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Superpixel-based intrinsic image decomposition of hyperspectral images / Xudong Jin in IEEE Transactions on geoscience and remote sensing, vol 55 n° 8 (August 2017)
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 IGN] classification pixellaire
[Termes IGN] décomposition d'image
[Termes IGN] décomposition du pixel
[Termes IGN] image hyperspectrale
[Termes IGN] matrice
[Termes IGN] méthode de réduction d'énergieRésumé : (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 Affiliation des auteurs : non IGN 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
in IEEE Transactions on geoscience and remote sensing > vol 55 n° 8 (August 2017) . - pp 4285 - 4295[article]
Titre : Guided superpixel method for topographic map processing Type de document : Article/Communication Auteurs : Qiguang Miao, Auteur ; Tiange Liu, Auteur ; Jianfeng Song, Auteur ; et al., Auteur Année de publication : 2016 Article en page(s) : pp 6265 - 6279 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] analyse infrapixellaire
[Termes IGN] cartographie topographique
[Termes IGN] décomposition du pixel
[Termes IGN] détection de contours
[Termes IGN] distribution spatiale
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] lever topographiqueRésumé : (Auteur) Superpixels have been widely used in lots of computer vision and image processing tasks but rarely used in topographic map processing due to the complex distribution of geographic elements in this kind of images. We propose a novel superpixel-generating method based on guided watershed transform (GWT). Before GWT, the cues of geographic element distribution and boundaries between different elements need to be obtained. A linear feature extraction method based on a compound opposite Gaussian filter and a shear transform is presented to acquire the distribution information. Meanwhile, a boundary detection method, which based on the color-opponent mechanisms of the visual system, is employed to get the boundary information. Then, both linear features and boundaries are input to the final partition procedure to obtain superpixels. The experiments show that our method has the best performance in shape control, size control, and boundary adherence, among all the comparison methods, which are classic and state of the art. Furthermore, we verify the low complexity and low cost of memory in our method through experiments, which makes it possible to deal with large-scale topographic maps. Numéro de notice : A2016-911 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2016.2567481 En ligne : https://doi.org/10.1109/TGRS.2016.2567481 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=83133
in IEEE Transactions on geoscience and remote sensing > vol 54 n° 11 (November 2016) . - pp 6265 - 6279[article]
Titre : Superpixel-based graphical model for remote sensing image mapping Type de document : Article/Communication Auteurs : Guangyun Zhang, Auteur ; Xiuping Jia, Auteur ; Jiankun Hu, Auteur Année de publication : 2015 Article en page(s) : pp 5861 - 5871 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] classification contextuelle
[Termes IGN] classification pixellaire
[Termes IGN] décomposition du pixel
[Termes IGN] image multibande
[Termes IGN] modèle sémantique de données
[Termes IGN] segmentation d'imageRésumé : (Auteur) Object-oriented remote sensing image classification is becoming more and more popular because it can integrate spatial information from neighboring regions of different shapes and sizes into the classification procedure to improve the mapping accuracy. However, object identification itself is difficult and challenging. Superpixels, which are groups of spatially connected similar pixels, have the scale between the pixel level and the object level and can be generated from oversegmentation. In this paper, we establish a new classification framework using a superpixel-based graphical model. Superpixels instead of pixels are applied as the basic unit to the graphical model to capture the contextual information and the spatial dependence between the superpixels. The advantage of this treatment is that it makes the classification less sensitive to noise and segmentation scale. The contribution of this paper is the application of a graphical model to remote sensing image semantic segmentation. It is threefold. 1) Gradient fusion is applied to multispectral images before the watershed segmentation algorithm is used for superpixel generation. 2) A probabilistic fusion method is designed to derive node potential in the superpixel-based graphical model to address the problem of insufficient training samples at the superpixel level. 3) A boundary penalty between the superpixels is introduced in the edge potential evaluation. Experiments on three real data sets were conducted. The results show that the proposed method performs better than the related state-of-the-art methods tested. Numéro de notice : A2015-770 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2015.2423688 Date de publication en ligne : 08/06/2015 En ligne : https://doi.org/10.1109/TGRS.2015.2423688 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=78826
in IEEE Transactions on geoscience and remote sensing > vol 53 n° 11 (November 2015) . - pp 5861 - 5871[article]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 065-2015111 SL Revue Centre de documentation Revues en salle Disponible
Titre : Minimum volume simplex analysis: A fast algorithm for linear hyperspectral unmixing Type de document : Article/Communication Auteurs : Jun Li, Auteur ; Alexander Agathos, Auteur ; Daniela Zaharie, Auteur ; et al., Auteur Année de publication : 2015 Article en page(s) : pp 5067 - 5082 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] algorithme du simplexe
[Termes IGN] analyse linéaire des mélanges spectraux
[Termes IGN] décomposition du pixel
[Termes IGN] image hyperspectrale
[Termes IGN] implémentation (informatique)Résumé : (Auteur) Linear spectral unmixing aims at estimating the number of pure spectral substances, also called endmembers, their spectral signatures, and their abundance fractions in remotely sensed hyperspectral images. This paper describes a method for unsupervised hyperspectral unmixing called minimum volume simplex analysis (MVSA) and introduces a new computationally efficient implementation. MVSA approaches hyperspectral unmixing by fitting a minimum volume simplex to the hyperspectral data, constraining the abundance fractions to belong to the probability simplex. The resulting optimization problem, which is computationally complex, is solved in this paper by implementing a sequence of quadratically constrained subproblems using the interior point method, which is particularly effective from the computational viewpoint. The proposed implementation (available online: www.lx.it.pt/%7ejun/DemoMVSA.zip) is shown to exhibit state-of-the-art performance not only in terms of unmixing accuracy, particularly in nonpure pixel scenarios, but also in terms of computational performance. Our experiments have been conducted using both synthetic and real data sets. An important assumption of MVSA is that pure pixels may not be present in the hyperspectral data, thus addressing a common situation in real scenarios which are often dominated by highly mixed pixels. In our experiments, we observe that MVSA yields competitive performance when compared with other available algorithms that work under the nonpure pixel regime. Our results also demonstrate that MVSA is well suited to problems involving a high number of endmembers (i.e., complex scenes) and also for problems involving a high number of pixels (i.e., large scenes). Numéro de notice : A2015-528 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2015.2417162 Date de publication en ligne : 21/04/2015 En ligne : https://doi.org/10.1109/TGRS.2015.2417162 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=77556
in IEEE Transactions on geoscience and remote sensing > vol 53 n° 9 (September 2015) . - pp 5067 - 5082[article]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 065-2015091 SL Revue Centre de documentation Revues en salle Disponible
Titre : Adaptive MAP sub-pixel mapping model based on regularization curve for multiple shifted hyperspectral imagery Type de document : Article/Communication Auteurs : Yanfei Zhong, Auteur ; Yunyun Wu, Auteur ; Liangpei Zhang, Auteur ; Xiong Xu, Auteur Année de publication : 2014 Article en page(s) : pp 134 - 148 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] décomposition du pixel
[Termes IGN] image hyperspectraleRésumé : (Auteur) Sub-pixel mapping is a promising technique for producing a spatial distribution map of different categories at the sub-pixel scale by using the fractional abundance image as the input. The traditional sub-pixel mapping algorithms based on single images often have uncertainty due to insufficient contraint of the sub-pixel land-cover patterns within the low-resolution pixels. To improve the sub-pixel mapping accuracy, sub-pixel mapping algorithms based on auxiliary datasets, e.g., multiple shifted images, have been designed, and the maximum a posteriori (MAP) model has been successfully applied to solve the ill-posed sub-pixel mapping problem. However, the regularization parameter is difficult to set properly. In this paper, to avoid a manually defined regularization parameter, and to utilize the complementary information, a novel adaptive MAP sub-pixel mapping model based on regularization curve, namely AMMSSM, is proposed for hyperspectral remote sensing imagery. In AMMSSM, a regularization curve which includes an L-curve or U-curve method is utilized to adaptively select the regularization parameter. In addition, to take the influence of the sub-pixel spatial information into account, three class determination strategies based on a spatial attraction model, a class determination strategy, and a winner-takes-all method are utilized to obtain the final sub-pixel mapping result. The proposed method was applied to three synthetic images and one real hyperspectral image. The experimental results confirm that the AMMSSM algorithm is an effective option for sub-pixel mapping, compared with the traditional sub-pixel mapping method based on a single image and the latest sub-pixel mapping methods based on multiple shifted images. Numéro de notice : A2014-376 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2014.06.019 En ligne : https://doi.org/10.1016/j.isprsjprs.2014.06.019 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=73815
in ISPRS Journal of photogrammetry and remote sensing > vol 96 (October 2014) . - pp 134 - 148[article]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 081-2014101 RAB Revue Centre de documentation En réserve L003 Disponible PermalinkPermalinkPermalinkPermalink
