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Fusion on multispectral and panchromatic images using improved IHS and PCA mergers based on wavelet decomposition / M. Gonzalez-Audicana in IEEE Transactions on geoscience and remote sensing, vol 42 n° 6 (June 2004)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 42 n° 6 (June 2004) . - pp 1291 - 1299 Titre : Fusion on multispectral and panchromatic images using improved IHS and PCA mergers based on wavelet decomposition Type de document : Article/Communication Auteurs : M. Gonzalez-Audicana, Auteur ; J.L. Saleta, Auteur ; et al., Auteur Année de publication : 2004 Article en page(s) : pp 1291 - 1299 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse comparative [Termes IGN] analyse en composantes principales [Termes IGN] fusion d'images [Termes IGN] image multibande [Termes IGN] image panchromatique [Termes IGN] image SPOT [Termes IGN] transformation en ondelettes [Termes IGN] transformation intensité-teinte-saturation Résumé : (Auteur) Since Chavez proposed the highpass filtering procedure to fuse multispectral and panchromatic images, several fusion methods have been developed based on the same principle: to extract from the panchromatic image spatial detail information to later inject it into the multispectral one. In this paper, we present new fusion alternatives based on the same concept, using the mul-resolution wavelet decomposition to execute the detail extraction phase and the intensity-hue-saturation (IHS) and principal component analysis (PCA) procedures to inject the spatial detail of the panchromatic image into the multispectral one. The multiresoluson wavelet decomposition has been performed using both decimated and undecimated algorithms and the resulting merged images compared both spectral and spatially. These fusion methods, as well as standard IHS-, PCA-, and wavelet-based methods have been used to merge Systeme Pour l'Observation de la Terre (SPOT) A XI and SPOT 4 M images with a ratio 4: 1. We have estimated the validity of each fusion method by analyzing, visually and quantitatively, the quality of the resulting fused images. The methoddogical approaches proposed in this paper result in merged images with improved quality with respect to those obtained by standard IHS, PCA, and standard wavelet-based fusion methods. For both proposed fusion methods, better results are obtained when an undecimated algorithm is used to perform the multiresolution wavelet decomposition. Numéro de notice : A2004-262 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2004.825593 En ligne : https://doi.org/10.1109/TGRS.2004.825593 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=26789 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
065-04061	RAB	Revue	Centre de documentation	En réserve L003	Disponible

Estimation of subpixel target size for remotely sensed imagery / C.I. Chang in IEEE Transactions on geoscience and remote sensing, vol 42 n° 6 (June 2004)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 42 n° 6 (June 2004) . - pp 1309 - 1320 Titre : Estimation of subpixel target size for remotely sensed imagery Type de document : Article/Communication Auteurs : C.I. Chang, Auteur ; H. Ren, Auteur ; et al., Auteur Année de publication : 2004 Article en page(s) : pp 1309 - 1320 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes IGN] analyse infrapixellaire [Termes IGN] analyse linéaire des mélanges spectraux [Termes IGN] estimation statistique [Termes IGN] identification automatique [Termes IGN] méthode des moindres carrés Résumé : (Auteur) One of the challenges in remote sensing image processing is subpixel detection where the target size is smaller than the ground sampling distance, therefore, embedded in a single pixel. Under such a circumstance, these targets can be only detected spectrally at the subpixel level, not spatially as ordinarily conducted by classical image processing techniques. This paper investigates a more challenging issue than subpixel detection, which is the estimation of target size at the subpixel level. More specifically, when a subpixel target is detected, we would like to know "what is the size of this particular target within the pixel?" The proposed approach is to estimate the abundance fraction of a subpixel target present in a pixel, then find what portion it contributes to the pixel that can be used to determine the size of the subpixel target by multiplying the ground sampling distance. In order to make our idea work, the subpixel target abundance fraction must be accurately estimated to truly reflect the portion of a subpixel target occupied within a pixel. So, a fully constrained linear unmixing method is required to reliably estimate the abundance fractions of a subpixel target for its size estimation. In this paper, a recently developed fully constrained least squares linear unmixing is used for this purpose. Experiments are conducted to demonstrate the utility of the proposed method in comparison with an unconstrained linear unmixing method, onconstrained least squares method, two partially constrained last square linear unmixing methods, sum-to-one constrained least squares, and nonnegativity constrained least squares. Numéro de notice : A2004-263 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2004.826559 En ligne : https://doi.org/10.1109/TGRS.2004.826559 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=26790 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
065-04061	RAB	Revue	Centre de documentation	En réserve L003	Disponible

An advanced system for the automatic classification of multitemporal SAR images / Lorenzo Bruzzone in IEEE Transactions on geoscience and remote sensing, vol 42 n° 6 (June 2004)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 42 n° 6 (June 2004) Titre : An advanced system for the automatic classification of multitemporal SAR images Type de document : Article/Communication Auteurs : Lorenzo Bruzzone, Auteur ; Mattia Marconcini, Auteur ; et al., Auteur Année de publication : 2004 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes IGN] classification automatique [Termes IGN] classification par réseau neuronal [Termes IGN] coefficient de rétrodiffusion [Termes IGN] extraction automatique [Termes IGN] image ERS-SAR [Termes IGN] image multitemporelle [Termes IGN] reconnaissance de formes Résumé : (Auteur) A novel system for the classification of multitemporal synthetic aperture radar (SAR) images is presented. It has been developed by integrating an analysis of the multitemporal SAR signal physics with a pattern recognition approach. The system is made up of a feature-extraction module and a neural-network classifier, as well as a set of standard preprocessing procedures. The feature-extraction module derives a set of features from a series of multitemporal SAR images. These features are based on the concepts of long-term coherence and backscattering temporal variability and have been defined according to an analysis of the multitemporal SAR signal behavior in the presence of different land-cover classes. The neural-network classifier (which is based on a radial basis function neural architecture) properly exploits the multitemporal features for producing accurate land-cover maps. Thanks to the effectiveness of the extracted features, the number of measures that can be provided as input to the classifier is significantly smaller than the number of available multitemporal images. This reduces the complexity of the neural architecture (and consequently increases the generalization capabilities of the classifier) and relaxes the requirements relating to the number of training patterns to be used for classifier learning. Experimental results (obtained on a multitemporal series of European Remote Sensing 1 satellite SAR images) confirm the effectiveness of the proposed system, which exhibits both high classification accuracy and good stability versus parameter settings. These results also point out that properly integrating a pattern recognition procedure (based on machine learning) with an accurate feature extraction phase (based on the SAR sensor physics understanding) represents an effective approach to SAR data analysis. Numéro de notice : A2004-264 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2004.826821 En ligne : https://doi.org/10.1109/TGRS.2004.826821 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=26791 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
065-04061	RAB	Revue	Centre de documentation	En réserve L003	Disponible