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Decision fusion in kernel-induced spaces for hyperspectral image classification / Wei Li in IEEE Transactions on geoscience and remote sensing, vol 52 n° 6 Tome 2 (June 2014)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 52 n° 6 Tome 2 (June 2014) . - pp 3399 - 3411 Titre : Decision fusion in kernel-induced spaces for hyperspectral image classification Type de document : Article/Communication Auteurs : Wei Li, Auteur ; Saurabh Prasad, Auteur ; James E. Fowler, Auteur Année de publication : 2014 Article en page(s) : pp 3399 - 3411 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] classification [Termes IGN] fusion d'images [Termes IGN] image hyperspectrale Résumé : (Auteur) The one-against-one (OAO) strategy is commonly employed with classifiers-such as support vector machines-which inherently provide binary two-class classification in order to handle multiple classes. This OAO strategy is introduced for the classification of hyperspectral imagery using discriminant analysis within kernel-induced feature spaces, producing a pair of algorithms-kernel discriminant analysis and kernel local Fisher discriminant analysis-for dimensionality reduction, which are followed by a quadratic Gaussian maximum-likelihood-estimation classifier. In the proposed approach, a multiclass problem is broken down into all possible binary classifiers, and various decision-fusion rules are considered for merging results from this classifier ensemble. Experimental results using several hyperspectral data sets demonstrate the benefits of the proposed approach-in addition to improved classification performance, the resulting classifier framework requires reduced memory for estimating kernel matrices. Numéro de notice : A2014-309 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2013.2272760 En ligne : https://doi.org/10.1109/TGRS.2013.2272760 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=33212 [article]

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Significance analysis of different types of ancillary geodata utilized in a multisource classification process for forest identification in Germany / Michael Förster in IEEE Transactions on geoscience and remote sensing, vol 52 n° 6 Tome 2 (June 2014)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 52 n° 6 Tome 2 (June 2014) . - pp 3453 - 3463 Titre : Significance analysis of different types of ancillary geodata utilized in a multisource classification process for forest identification in Germany Type de document : Article/Communication Auteurs : Michael Förster, Auteur ; B. Kleinschmit, Auteur Année de publication : 2014 Article en page(s) : pp 3453 - 3463 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse d'image numérique [Termes IGN] classification floue [Termes IGN] données multisources [Termes IGN] forêt [Termes IGN] image à très haute résolution [Termes IGN] télédétection spatiale Résumé : (Auteur) Ancillary geodata can supply information to enhance classification accuracy for a variety of remote-sensing applications. To understand the integration of different data into a knowledge-based multisource classification process, this paper evaluates the significance of geodata for the classification accuracy of a very high spatial resolution satellite image for the identification of forest types in Germany. The approach utilizes a fuzzy-logic classifier for the integration of a knowledge base, which combines spectral information with ancillary data layers. The results of the classification were used to test a method for evaluating the influence of the integration of single geodata, the effects on different classes, and the impacts of the applied rules. A microarray significance analysis (MSA) was used to evaluate the significance of the classification results, whereas an ISODATA clustering was utilized for visualizing. A sequence of 50 accuracy assessments of classifications with possible combinations of geodata and rules for the identified classes was derived. The resulting microarray of accuracy percentages of single classes and the overall classification was used for further investigation. The MSA supplies the measure of significance, called relative difference d(i). The MSA identified 11 classifications of positive significance (d(i) greater than 1.44) and three classifications of negative significance (d(i) lower than -2.87). In particular, classifications that contain all rules were rated as positive significant. Numéro de notice : A2014-310 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2013.2273080 En ligne : https://doi.org/10.1109/TGRS.2013.2273080 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=33213 [article]

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Facade reconstruction using multiview spaceborne TomoSAR point clouds / Xiao Xiang Zhu in IEEE Transactions on geoscience and remote sensing, vol 52 n° 6 Tome 2 (June 2014)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 52 n° 6 Tome 2 (June 2014) . - pp 3541 - 3552 Titre : Facade reconstruction using multiview spaceborne TomoSAR point clouds Type de document : Article/Communication Auteurs : Xiao Xiang Zhu, Auteur ; Muhammad Shahzad, Auteur Année de publication : 2014 Article en page(s) : pp 3541 - 3552 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques [Termes IGN] données 4D [Termes IGN] façade [Termes IGN] reconstruction 3D du bâti [Termes IGN] semis de points [Termes IGN] tomographie radar Résumé : (Auteur) Recent advances in very high resolution tomographic synthetic aperture radar inversion (TomoSAR) using multiple data stacks from different viewing angles enables us to generate 4-D (space-time) point clouds of the illuminated area from space with a point density comparable to LiDAR. They can be potentially used for facade reconstruction and deformation monitoring in urban environment. In this paper, we present the first attempt to reconstruct facades from this class of data: First, the facade region is extracted using the density estimates of the points projected to the ground plane, the extracted facade points are then clustered into individual facades by means of orientation analysis, surface (flat or curved) model parameters of the segmented building facades are further estimated, and the geometric primitives such as intersection points of the adjacent facades are determined to complete the reconstruction process. The proposed approach is illustrated and validated by examples using TomoSAR point clouds generated from stacks of TerraSAR-X high-resolution spotlight images from two viewing angles, i.e., both ascending and descending orbits. The performance of the proposed approach is systematically analyzed. To explore the possible applications, we refine the elevation estimate of each raw TomoSAR point by using its more accurate azimuth and range coordinates and the corresponding reconstructed building facade model. Compared to the raw TomoSAR point clouds, significantly improved elevation positioning accuracy is achieved. Finally, a first example of the reconstructed 4-D city model is presented. Numéro de notice : A2014-311 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2013.2273619 En ligne : https://ieeexplore.ieee.org/document/6573417 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=33214 [article]

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Semisupervised dual-geometric subspace projection for dimensionality reduction of hyperspectral image data / Shuyuan Yang in IEEE Transactions on geoscience and remote sensing, vol 52 n° 6 Tome 2 (June 2014)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 52 n° 6 Tome 2 (June 2014) . - pp 3587 - 3593 Titre : Semisupervised dual-geometric subspace projection for dimensionality reduction of hyperspectral image data Type de document : Article/Communication Auteurs : Shuyuan Yang, Auteur ; Penglei Jin, Auteur ; Bin Li, Auteur ; et al., Auteur Année de publication : 2014 Article en page(s) : pp 3587 - 3593 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] classification semi-dirigée [Termes IGN] image hyperspectrale [Termes IGN] matrice [Termes IGN] réduction géométrique Résumé : (Auteur) Exploring the geometric prior in the dimensionality reduction (DR) of hyperspectral image data (HID) is an important issue because it can overcome the possible overclassification of spectrally homogeneous areas in the HID classification. In this paper, the local geometric similarity of hyperspectral vectors is explored in both the manifold domain and image domain, and a semisupervised dual-geometric subspace projection (DGSP) approach is proposed for the DR of HID, by utilizing both labeled and unlabeled samples. First, the geometric information in the manifold domain is captured by a sparse coding-based geometric graph, and then, a local-consistency-constrained geometric matrix is defined to reveal the geometric structure in the image domain. Second, unlabeled samples are used to refine the geometric structure by defining a pairwise similarity matrix. Third, three scatter matrices are then derived from these similarity matrices to find the optimal subspace projection that captures the most important properties of the subspaces with respect to classification. Some experiments are taken on the airborne visible infrared imaging spectrometer (AVIRIS) HID to prove the efficiency of the proposed method. Numéro de notice : A2014-312 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2013.2273798 En ligne : https://doi.org/10.1109/TGRS.2013.2273798 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=33215 [article]

Exemplaires(1)

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Crop type classification by simultaneous use of satellite images of different resolutions / Mark W. Liu in IEEE Transactions on geoscience and remote sensing, vol 52 n° 6 Tome 2 (June 2014)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 52 n° 6 Tome 2 (June 2014) . - pp 3637 - 3649 Titre : Crop type classification by simultaneous use of satellite images of different resolutions Type de document : Article/Communication Auteurs : Mark W. Liu, Auteur ; Mutlu Ozdogan, Auteur ; Xiaojin Zhu, Auteur Année de publication : 2014 Article en page(s) : pp 3637 - 3649 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] agriculture [Termes IGN] analyse multirésolution [Termes IGN] carte de la végétation [Termes IGN] classification [Termes IGN] fréquence [Termes IGN] réflectance végétale Résumé : (Auteur) Accurate and timely identification of crop types has significant economic, agricultural, policy, and environmental applications. The existing remote sensing methods to identify crop types rely on remotely sensed images of high temporal frequency in order to utilize phenological changes in crop reflectance characteristics. However, these image sets generally have relatively low spatial resolution. This tradeoff makes it difficult to classify remotely sensed images in fragmented landscapes where field sizes are smaller than the resolution of imaging sensor. Here, we develop a method for combining high spatial resolution (high-resolution) data with images with low spatial resolution but with high time frequency to achieve a superior classification of crop types. The solution is implemented and tested on both synthetic and real data sets as a proof of concept. We show that, by incorporating high-temporal-frequency but low spatial resolution data into the classification process, up to 20% of improvement in classification accuracy can be achieved even if very few high-resolution images are available for a location. This boost in accuracy is roughly equivalent to including an additional high-resolution image to the temporal stack during the classification process. The limitations of the current algorithm include computational performance and the need for ideal crop curves. Nevertheless, the resulting boost in accuracy can help researchers create superior crop type classification maps, thereby creating the opportunity to make more informed decisions. Numéro de notice : A2014-313 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2013.2274431 En ligne : https://doi.org/10.1109/TGRS.2013.2274431 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=33216 [article]

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Feature extraction of hyperspectral images with image fusion and recursive filtering / Xudong Kang in IEEE Transactions on geoscience and remote sensing, vol 52 n° 6 Tome 2 (June 2014)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 52 n° 6 Tome 2 (June 2014) . - pp 3742 - 3753 Titre : Feature extraction of hyperspectral images with image fusion and recursive filtering Type de document : Article/Communication Auteurs : Xudong Kang, Auteur ; Shutao Li, Auteur ; Jon Atli Benediktsson, Auteur Année de publication : 2014 Article en page(s) : pp 3742 - 3753 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] classification par séparateurs à vaste marge [Termes IGN] extraction de traits caractéristiques [Termes IGN] filtrage numérique d'image [Termes IGN] fusion d'images [Termes IGN] image hyperspectrale Résumé : (Auteur) Feature extraction is known to be an effective way in both reducing computational complexity and increasing accuracy of hyperspectral image classification. In this paper, a simple yet quite powerful feature extraction method based on image fusion and recursive filtering (IFRF) is proposed. First, the hyperspectral image is partitioned into multiple subsets of adjacent hyperspectral bands. Then, the bands in each subset are fused together by averaging, which is one of the simplest image fusion methods. Finally, the fused bands are processed with transform domain recursive filtering to get the resulting features for classification. Experiments are performed on different hyperspectral images, with the support vector machines (SVMs) serving as the classifier. By using the proposed method, the accuracy of the SVM classifier can be improved significantly. Furthermore, compared with other hyperspectral classification methods, the proposed IFRF method shows outstanding performance in terms of classification accuracy and computational efficiency. Numéro de notice : A2014-314 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2013.2275613 En ligne : https://doi.org/10.1109/TGRS.2013.2275613 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=33217 [article]

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