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A variational gradient-based fusion method for visible and SWIR imagery / H. Li in Photogrammetric Engineering & Remote Sensing, PERS, vol 78 n° 9 (September 2012)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 78 n° 9 (September 2012) . - pp 947 - 958 Titre : A variational gradient-based fusion method for visible and SWIR imagery Type de document : Article/Communication Auteurs : H. Li, Auteur ; L. Zhang, Auteur ; H. Shen, Auteur ; P. Li, Auteur Année de publication : 2012 Article en page(s) : pp 947 - 958 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] appariement d'images [Termes IGN] débrumage [Termes IGN] effet atmosphérique [Termes IGN] fusion d'images [Termes IGN] gradient [Termes IGN] image Landsat-ETM+ [Termes IGN] image Landsat-SWIR [Termes IGN] longueur d'onde Résumé : (Auteur) This paper presents a new variational gradient-based fusion method for visible and short-wave infrared (swm) imagery. The proposed method enables spatial enhancement and dehazing of visible imagery. Integrating gradients from SWIR imagery into visible imagery produces a single image with true color and sharp gradients. A constraint based on band correlation is included to improve the enhancement and implement dehazing. The band correlation is according to the quantitative relationship between the wavelength and the atmospheric effect caused by Rayleigh scattering. In this study, both clear and hazy Landsat ETM+ images are used in the experiments. By visual assessment, the gradient of the fused image is more salient than that of the original image, and the true color is well preserved. With the inclusion of the band correlation constraint, the proposed fusion method yields almost haze-free results. Quantitatively, the Metric Q of the fused images is significantly higher than that of the original images; the largest increase of the Metric Q in the experimental results is from 0.0114 to 0.0611. Moreover, for the results of the proposed method, the Metric Q increase in the visible bands declines from blue band to red band. Numéro de notice : A2012-442 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.14358/PERS.78.9.947 En ligne : https://doi.org/10.14358/PERS.78.9.947 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=31888 [article]

Land-cover change detection using one-class support vector machine / P. Li in Photogrammetric Engineering & Remote Sensing, PERS, vol 76 n° 3 (March 2010)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 76 n° 3 (March 2010) . - pp 255 - 263 Titre : Land-cover change detection using one-class support vector machine Type de document : Article/Communication Auteurs : P. Li, Auteur ; H. Xu, Auteur Année de publication : 2010 Article en page(s) : pp 255 - 263 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes IGN] analyse comparative [Termes IGN] classification par séparateurs à vaste marge [Termes IGN] détection de changement [Termes IGN] données multitemporelles [Termes IGN] Kappa de Cohen [Termes IGN] occupation du sol [Termes IGN] traitement d'image Résumé : (Auteur) Change detection using remote sensing has considerable potential for monitoring land-cover change. Commonly, one specific class of change is of interest in many applications. In this paper, a recently developed one-class classifier, the One-Class Support Vector Machine (OCSVM), is proposed for the change detection of one specific class by multitemporal classification. The classifier only requires samples from the change class of interest as the training data. The performance of the proposed method was evaluated in two applications by comparing with conventional post-classification comparison methods. The results demonstrated the proposed method achieved both higher overall accuracy and higher kappa coefficient than the conventional methods, and demonstrated good potential for further application. The study also indicated that with the ocsvm, the analysis can focus only on the specific class of interest and does not need to treat other classes, thus providing highly accurate change detection. The OCSVM-based change detection method, as a general and easily implemented method, can be used for applications where only the change of one specific class is of interest. Copyright ASPRS Numéro de notice : A2010-087 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.14358/PERS.76.3.255 En ligne : https://doi.org/10.14358/PERS.76.3.255 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=30283 [article]

Classification of very high spatial resolution imagery based on the fusion of edge and multispectral information / X. Huang in Photogrammetric Engineering & Remote Sensing, PERS, vol 74 n° 12 (December 2008)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 74 n° 12 (December 2008) . - pp 1585 - 1596 Titre : Classification of very high spatial resolution imagery based on the fusion of edge and multispectral information Type de document : Article/Communication Auteurs : X. Huang, Auteur ; L. Zhang, Auteur ; P. Li, Auteur Année de publication : 2008 Article en page(s) : pp 1585 - 1596 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse en composantes indépendantes [Termes IGN] classification par séparateurs à vaste marge [Termes IGN] détection de contours [Termes IGN] extraction de traits caractéristiques [Termes IGN] image à résolution subdecamétrique [Termes IGN] image à très haute résolution [Termes IGN] image multibande [Termes IGN] image Quickbird [Termes IGN] matrice de co-occurrence [Termes IGN] Normalized Difference Vegetation Index [Termes IGN] prise en compte du contexte Résumé : (Auteur) A new algorithm based on the fusion of edge and multispectral information is proposed for the pixel-wise classification of very high-resolution (VHR) remotely sensed imagery. It integrates the multispectral, spatial and structural information existing in the image. The edge feature is first extracted using an improved multispectral edge detection method, which takes into account the original multispectral bands, the linear NDVI, and the independent spectral components extracted by independent component analysis (ICA). Direction-lines are then defined using the edge and multispectral information. Two effective spatial measures are calculated based on the direction-lines in order to describe the contextual information and strengthen the multispectral feature space. Then, the support vector machine (SVM) is employed to classify the hybrid structural-multispectral feature set. In experiments, the proposed spatial measures were compared with the pixel shape index (PSI) and the gray level co-occurrence matrix (GLCM). The experimental results show that the proposed algorithm performs well in terms of classification accuracies and visual interpretation. Copyright ASPRS Numéro de notice : A2008-479 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.14358/PERS.74.12.1585 En ligne : https://doi.org/10.14358/PERS.74.12.1585 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=29548 [article]

A supervised artificial immune classifier for remote-sensing imagery / Y. Zhong in IEEE Transactions on geoscience and remote sensing, vol 45 n° 12 Tome 1 (December 2007)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 45 n° 12 Tome 1 (December 2007) . - pp 3957 - 3966 Titre : A supervised artificial immune classifier for remote-sensing imagery Type de document : Article/Communication Auteurs : Y. Zhong, Auteur ; L. Zhang, Auteur ; J. Gong, Auteur ; P. Li, Auteur Année de publication : 2007 Article en page(s) : pp 3957 - 3966 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes IGN] classificateur [Termes IGN] classification dirigée [Termes IGN] image [Termes IGN] réseau neuronal artificiel [Termes IGN] système immunitaire artificiel Résumé : (Auteur) The artificial immune network (AIN), which is a new computational intelligence model based on artificial immune systems inspired by the vertebrate immune system, has been widely utilized for pattern recognition and data analysis. However, due to the inherent complexity of current AIN models, their application to remote-sensing image classification has been rather limited. This paper presents a novel supervised classification algorithm based on a multiple-valued immune network, which is a novel AIN model, to perform remote-sensing image classification. The proposed method trains the immune network using the samples of regions of interest and obtains an immune network with memory to classify the remote-sensing imagery. Two experiments with different types of images are performed to evaluate the performance of the proposed algorithm in comparison with other traditional image classification algorithms: Parallelepiped, Minimum Distance, Maximum Likelihood, and Back-Propagation Neural Network. The results evince that the proposed algorithm consistently outperforms the traditional algorithms in all the experiments and, hence, provides an effective option for processing remote-sensing imagery. Copyright IEEE Numéro de notice : A2007-585 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2007.907739 En ligne : https://doi.org/10.1109/TGRS.2007.907739 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=28948 [article]

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