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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)
[article]
Titre : MTF-adjusted pansharpening approach based on coupled multiresolution decompositions Type de document : Article/Communication Auteurs : Abdelaziz Kallel, Auteur Année de publication : 2015 Article en page(s) : pp 3124 - 3145 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse multirésolution
[Termes IGN] décomposition d'image
[Termes IGN] fonction de transfert de modulation
[Termes IGN] image Geoeye
[Termes IGN] image multibande
[Termes IGN] image Pléiades-HR
[Termes IGN] image SPOT 6
[Termes IGN] pansharpening (fusion d'images)
[Termes IGN] transformation en ondelettesRésumé : (Auteur) Among others, the wavelet-based pansharpening approach tries to enhance the resolution of the multispectral (MS) image by injection of spatial details extracted from the high-resolution panchromatic (PAN) image. The problem is presented as follows, the inputs are a coarse-resolution MS image and a high-resolution detail image provided from the PAN image; therefore, one would think that the wavelet reconstruction allows combining approximations and details to construct the high-resolution MS image. However, the wavelet transform (WT) assumes that details and approximations are calculated using the same wavelet decomposition. Now, in the pansharpening case, the MS low-resolution image is assumed to be aliased and blurred due to the imaging system modulation transfer function (MTF) that is approximated as a specific low-pass filter. Meanwhile, there are no constraints about details that can be extracted from PAN using discrete WT (DWT). Approximation and details are not any more orthogonal as needed in the reconstruct of the MS high-resolution image based on DWT. For that, we propose in this paper a new fusion schema [coupled multiresolution decomposition model (CMD)] allowing the reconstruction of a high-resolution MS given its approximation and details obtained by MTF-tailored downsampling and wavelet decomposition, respectively. For validation, CMD is applied to Pléiades, GeoEye-1, and SPOT 6 images. Compared to other approaches [i.e., Gram-Schmidt (GS) adaptive, GS mode 2 (GS2), “À trous' WT (AWT), generalized Laplacian pyramid (GLP), DWT, and PCI Geomatics software algorithm], our method performs generally better. Numéro de notice : A2015-282 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2014.2369056 Date de publication en ligne : 19/12/2014 En ligne : https://doi.org/10.1109/TGRS.2014.2369056 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=76399
in IEEE Transactions on geoscience and remote sensing > vol 53 n° 6 (June 2015) . - pp 3124 - 3145[article]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 065-2015061 SL Revue Centre de documentation Revues en salle Disponible Hyperspectral image classification based on three-dimensional scattering wavelet transform / Yuan Yan Tang in IEEE Transactions on geoscience and remote sensing, vol 53 n° 5 (mai 2015)
[article]
Titre : Hyperspectral image classification based on three-dimensional scattering wavelet transform Type de document : Article/Communication Auteurs : Yuan Yan Tang, Auteur ; Y. Lu, Auteur ; Haoliang Yuan, Auteur Année de publication : 2015 Article en page(s) : pp 2467 - 2480 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] classification spectrale
[Termes IGN] diffusion spatiale
[Termes IGN] filtrage numérique d'image
[Termes IGN] image hyperspectrale
[Termes IGN] transformation en ondelettesRésumé : (Auteur) Recent research has shown that utilizing the spectral-spatial information can improve the performance of hyperspectral image (HSI) classification. Since HSI is a 3-D cube datum, 3-D spatial filtering becomes a simple and effective method for extracting the spectral-spatial information. In this paper, we propose a 3-D scattering wavelet transform, which filters the HSI cube data with a cascade of wavelet decompositions, complex modulus, and local weighted averaging. The scattering feature can adequately capture the spectral-spatial information for classification. In the classification step, a support vector machine based on Gaussian kernel is used as a classifier due to its capability to deal with high-dimensional data. Our method is fully evaluated on four classic HSIs, i.e., Indian Pines, Pavia University, Botswana, and Kennedy Space Center. The classification results show that our method achieves as high as 94.46%, 99.30%, 97.57%, and 95.20% accuracies, respectively, when only 5% of the total samples per class is labeled. Numéro de notice : A2015-518 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2014.2360672 En ligne : https://doi.org/10.1109/TGRS.2014.2360672 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=77524
in IEEE Transactions on geoscience and remote sensing > vol 53 n° 5 (mai 2015) . - pp 2467 - 2480[article]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 065-2015051 RAB Revue Centre de documentation En réserve L003 Disponible Kernel sparse multitask learning for hyperspectral image classification with empirical mode decomposition and morphological wavelet-based features / Z. He in IEEE Transactions on geoscience and remote sensing, vol 52 n° 8 Tome 2 (August 2014)
[article]
Titre : Kernel sparse multitask learning for hyperspectral image classification with empirical mode decomposition and morphological wavelet-based features Type de document : Article/Communication Auteurs : Z. He, Auteur ; Qiang Wang, Auteur ; Y. Shen, Auteur ; et al., Auteur Année de publication : 2014 Article en page(s) : pp 5150 -5163 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] classificateur
[Termes IGN] décomposition en fonctions orthogonales empiriques
[Termes IGN] image hyperspectrale
[Termes IGN] pouvoir de résolution géométrique
[Termes IGN] pouvoir de résolution spectrale
[Termes IGN] précision de la classification
[Termes IGN] transformation en ondelettesRésumé : (Auteur) Recently, many researchers have attempted to exploit spectral-spatial features and sparsity-based hyperspectral image classifiers for higher classification accuracy. However, challenges remain for efficient spectral-spatial feature generation and combination in the sparsity-based classifiers. This paper utilizes the empirical mode decomposition (EMD) and morphological wavelet transform (MWT) to gain spectral-spatial features, which can be significantly integrated by the sparse multitask learning (MTL). In the feature extraction step, the sum of the intrinsic mode functions extracted by an optimized EMD is taken as spectral features, whereas the spatial features are formed by the low-frequency components of one-level MWT. In the classification step, a kernel-based sparse MTL solved by the accelerated proximal gradient is applied to analyze both the spectral and spatial features simultaneously. Experiments are conducted on two benchmark data sets with different spectral and spatial resolutions. It is found that the proposed methods provide more accurate classification results compared to the state-of-the-art techniques with various ratio of training samples. Numéro de notice : A2014-436 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2013.2287022 En ligne : https://doi.org/10.1109/TGRS.2013.2287022 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=73973
in IEEE Transactions on geoscience and remote sensing > vol 52 n° 8 Tome 2 (August 2014) . - pp 5150 -5163[article]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 065-2014081B RAB Revue Centre de documentation En réserve L003 Disponible Deriving leaf mass per area (LMA) from foliar reflectance across a variety of plant species using continuous wavelet analysis / Tao Cheng in ISPRS Journal of photogrammetry and remote sensing, vol 87 (January 2014)
[article]
Titre : Deriving leaf mass per area (LMA) from foliar reflectance across a variety of plant species using continuous wavelet analysis Type de document : Article/Communication Auteurs : Tao Cheng, Auteur ; Benoit Rivard, Auteur ; Arturo G. Sanchez-Azofeifa, Auteur ; et al., Auteur Année de publication : 2014 Article en page(s) : pp 28 - 38 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] biomasse
[Termes IGN] espèce végétale
[Termes IGN] indice foliaire
[Termes IGN] Leaf Mass per Area
[Termes IGN] modèle physique
[Termes IGN] ondelette
[Termes IGN] réflectance végétale
[Termes IGN] réponse spectrale
[Termes IGN] transformation en ondelettesRésumé : (Auteur) Leaf mass per area (LMA), the ratio of leaf dry mass to leaf area, is a trait of central importance to the understanding of plant light capture and carbon gain. It can be estimated from leaf reflectance spectroscopy in the infrared region, by making use of information about the absorption features of dry matter. This study reports on the application of continuous wavelet analysis (CWA) to the estimation of LMA across a wide range of plant species. We compiled a large database of leaf reflectance spectra acquired within the framework of three independent measurement campaigns (ANGERS, LOPEX and PANAMA) and generated a simulated database using the PROSPECT leaf optical properties model. CWA was applied to the measured and simulated databases to extract wavelet features that correlate with LMA. These features were assessed in terms of predictive capability and robustness while transferring predictive models from the simulated database to the measured database. The assessment was also conducted with two existing spectral indices, namely the Normalized Dry Matter Index (NDMI) and the Normalized Difference index for LMA (NDLMA). Five common wavelet features were determined from the two databases, which showed significant correlations with LMA (R2: 0.51–0.82, p Numéro de notice : A2014-009 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2013.10.009 En ligne : https://doi.org/10.1016/j.isprsjprs.2013.10.009 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32914
in ISPRS Journal of photogrammetry and remote sensing > vol 87 (January 2014) . - pp 28 - 38[article]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 081-2014011 RAB Revue Centre de documentation En réserve L003 Disponible
Titre : Signal Processing : A Mathematical Approach Type de document : Monographie Auteurs : Charles L. Byrne, Auteur Editeur : Boca Raton, New York, ... : CRC Press Année de publication : 2014 Importance : 397 p. Format : 16 x 23 cm ISBN/ISSN/EAN : 978-0-429-15871-1 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement du signal
[Termes IGN] détection du signal
[Termes IGN] écho radar
[Termes IGN] filtre de Wiener
[Termes IGN] phase
[Termes IGN] probabilités
[Termes IGN] propagation du signal
[Termes IGN] reconstruction d'image
[Termes IGN] série de Fourier
[Termes IGN] signal acoustique
[Termes IGN] tomographie
[Termes IGN] transformation de Fourier
[Termes IGN] transformation en ondelettesRésumé : (éditeur) A practical guide to the mathematics behind signal processing, this book provides the essential mathematical background and tools necessary to understand and employ signal processing techniques. Topics addressed include: - Fourier series and transforms in one and several variables, - applications to acoustic and electromagnetic propagation models, - transmission and emission tomography and image reconstruction, - optimization techniques, - high-resolution methods, and more. The emphasis is on the general problem of extracting information from limited data obtained by some form of remote sensing: acoustic or radar processing, satellite imaging, or medical tomographic scanning. Note de contenu : 1- Introduction
2- Fourier Series and Fourier Transforms
3- Remote Sensing
4- Finite-Parameter Models
5- Transmission and Remote Sensing
6- The Fourier Transform and Convolution Filtering
7- Infinite Sequences and Discrete Filters
8- Convolution and the Vector DFT
9- Plane-Wave Propagation
10- The Phase Problem
11- Transmission Tomography
12- Random Sequences
13- Nonlinear Methods
14- Discrete Entropy Maximization
15- Analysis and Synthesis
16- Wavelets
17- The BLUE and the Kalman Filter
18- Signal Detection and Estimation
19- Inner Products
20- Wiener Filtering
21- Matrix Theory
22- Compressed Sensing
23- Probability
24- Using the Wave Equation
25- Reconstruction in Hilbert Space
26- Some Theory of Fourier Analysis
27- Reverberation and Echo CancellationNuméro de notice : 25846 Affiliation des auteurs : non IGN Thématique : IMAGERIE/MATHEMATIQUE Nature : Monographie En ligne : https://www.taylorfrancis.com/books/9780429158711 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95260 A combined object- and pixel-based image analysis framework for urban land cover classification of VHR imagery / Bahram Salehi in Photogrammetric Engineering & Remote Sensing, PERS, vol 79 n° 11 (November 2013)PermalinkUtility of the wavelet transform for LAI estimation using hyperspectral data / Asim Banskota in Photogrammetric Engineering & Remote Sensing, PERS, vol 79 n° 7 (July 2013)PermalinkNew constraints on the origin of the Hawaiian swell from wavelet analysis of the geoid-to-topography ratio / Cécilia Cadio in Earth and planetary science letters, vol 359–360 (15 December 2012)PermalinkA wavelet spectral analysis technique for automatic detection of geomagnetic sudden commencements / E. Ghamry in IEEE Transactions on geoscience and remote sensing, vol 50 n° 11 Tome 1 (November 2012)PermalinkInformation fusion in the redundant-wavelet-transform domain for noise-robust hyperspectral classification / S. Prasad in IEEE Transactions on geoscience and remote sensing, vol 50 n° 9 (October 2012)PermalinkWavelet‐based directional analysis of the gravity field : evidence for large‐scale undulations / M. Hayn in Geophysical journal international, vol 189 n° 3 (June 2012)PermalinkA nonlocal SAR image denoising algorithm based on LLMMSE wavelet shrinkage / S. Parrilli in IEEE Transactions on geoscience and remote sensing, vol 50 n° 2 (February 2012)PermalinkEffect of climatic cycles in Pacific Ocean on mean sea level variations over the Southwest Pacific Ocean and Tasman Sea / Anthony Wiart (2012)PermalinkFlexible dataset combination and modelling by domain decomposition approaches / Isabelle Panet (2012)PermalinkInterference suppression algorithm for SAR based on time-frequency transform / S. Zhang in IEEE Transactions on geoscience and remote sensing, vol 49 n° 10 Tome 1 (October 2011)Permalink