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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)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 55 n° 9 (September 2017) . - pp 4868 - 4877 Titre : Critical analysis of model-based incoherent polarimetric decomposition methods and investigation of deorientation effect Type de document : Article/Communication Auteurs : Pooja Mishra, Auteur ; Akanksha Garg, Auteur ; Dharmendra Singh, Auteur Année de publication : 2017 Article en page(s) : pp 4868 - 4877 Note générale : Bibliothèque Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications [Termes descripteurs IGN] décomposition d'image [Termes descripteurs IGN] diffusion du rayonnement [Termes descripteurs IGN] données polarimétriques [Termes descripteurs IGN] image ALOS [Termes descripteurs IGN] image ALOS-PALSAR [Termes descripteurs IGN] occupation du sol [Termes descripteurs IGN] polarimétrie radar [Termes descripteurs IGN] valeur propre [Termes descripteurs IGN] végétation [Termes descripteurs IGN] zone urbaine Résumé : (Auteur) This paper critically analyzes several incoherent model-based decomposition methods for assessing the effect of deorientation in characterization of various land covers. It has been found that even after performing decomposition, ambiguity still occurs in scattering response from various land covers, such as urban and vegetation. Researchers introduced the concept of deorientation to remove this ambiguity. Therefore, in this paper, a critical analysis has been carried out using seven different three- and four-component decomposition methods with and without deorientation and two Eigen decomposition-based methods to investigate the scattering response on various land covers, such as urban, vegetation, bare soil, and water. The comprehensive evaluation of decomposition and deorientation effect has been performed by both visual and quantitative analyses. Two types of quantitative analysis have been performed; first, by observing percentage of scattering power and second, by analyzing the variation in the number of pixels in different land covers for each scattering contribution. The analysis shows that deorientation increases not only the power but also the number of pixels for surface and double bounce scattering. The number of pixels representing volume scattering remain almost the same for all the methods with or without deorientation, whereas volume scattering power reduces after deorientation. Eigen decomposition-based methods are observed to solve the problem of overestimation of volume scattering power. Numéro de notice : A2017-657 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2017.2652060 En ligne : http://dx.doi.org/10.1109/TGRS.2017.2652060 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=87067 [article]

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)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 55 n° 8 (August 2017) . - pp 4618 - 4637 Titre : Parallax-tolerant aerial image georegistration and efficient camera pose refinement—without piecewise homographies Type de document : Article/Communication Auteurs : Hadi AliAkbarpour, Auteur ; Kannappan Palaniappan, Auteur ; Guna Seetharaman, Auteur ; et al., Auteur Année de publication : 2017 Article en page(s) : pp 4618 - 4637 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Acquisition d'image(s) et de donnée(s) [Termes descripteurs IGN] bruit (théorie du signal) [Termes descripteurs IGN] chambre de prise de vue numérique [Termes descripteurs IGN] compensation par faisceaux [Termes descripteurs IGN] décomposition d'image [Termes descripteurs IGN] géoréférencement [Termes descripteurs IGN] image aérienne [Termes descripteurs IGN] métadonnées [Termes descripteurs IGN] orthorectification [Termes descripteurs IGN] reconstruction 3D [Termes descripteurs IGN] structure-from-motion Résumé : (Auteur) We describe a fast and efficient camera pose refinement and Structure from Motion (SfM) method for sequential aerial imagery with applications to georegistration and 3-D reconstruction. Inputs to the system are 2-D images combined with initial noisy camera metadata measurements, available from on-board sensors (e.g., camera, global positioning system, and inertial measurement unit). Georegistration is required to stabilize the ground-plane motion to separate camera-induced motion from object motion to support vehicle tracking in aerial imagery. In the proposed approach, we recover accurate camera pose and (sparse) 3-D structure using bundle adjustment for sequential imagery (BA4S) and then stabilize the video from the moving platform by analytically solving for the image-plane-to-ground-plane homography transformation. Using this approach, we avoid relying upon image-to-image registration, which requires estimating feature correspondences (i.e., matching) followed by warping between images (in a 2-D space) that is an error prone process for complex scenes with parallax, appearance, and illumination changes. Both our SfM (BA4S) and our analytical ground-plane georegistration method avoid the use of iterative consensus combinatorial methods like RANdom SAmple Consensus which is a core part of many published approaches. BA4S is very efficient for long sequential imagery and is more than 130 times faster than VisualSfM, 35 times faster than MavMap, and about 274 times faster than Pix4D. Various experimental results demonstrate the efficiency and robustness of the proposed pipeline for the refinement of camera parameters in sequential aerial imagery and georegistration. Numéro de notice : A2017-501 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2017.2695172 En ligne : http://dx.doi.org/10.1109/TGRS.2017.2695172 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=86444 [article]

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

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 55 n° 8 (August 2017) . - pp 4285 - 4295 Titre : Superpixel-based intrinsic image decomposition of hyperspectral images Type de document : Article/Communication Auteurs : Xudong Jin, Auteur ; Yanfeng Gu, Auteur Année de publication : 2017 Article en page(s) : pp 4285 - 4295 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes descripteurs IGN] classification pixellaire [Termes descripteurs IGN] décomposition d'image [Termes descripteurs IGN] décomposition du pixel [Termes descripteurs IGN] image hyperspectrale [Termes descripteurs IGN] matrice [Termes descripteurs IGN] méthode de réduction d'énergie Résumé : (Auteur) In this paper, we propose a novel superpixel-based intrinsic image decomposition (SIID) framework for hyperspectral images. Intrinsic images are usually referred to the separation of shading and reflectance components from an input image. Considering the high dimensionality of hyperspectral images, we further decompose the shading component into the product of environment illumination and surface orientation changes, thus modeling the problem more properly. The proposed method consists of the following steps. First, we build two superpixel segmentation maps of different scales, i.e., a finer one that is oversegmented and a coarser one that is undersegmented. Based on the observation that the finer superpixel map achieves a higher segmentation accuracy, whereas the coarser superpixel map tends to reserve the objectness of the original image, we model the SIID decomposition problem in a matrix form based on the finer superpixel map and define a constraint matrix by integrating the information in the coarser superpixel map. The constraint matrix is introduced as a secondary constraint in order to make the ill-posed IID problem solvable. Finally, we transform the original decomposition problem into minimizing the Frobenius norm of the proposed matrix energy function and iteratively derive the solution. Our experimental results demonstrate that the proposed method is able to achieve a performance outperforming the state-of-the-art while making a great improvement in efficiency. Numéro de notice : A2017-493 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2017.2690445 En ligne : http://dx.doi.org/10.1109/TGRS.2017.2690445 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=86423 [article]

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

Public [article]  inInternational journal of computer vision > vol 122 n° 1 (March 2017) . - pp 1 – 16 Titre : Refining geometry from depth sensors using IR shading images Type de document : Article/Communication Auteurs : Gyeongmin Choe, Auteur ; Jaesik Park, Auteur ; Yu-Wing Tai, Auteur ; In So Kweon, Auteur Année de publication : 2017 Article en page(s) : pp 1 – 16 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes descripteurs IGN] albedo [Termes descripteurs IGN] bande infrarouge [Termes descripteurs IGN] décomposition d'image [Termes descripteurs IGN] géométrie affine [Termes descripteurs IGN] image optique [Termes descripteurs IGN] Kinect [Termes descripteurs IGN] maille triangulaire [Termes descripteurs IGN] ombre Résumé : (auteur) We propose a method to refine geometry of 3D meshes from a consumer level depth camera, e.g. Kinect, by exploiting shading cues captured from an infrared (IR) camera. A major benefit to using an IR camera instead of an RGB camera is that the IR images captured are narrow band images that filter out most undesired ambient light, which makes our system robust against natural indoor illumination. Moreover, for many natural objects with colorful textures in the visible spectrum, the subjects appear to have a uniform albedo in the IR spectrum. Based on our analyses on the IR projector light of the Kinect, we define a near light source IR shading model that describes the captured intensity as a function of surface normals, albedo, lighting direction, and distance between light source and surface points. To resolve the ambiguity in our model between the normals and distances, we utilize an initial 3D mesh from the Kinect fusion and multi-view information to reliably estimate surface details that were not captured and reconstructed by the Kinect fusion. Our approach directly operates on the mesh model for geometry refinement. We ran experiments on our algorithm for geometries captured by both the Kinect I and Kinect II, as the depth acquisition in Kinect I is based on a structured-light technique and that of the Kinect II is based on a time-of-flight technology. The effectiveness of our approach is demonstrated through several challenging real-world examples. We have also performed a user study to evaluate the quality of the mesh models before and after our refinements. Numéro de notice : A2017-174 Thématique : IMAGERIE Nature : Article En ligne : https://doi.org/10.1007/s11263-016-0937-y Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=85921 [article]

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)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 54 n° 11 (November 2016) . - pp 6625 - 6643 Titre : Fast three-dimensional empirical mode decomposition of hyperspectral images for class-oriented multitask learning Type de document : Article/Communication Auteurs : Zhi He, Auteur ; Jun Li, Auteur ; Lin Liu, Auteur ; et al., Auteur Année de publication : 2016 Article en page(s) : pp 6625 - 6643 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes descripteurs IGN] apprentissage automatique [Termes descripteurs IGN] classification [Termes descripteurs IGN] décomposition d'image [Termes descripteurs IGN] image 3D Résumé : (Auteur) In this paper, we propose a fast 3-D empirical mode decomposition (fTEMD) method for hyperspectral images (HSIs) to achieve class-oriented multitask learning (cMTL). The major steps of the proposed method are twofold: 1) fTEMD and 2) cMTL. On the one hand, the traditional empirical mode decomposition is extended to its 3-D version, which naturally treats the HSI as a cube and effectively decomposes the HSI into several 3-D intrinsic mode functions (TIMFs). To accelerate the fTEMD, 3-D Delaunay triangulation is adopted to determine the distances of extrema, whereas separable filters are implemented to generate the envelopes. On the other hand, cMTL is performed on the TIMFs by taking those TIMFs as features of different tasks. The proposed cMTL learns the representation coefficients by taking advantage of the class labels and fully exploiting the information contained in each TIMF. Experiments conducted on three benchmark data sets demonstrate the effectiveness of the proposed method. Numéro de notice : A2016-916 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern En ligne : http://dx.doi.org/10.1109/TGRS.2016.2587672 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=83143 [article]

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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Soil moisture retrieval in agricultural fields using adaptive model-based polarimetric decomposition of SAR data / Lian He in IEEE Transactions on geoscience and remote sensing, vol 54 n° 8 (August 2016)  

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Multiple morphological component analysis based decomposition for remote sensing image classification / Xiang Xu in IEEE Transactions on geoscience and remote sensing, vol 54 n° 5 (May 2016)  

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Compressive sensing for multibaseline polarimetric SAR tomography of forested areas / Xinwu Li in IEEE Transactions on geoscience and remote sensing, vol 54 n° 1 (January 2016)  

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Linear models for airborne-laser-scanning-based operational forest inventory with small field sample size and highly correlated LiDAR data / Virpi Junttila in IEEE Transactions on geoscience and remote sensing, vol 53 n° 10 (October 2015)

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Hyperspectral and multispectral image fusion based on a sparse representation / Qi Wei in IEEE Transactions on geoscience and remote sensing, vol 53 n° 7 (July 2015)

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MTF-adjusted pansharpening approach based on coupled multiresolution decompositions / Abdelaziz Kallel in IEEE Transactions on geoscience and remote sensing, vol 53 n° 6 (June 2015)

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CAESAR: an approach based on covariance matrix decomposition to improve multibaseline–multitemporal interferometric SAR processing / Gianfranco Fornaro in IEEE Transactions on geoscience and remote sensing, vol 53 n° 4 (April 2015)

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Polarimetric incoherent target decomposition by means of independent component analysis / Nikola Besic in IEEE Transactions on geoscience and remote sensing, vol 53 n° 3 (March 2015)

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