM are rather robust and can consistently achieve acceptable results. All DL algorithms are robust to geometric configurations of stereopairs and are less sensitive in comparison to the Census-SGM, while learning-based cost metrics can generalise on satellite images when trained on different datasets (airborne or ground-view). Numéro de notice : A2022-938 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1111/phor.12430 Date de publication en ligne : 09/11/2022 En ligne : https://doi.org/10.1111/phor.12430 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102684 [article]

Detection of potential gold mineralization areas using MF-fuzzy approach on multispectral data / Tohid Nouri in Geocarto international, Vol 37 n° 17 ([20/08/2022])  

Public [article]  inGeocarto international > Vol 37 n° 17 [20/08/2022] . - pp 5017 - 5040 Titre : Detection of potential gold mineralization areas using MF-fuzzy approach on multispectral data Type de document : Article/Communication Auteurs : Tohid Nouri, Auteur Année de publication : 2022 Article en page(s) : pp 5017 - 5040 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes IGN] altération géologique [Termes IGN] analyse des mélanges spectraux [Termes IGN] appariement d'images [Termes IGN] diffraction [Termes IGN] image multibande [Termes IGN] Iran [Termes IGN] logique floue [Termes IGN] mine d'or [Termes IGN] MNS ASTER [Termes IGN] pixel [Termes IGN] prospection minérale [Termes IGN] sédiment [Termes IGN] spectrométrie Résumé : (auteur) The northeast area of Ardabil, a city located in northwestern Iran, is one of the potential gold mineralization areas. In this study, ASTER data were used to identify the alteration events in this region. For this purpose, a novel approach was used in which the fuzzy logic was implemented to extract the co-occurrence map of the endmembers. This method revealed alterations more accurately than SID. Stream sediment samples were employed to validate the obtained results. Since these samples are alluvial, their catchment basins were determined and overlaid with the alteration maps. To the best of the authors’ knowledge, this validation approach has not been used in previous studies. The extracted alteration zones were in high conformity to the stream sediment samples. Next, X-ray diffraction (XRD) analysis and field spectrometry were used for delineation of the mineralogical phases present in the anomalous areas. Finally, the potential gold mineralization zones were identified. Numéro de notice : A2022-701 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2021.1903575 Date de publication en ligne : 07/06/2021 En ligne : https://doi.org/10.1080/10106049.2021.1903575 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101560 [article]

Deep learning feature representation for image matching under large viewpoint and viewing direction change / Lin Chen in ISPRS Journal of photogrammetry and remote sensing, vol 190 (August 2022)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 190 (August 2022) . - pp 94 -112 Titre : Deep learning feature representation for image matching under large viewpoint and viewing direction change Type de document : Article/Communication Auteurs : Lin Chen, Auteur ; Christian Heipke, Auteur Année de publication : 2022 Article en page(s) : pp 94 -112 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] appariement d'images [Termes IGN] apprentissage profond [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] extraction de traits caractéristiques [Termes IGN] image aérienne oblique [Termes IGN] orientation d'image [Termes IGN] reconnaissance de formes [Termes IGN] réseau neuronal siamois [Termes IGN] SIFT (algorithme) Résumé : (auteur) Feature based image matching has been a research focus in photogrammetry and computer vision for decades, as it is the basis for many applications where multi-view geometry is needed. A typical feature based image matching algorithm contains five steps: feature detection, affine shape estimation, orientation assignment, description and descriptor matching. This paper contains innovative work in different steps of feature matching based on convolutional neural networks (CNN). For the affine shape estimation and orientation assignment, the main contribution of this paper is twofold. First, we define a canonical shape and orientation for each feature. As a consequence, instead of the usual Siamese CNN, only single branch CNNs needs to be employed to learn the affine shape and orientation parameters, which turns the related tasks from supervised to self supervised learning problems, removing the need for known matching relationships between features. Second, the affine shape and orientation are solved simultaneously. To the best of our knowledge, this is the first time these two modules are reported to have been successfully trained together. In addition, for the descriptor learning part, a new weak match finder is suggested to better explore the intra-variance of the appearance of matched features. For any input feature patch, a transformed patch that lies far from the input feature patch in descriptor space is defined as a weak match feature. A weak match finder network is proposed to actively find these weak match features; they are subsequently used in the standard descriptor learning framework. The proposed modules are integrated into an inference pipeline to form the proposed feature matching algorithm. The algorithm is evaluated on standard benchmarks and is used to solve for the parameters of image orientation of aerial oblique images. It is shown that deep learning feature based image matching leads to more registered images, more reconstructed 3D points and a more stable block geometry than conventional methods. The code is available at https://github.com/Childhoo/Chen_Matcher.git. Numéro de notice : A2022-502 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2022.06.003 Date de publication en ligne : 14/06/2022 En ligne : https://doi.org/10.1016/j.isprsjprs.2022.06.003 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101000 [article]

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A dual-generator translation network fusing texture and structure features for SAR and optical image matching / Han Nie in Remote sensing, Vol 14 n° 12 (June-2 2022)  

Public [article]  inRemote sensing > Vol 14 n° 12 (June-2 2022) . - n° 2946 Titre : A dual-generator translation network fusing texture and structure features for SAR and optical image matching Type de document : Article/Communication Auteurs : Han Nie, Auteur ; Zhitao Fu, Auteur ; Bo-Hui Tang, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 2946 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image mixte [Termes IGN] agrégation de détails [Termes IGN] appariement d'images [Termes IGN] fusion d'images [Termes IGN] image radar moirée [Termes IGN] image Sentinel-MSI [Termes IGN] image Sentinel-SAR [Termes IGN] rapport signal sur bruit [Termes IGN] rift [Termes IGN] texture d'image Résumé : (auteur) The matching problem for heterologous remote sensing images can be simplified to the matching problem for pseudo homologous remote sensing images via image translation to improve the matching performance. Among such applications, the translation of synthetic aperture radar (SAR) and optical images is the current focus of research. However, the existing methods for SAR-to-optical translation have two main drawbacks. First, single generators usually sacrifice either structure or texture features to balance the model performance and complexity, which often results in textural or structural distortion; second, due to large nonlinear radiation distortions (NRDs) in SAR images, there are still visual differences between the pseudo-optical images generated by current generative adversarial networks (GANs) and real optical images. Therefore, we propose a dual-generator translation network for fusing structure and texture features. On the one hand, the proposed network has dual generators, a texture generator, and a structure generator, with good cross-coupling to obtain high-accuracy structure and texture features; on the other hand, frequency-domain and spatial-domain loss functions are introduced to reduce the differences between pseudo-optical images and real optical images. Extensive quantitative and qualitative experiments show that our method achieves state-of-the-art performance on publicly available optical and SAR datasets. Our method improves the peak signal-to-noise ratio (PSNR) by 21.0%, the chromatic feature similarity (FSIMc) by 6.9%, and the structural similarity (SSIM) by 161.7% in terms of the average metric values on all test images compared with the next best results. In addition, we present a before-and-after translation comparison experiment to show that our method improves the average keypoint repeatability by approximately 111.7% and the matching accuracy by approximately 5.25%. Numéro de notice : A2022-562 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.3390/rs14122946 Date de publication en ligne : 20/06/2022 En ligne : https://doi.org/10.3390/rs14122946 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101237 [article]

The interrelationship between LST, NDVI, NDBI, and land cover change in a section of Lagos metropolis, Nigeria / Alfred S. Alademomi in Applied geomatics, vol 14 n° 2 (June 2022)  

Public [article]  inApplied geomatics > vol 14 n° 2 (June 2022) . - pp 299 - 314 Titre : The interrelationship between LST, NDVI, NDBI, and land cover change in a section of Lagos metropolis, Nigeria Type de document : Article/Communication Auteurs : Alfred S. Alademomi, Auteur ; Chukwuma J. Okolie, Auteur ; Olagoke E. Daramola, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 299 - 314 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] corrélation temporelle [Termes IGN] détection de changement [Termes IGN] image Landsat-8 [Termes IGN] image Landsat-ETM+ [Termes IGN] image Landsat-TM [Termes IGN] Lagos [Termes IGN] Normalized Difference Built-up Index [Termes IGN] Normalized Difference Vegetation Index [Termes IGN] occupation du sol [Termes IGN] température au sol Résumé : (auteur) In recent times, there has been renewed interest in understanding the dynamics of land cover change and its relationship with several environmental parameters. This study assesses the interrelationship between land surface temperature (LST), normalized difference vegetation index (NDVI), normalized difference built-up index (NDBI), and land cover change in Amuwo-Odofin Local Government Area of Lagos State, Nigeria. Multi-temporal and multi-spectral Landsat imageries for years 2002, 2013, 2016, and 2019 served as the primary dataset. Using the parallelepiped classifier, the imageries were classified into five land cover classes — mixed vegetation, bare land, built-up area, water body, and wetland. The spectral indices (NDVI and NDBI) were computed and the LST was determined using a single-channel algorithm. Land cover transition matrices were calculated to examine the proportion of land cover change between classes, including the unchanged areas. Pearson’s correlation analysis enabled an analysis of the interdependence or interrelationship in the distribution of the parameters. From 2002 to 2019, the highest land cover transitions recorded were bare land to built-up area (12.64 km2), mixed vegetation to built-up area (21.55 km2), wetland to mixed vegetation (8.87 km2), and mixed vegetation to bare land (8.46 km2). There was a negative correlation between LST and NDVI, and between NDVI and NDBI. The distribution of the LST, NDVI, and NDBI varied correspondingly in accordance with land cover changes. The increase in built-up area could be the major driver of the observed changes in LST, NDBI, and NDVI, with an observed relationship that NDBI and LST values increase with increase in built-up areas. Numéro de notice : A2022-463 Affiliation des auteurs : non IGN Thématique : IMAGERIE/URBANISME Nature : Article DOI : 10.1007/s12518-022-00434-2 Date de publication en ligne : 06/04/2022 En ligne : https://doi.org/10.1007/s12518-022-00434-2 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100790 [article]

Hybrid georeferencing of images and LiDAR data for UAV-based point cloud collection at millimetre accuracy / Norbert Haala in ISPRS Open Journal of Photogrammetry and Remote Sensing, vol 4 (April 2022)  

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Uncertainty estimation for stereo matching based on evidential deep learning / Chen Wang in Pattern recognition, vol 124 (April 2022)  

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Exploiting light directionality for image-based 3D reconstruction of non-collaborative surfaces / Ali Karami in Photogrammetric record, vol 37 n° 177 (March 2022)  

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Simultaneous retrieval of selected optical water quality indicators from Landsat-8, Sentinel-2, and Sentinel-3 / Nima Pahlevan in Remote sensing of environment, vol 270 (March 2022)  

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Fast local adaptive multiscale image matching algorithm for remote sensing image correlation / Niccolò Dematteis in Computers & geosciences, vol 159 (February 2022)  

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Automatic algorithm for georeferencing historical-to-nowadays aerial images acquired in natural environments / Daniela Craciun (2022)  

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Deep learning based 3D reconstruction: supervision and representation / François Darmon (2022)  

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Feature matching for multi-epoch historical aerial images / Lulin Zhang (2022)  

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GNSS/INS Kalman filter integrity monitoring with uncertain time correlated error processes / Omar Garcia Crespillo (2022)  

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A multipath and thermal noise joint error characterization and exploitation for low-cost GNSS PVT estimators in urban environment / Eustachio Roberto Matera (2022)  

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