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Sparse Subspace Clustering in Hyperspectral Images using Incomplete Pixels

Agrupación de Subespacios Escasos en Imágenes Hiperespectrales usando Pixeles incompletos

dc.creatorBacca, Jorge Luis
dc.creatorArguello, Henry
dc.date2019-09-20
dc.date.accessioned2021-03-18T21:12:25Z
dc.date.available2021-03-18T21:12:25Z
dc.identifierhttps://revistas.itm.edu.co/index.php/tecnologicas/article/view/1205
dc.identifier10.22430/22565337.1205
dc.identifier.urihttp://test.repositoriodigital.com:8080/handle/123456789/11777
dc.descriptionSpectral image clustering is an unsupervised classification method which identifies distributions of pixels using spectral information without requiring a previous training stage. The sparse subspace clustering-based methods (SSC) assume that hyperspectral images lie in the union of multiple low-dimensional subspaces.  Using this, SSC groups spectral signatures in different subspaces, expressing each spectral signature as a sparse linear combination of all pixels, ensuring that the non-zero elements belong to the same class. Although these methods have shown good accuracy for unsupervised classification of hyperspectral images, the computational complexity becomes intractable as the number of pixels increases, i.e. when the spatial dimension of the image is large. For this reason, this paper proposes to reduce the number of pixels to be classified in the hyperspectral image, and later, the clustering results for the missing pixels are obtained by exploiting the spatial information. Specifically, this work proposes two methodologies to remove the pixels, the first one is based on spatial blue noise distribution which reduces the probability to remove cluster of neighboring pixels, and the second is a sub-sampling procedure that eliminates every two contiguous pixels, preserving the spatial structure of the scene. The performance of the proposed spectral image clustering framework is evaluated in three datasets showing that a similar accuracy is obtained when up to 50% of the pixels are removed, in addition, it is up to 7.9 times faster compared to the classification of the data sets without incomplete pixels.en-US
dc.descriptionEl agrupamiento de imágenes espectrales es un método de clasificación no supervisada que identifica las distribuciones de píxeles utilizando información espectral sin necesidad de una etapa previa de entrenamiento. Los métodos basados ​​en agrupación de subespacio escasos (SSC) suponen que las imágenes hiperespectrales viven en la unión de múltiples subespacios de baja dimensión. Basado en esto, SSC asigna firmas espectrales a diferentes subespacios, expresando cada firma espectral como una combinación lineal escasa de todos los píxeles, garantizando que los elementos que no son cero pertenecen a la misma clase. Aunque estos métodos han demostrado una buena precisión para la clasificación no supervisada de imágenes hiperespectrales, a medida que aumenta el número de píxeles, es decir, la dimensión de la imagen es grande, la complejidad computacional se vuelve intratable. Por este motivo, este documento propone reducir el número de píxeles a clasificar en la imagen hiperespectral, y posteriormente, los resultados del agrupamiento para los píxeles faltantes se obtienen explotando la información espacial. Específicamente, este trabajo propone dos metodologías para remover los píxeles, la primera se basa en una distribución espacial de ruido azul que reduce la probabilidad de que se eliminen píxeles vecinos y la segunda es un procedimiento de submuestreo que elimina cada dos píxeles contiguos, preservando la estructura espacial de la escena. El rendimiento del algoritmo de agrupamiento de imágenes espectrales propuesto se evalúa en tres conjuntos de datos mostrando que se obtiene una precisión similar cuando se elimina hasta la mitad de los pixeles, además, es hasta 7.9 veces más rápido en comparación con la clasificación de los conjuntos de datos completos.es-ES
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dc.languageeng
dc.publisherInstituto Tecnológico Metropolitano (ITM)en-US
dc.relationhttps://revistas.itm.edu.co/index.php/tecnologicas/article/view/1205/1293
dc.relationhttps://revistas.itm.edu.co/index.php/tecnologicas/article/view/1205/1441
dc.relationhttps://revistas.itm.edu.co/index.php/tecnologicas/article/view/1205/1458
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dc.rightsCopyright (c) 2019 TecnoLógicasen-US
dc.rightshttp://creativecommons.org/licenses/by-nc-sa/4.0en-US
dc.sourceTecnoLógicas; Vol. 22 No. 46 (2019); 1-14en-US
dc.sourceTecnoLógicas; Vol. 22 Núm. 46 (2019); 1-14es-ES
dc.source2256-5337
dc.source0123-7799
dc.subjectSpectral imagesen-US
dc.subjectSpectral clusteringen-US
dc.subjectSparse subspace clusteringen-US
dc.subjectSub-samplingen-US
dc.subjectimage classificationen-US
dc.subjectImágenes hiperespectraleses-ES
dc.subjectAgrupación espectrales-ES
dc.subjectAgrupación de subespacios escasoses-ES
dc.subjectSubmuestreoes-ES
dc.subjectclasificación de imágeneses-ES
dc.titleSparse Subspace Clustering in Hyperspectral Images using Incomplete Pixelsen-US
dc.titleAgrupación de Subespacios Escasos en Imágenes Hiperespectrales usando Pixeles incompletoses-ES
dc.typeinfo:eu-repo/semantics/article
dc.typeinfo:eu-repo/semantics/publishedVersion
dc.typeResearch Papersen-US
dc.typeArtículos de investigaciónes-ES


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