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Change alignment-based image transformation for unsupervised heterogeneous change detection / Kuowei Xiao in Remote sensing, vol 14 n° 21 (November-1 2022)  

Public [article]  inRemote sensing > vol 14 n° 21 (November-1 2022) . - n° 5622 Titre : Change alignment-based image transformation for unsupervised heterogeneous change detection Type de document : Article/Communication Auteurs : Kuowei Xiao, Auteur ; Yuli Sun, Auteur ; Lin Lei, Auteur Année de publication : 2022 Article en page(s) : n° 5622 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] alignement [Termes IGN] classification non dirigée [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] décomposition d'image [Termes IGN] détection de changement [Termes IGN] données hétérogènes [Termes IGN] masque Résumé : (auteur) Change detection (CD) with heterogeneous images is currently attracting extensive attention in remote sensing. In order to make heterogeneous images comparable, the image transformation methods transform one image into the domain of another image, which can simultaneously obtain a forward difference map (FDM) and backward difference map (BDM). However, previous methods only fuse the FDM and BDM in the post-processing stage, which cannot fundamentally improve the performance of CD. In this paper, a change alignment-based change detection (CACD) framework for unsupervised heterogeneous CD is proposed to deeply utilize the complementary information of the FDM and BDM in the image transformation process, which enhances the effect of domain transformation, thus improving CD performance. To reduce the dependence of the transformation network on labeled samples, we propose a graph structure-based strategy of generating prior masks to guide the network, which can reduce the influence of changing regions on the transformation network in an unsupervised way. More importantly, based on the fact that the FDM and BDM are representing the same change event, we perform change alignment during the image transformation, which can enhance the image transformation effect and enable FDM and BDM to effectively indicate the real change region. Comparative experiments are conducted with six state-of-the-art methods on five heterogeneous CD datasets, showing that the proposed CACD achieves the best performance with an average overall accuracy (OA) of 95.9% on different datasets and at least 6.8% improvement in the kappa coefficient. Numéro de notice : A2022-855 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.3390/rs14215622 Date de publication en ligne : 07/11/2022 En ligne : https://doi.org/10.3390/rs14215622 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102103 [article]

Deep learning-based image de-raining using discrete Fourier transformation / Prasen Kumar Sharma in The Visual Computer, vol 37 n° 8 (August 2021)  

Public [article]  inThe Visual Computer > vol 37 n° 8 (August 2021) . - pp 2083 - 2096 Titre : Deep learning-based image de-raining using discrete Fourier transformation Type de document : Article/Communication Auteurs : Prasen Kumar Sharma, Auteur ; Sathisha Basavaraju, Auteur ; Arijit Sur, Auteur Année de publication : 2021 Article en page(s) : pp 2083 - 2096 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] apprentissage profond [Termes IGN] bruit (théorie du signal) [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] décomposition d'image [Termes IGN] filtrage du bruit [Termes IGN] pluie [Termes IGN] transformation de Fourier Résumé : (auteur) Single image rain streak removal is a well-explored topic in the field of computer vision. The de-raining problem is modeled as an image decomposition task where a rainy image is decomposed into rain-free background image and rain streek map. Unlike most of the existing de-raining methods, this paper attempts to decompose the rainy image in the frequency domain. The idea is inspired by pseudo-periodic characteristics of the noise signal (here the rain streaks) which leave some traces in the frequency domain, and the same can be utilized to predict the noise signal. In this paper, a deep learning-based rain streak prediction model is proposed which learns in discrete Fourier transform Oppenheim and Schafer (Discrete-Time Signal Processing, Prentice Hall, Upper Saddle River, 1989) domain. To the best of our knowledge, this is the first approach where compressed domain coefficients are directly used as input to a deep convolutional neural network. The proposed model has been tested on publicly available synthetic datasets Fu et al. (in: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017. https://doi.org/10.1109/CVPR.2017.186, Yang et al. (in: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017. https://doi.org/10.1109/CVPR.2017.183), Yeh et al. (in: 2015 IEEE International Conference on Consumer Electronics-Taiwan, 2015. https://doi.org/10.1109/ICCE-TW.2015.7216999) and results are found to be comparable with the state of the art methods in the spatial domain. The presented analysis and study have an obvious indication to extend transform domain input to train the deep learning architecture especially image de-noising like problems. Numéro de notice : A2021-597 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1007/s00371-020-01971-w Date de publication en ligne : 16/09/2020 En ligne : https://doi.org/10.1007/s00371-020-01971-w Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98226 [article]

Using a fully polarimetric SAR to detect landslide in complex surroundings: Case study of 2015 Shenzhen landslide / Chaoyang Niu in ISPRS Journal of photogrammetry and remote sensing, vol 174 (April 2021)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 174 (April 2021) . - pp 56 - 67 Titre : Using a fully polarimetric SAR to detect landslide in complex surroundings: Case study of 2015 Shenzhen landslide Type de document : Article/Communication Auteurs : Chaoyang Niu, Auteur ; Haobo Zhang, Auteur ; Wei Liu, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 56 - 67 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications [Termes IGN] décomposition d'image [Termes IGN] détection de changement [Termes IGN] effondrement de terrain [Termes IGN] image radar moirée [Termes IGN] mouvement de terrain [Termes IGN] polarimétrie radar [Termes IGN] processus de hiérarchisation analytique [Termes IGN] Shenzhen Résumé : (auteur) Synthetic aperture radar (SAR) polarimetry has demonstrated high efficiency in the detection of landslides in vegetated mountainous areas. In such places, post-landslide soil layers appear to correspond to the typical surface scattering mechanism, which is significantly different from the volume scattering behaviour of the surrounding vegetation. However, a landslide in the complex surroundings of various landforms, involving naked hillslopes, construction fields, bare farmlands, and other such aspects, may not be accurately identified owing to the occurrence of surface scattering behaviours. In order to detect landslides using SAR polarimetry without the limitation of vegetated mountainous areas, we propose a novel method of combining change detection (CD) and an analytic hierarchy process (AHP) based on the Yamaguchi decomposition (YD) to identify landslides while ensuring fewer false alarms. In particular, CD is applied to a pair of pre- and post-event datasets to determine the regions modified by landslides or human activities, and the AHP is performed over the post-event dataset to identify the suspect landslide region characterised by the surface scattering mechanism. Finally, the two results are fused by a logical operation to identify the actual landslide by removing the non-modified surface scattering regions. A case study of the Shenzhen landslide in complex surroundings was considered to verify the performance of the proposed method (CD-AHP). The results indicate that the method could clearly define the main body of the Shenzhen landslide from the city suburbs with a small number of false alarms. Therefore, this method provides a considerable perspective for landslide detection in complex surroundings using SAR polarimetry. Numéro de notice : A2021-207 Affiliation des auteurs : non IGN Thématique : IMAGERIE/POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2021.01.022 Date de publication en ligne : 19/02/2021 En ligne : https://doi.org/10.1016/j.isprsjprs.2021.01.022 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97184 [article]

Exemplaires(3)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
081-2021041	SL	Revue	Centre de documentation	Revues en salle	Disponible
081-2021043	DEP-RECP	Revue	LASTIG	Dépôt en unité	Exclu du prêt
081-2021042	DEP-RECF	Revue	Nancy	Dépôt en unité	Exclu du prêt

A fractal projection and Markovian segmentation-based approach for multimodal change detection / Max Mignotte in IEEE Transactions on geoscience and remote sensing, vol 58 n° 11 (November 2020)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 58 n° 11 (November 2020) . - pp 8046 - 8058 Titre : A fractal projection and Markovian segmentation-based approach for multimodal change detection Type de document : Article/Communication Auteurs : Max Mignotte, Auteur Année de publication : 2020 Article en page(s) : pp 8046 - 8058 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] champ aléatoire de Markov [Termes IGN] classification non dirigée [Termes IGN] décomposition d'image [Termes IGN] détection de changement [Termes IGN] estimation bayesienne [Termes IGN] géométrie fractale [Termes IGN] image satellite [Termes IGN] projection [Termes IGN] segmentation d'image Résumé : (auteur) Change detection in heterogeneous bitemporal satellite images has become an emerging, important, and challenging research topic in remote sensing for rapid damage assessment. In this article, we explore a new parametric mapping strategy based on a modified geometric fractal decomposition and a contractive mapping approach allowing us to project the before image on any after imaging modality type. This projection exploits the fact that any satellite image data can be approximatively encoded in terms of spatial self-similarities at different scales and this property remains quite invariant to a given imaging modality type. Once the projection is performed and that a pixelwise difference map between the two images (presented in the same imaging modality) is then binarized in the unsupervised Bayesian framework. At this stage, we will test several parameter estimation procedures combined with several segmentation strategies based on different Bayesian cost functions. The experiments for change detection, with real images showing different multimodalities and changed events, indicate that this new fractal-based projection method, which is entirely based on a series of structural and spatial information, is an interesting alternative to classical regression-based projection methods (based only on luminance transformation). Besides, the experiments also show that the difference map, resulting in this novel projection strategy, is also particularly amenable for an unsupervised Markovian binarization approach. Numéro de notice : A2020-682 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2020.2986239 Date de publication en ligne : 30/04/2020 En ligne : https://doi.org/10.1109/TGRS.2020.2986239 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96207 [article]

Indoor positioning using PnP problem on mobile phone images / Hana Kubickova in ISPRS International journal of geo-information, vol 9 n° 6 (June 2020)  

Public [article]  inISPRS International journal of geo-information > vol 9 n° 6 (June 2020) . - 19 p. Titre : Indoor positioning using PnP problem on mobile phone images Type de document : Article/Communication Auteurs : Hana Kubickova, Auteur ; Karel Jedlička, Auteur ; Radek Fiala, Auteur ; Daniel Beran, Auteur Année de publication : 2020 Article en page(s) : 19 p. Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] appariement d'images [Termes IGN] base de données d'images [Termes IGN] couplage GNSS-INS [Termes IGN] décomposition d'image [Termes IGN] extraction de traits caractéristiques [Termes IGN] géométrie épipolaire [Termes IGN] point d'appui [Termes IGN] positionnement en intérieur [Termes IGN] recherche d'image basée sur le contenu [Termes IGN] SIFT (algorithme) [Termes IGN] téléphone intelligent [Termes IGN] vision par ordinateur Résumé : (auteur) As people grow accustomed to effortless outdoor navigation, there is a rising demand for similar possibilities indoors as well. Unfortunately, indoor localization, being one of the requirements for navigation, continues to be a problem without a clear solution. In this article, we are proposing a method for an indoor positioning system using a single image. This is made possible using a small preprocessed database of images with known control points as the only preprocessing needed. Using feature detection with the SIFT (Scale Invariant Feature Transform) algorithm, we can look through the database and find an image that is the most similar to the image taken by a user. Such a pair of images is then used to find coordinates of a database of images using the PnP problem. Furthermore, projection and essential matrices are determined to calculate the user image localization—determining the position of the user in the indoor environment. The benefits of this approach lie in the single image being the only input from a user and the lack of requirements for new onsite infrastructure. Thus, our approach enables a more straightforward realization for building management. Numéro de notice : A2020-309 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi9060368 Date de publication en ligne : 02/06/2020 En ligne : https://doi.org/10.3390/ijgi9060368 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95156 [article]

Contextual filtering methods based on the subbands and subspaces decomposition of complex SAR interferograms / Saoussen Belhadj-Aissa in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol 12 n° 12 (December 2019)  

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Critical analysis of model-based incoherent polarimetric decomposition methods and investigation of deorientation effect / Pooja Mishra in IEEE Transactions on geoscience and remote sensing, vol 55 n° 9 (September 2017)  

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Morphologically decoupled structured sparsity for rotation-invariant hyperspectral image analysis / Saurabh Prasad in IEEE Transactions on geoscience and remote sensing, vol 55 n° 8 (August 2017)  

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Parallax-tolerant aerial image georegistration and efficient camera pose refinement—without piecewise homographies / Hadi AliAkbarpour in IEEE Transactions on geoscience and remote sensing, vol 55 n° 8 (August 2017)  

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Superpixel-based intrinsic image decomposition of hyperspectral images / Xudong Jin in IEEE Transactions on geoscience and remote sensing, vol 55 n° 8 (August 2017)  

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Refining geometry from depth sensors using IR shading images / Gyeongmin Choe in International journal of computer vision, vol 122 n° 1 (March 2017)  

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Fast three-dimensional empirical mode decomposition of hyperspectral images for class-oriented multitask learning / Zhi He in IEEE Transactions on geoscience and remote sensing, vol 54 n° 11 (November 2016)  

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Robust multitask learning with three-dimensional empirical mode decomposition-based features for hyperspectral classification / Zhi He in ISPRS Journal of photogrammetry and remote sensing, vol 121 (November 2016)  

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Object-based morphological profiles for classification of remote sensing imagery / Christian Geiss in IEEE Transactions on geoscience and remote sensing, vol 54 n° 10 (October 2016)  

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Regression wavelet analysis for lossless coding of remote-sensing data / Naoufal Amrani in IEEE Transactions on geoscience and remote sensing, vol 54 n° 9 (September 2016)  

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