Spectral Image Classification From Optimal Coded-Aperture Compressive Measurements

Ramirez, Ana B.; Argüello, Henry; Arce, Gonzalo R.; Sadler, Brian M.

doi:10.1109/tgrs.2013.2272378

Cited by 28 publications

(19 citation statements)

References 26 publications

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“…From [19], we know that the orthonormal basis Ψ can be formed by the set of eigenvectors of the correlation matrix C ∈ ℝ L×L given by the following:…”

Section: Proposed Modelmentioning

confidence: 99%

“…Principal component analysis (PCA) is one of the most frequently used approaches for hyperspectral dimensionality reduction and compression in HIS because it preserves the meaningful information of the image in a few of its components. Such basis transformation was successfully performed for classification in [19]. Thus, the original sparse vector w representing the test pixel f i can be transformed into a new sparse vector w ∈ ℝ L , which represents the pixel in the orthogonal basis w. The observation pixel f i can now be expressed as:…”

Section: Proposed Modelmentioning

confidence: 99%

“…In addition, the number of spectral bands available is limited by the size of the detector, M × (N + L -1). However, this example is one of the most broadly studied compressive spectral architectures and has been used in several applications [19], [21], [22], which is why it was selected for this work.…”

Section: Introductionmentioning

confidence: 99%

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An sparsity-based approach for spectral image target detection from compressive measurements acquired by the CASSI architecture

Boada

Garcia

Ferreira

et al. 2017

I&U:EfD

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<p>Hyperspectral imaging entails data typically spanning hundreds of contiguous wavebands in a certain spectral range. Each spatial point in hyperspectral images is therefore represented by a vector whose entries correspond to the intensity on each spectral band. These images enable object and feature detection, classification, or identification based on their spectral characteristics. Novel architectures have been developed for the acquisition of compressive spectral images with just a few coded aperture focal plane array measurements. This work focuses on the development of a target detection approach in hyperspectral images directly from compressive measurements without first reconstructing the full data cube that represents the real image. Specifically, a sparsity-based target detection model that uses compressive measurement for the detection task is designed and tested using an optimization algorithm. Simulations show that it is possible to perform certain transformations to the dictionaries used in traditional target detection, in order to achieve an accurate image representation in the compressed subspace</p>

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“…From [19], we know that the orthonormal basis Ψ can be formed by the set of eigenvectors of the correlation matrix C ∈ ℝ L×L given by the following:…”

Section: Proposed Modelmentioning

confidence: 99%

Section: Proposed Modelmentioning

confidence: 99%

See 1 more Smart Citation

An sparsity-based approach for spectral image target detection from compressive measurements acquired by the CASSI architecture

Boada

Garcia

Ferreira

et al. 2017

I&U:EfD

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show abstract

“…Si al calcular (1) se obtiene el máximo valor, la señal f no puede ser muestreada. Para resolver esta dificultad, se recurre al muestreo aleatorio [37], [38], [39], [40].…”

Section: E Muestreo Compresivounclassified

Diseño de Patrones de Estratificación de Cultivos en el Departamento del Huila Colombia, Minería de Datos para una Agricultura de Precisión

Ferley

Henry²,

Gómez³

2016

Proceedings of the 14th LACCEI International Multi-Conference for Engineering, Education, and Technology: “Engineering Innovati

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-Currently in the department of Huila stratification of crops it is through the selection of soil type, height above sea level, and the characteristics of the seed. The existence of other factors related to the characteristics of the climate and the social conditions of the area is not taken into account. The use the stratification patterns in crops the department of Huila allows planning of agricultural areas and contributes to improved economic conditions in areas. Using tools like as CRISP-DM and WEKA software contribute to the design of these patterns. A second phase is in process with the use of spectral images, treated with compressive sampling techniques to help strengthen precision agriculture in this department.Keywords -Data mining, precision agriculture, spectral images. Using tools like as CRISP-DM and WEKA software contribute to the design of these patterns. A second phase is in process with the use of spectral images, treated with compressive sampling techniques to help strengthen precision agriculture in this department.

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“…The Coded Aperture Snapshot Spectral Imager (CASSI) is one example of a compressive spectral imaging (CSI) sensor that attains compressive measurements. CASSI has motivated research work in areas such as compressive spectral classification [10], [11], compressive tomography [12], compressive holography [13], X-ray compressive imaging [14], compressive fluorescence microscopy [15], and compressive Raman spectroscopy [16], [17]. The projections measured in CASSI are given by y = Hf f f , where H is an N (N + L − 1) × (N · N · L) matrix whose structure is determined by the coded aperture entries and the dispersive element effect.…”

Section: Introductionmentioning

confidence: 99%

Coded aperture compressive spectral imaging

Fuentes

Arce

2013

Symposium of Signals, Images and Artificial Vision - 2013: STSIVA - 2013

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Spectral Image Classification From Optimal Coded-Aperture Compressive Measurements

Cited by 28 publications

References 26 publications

An sparsity-based approach for spectral image target detection from compressive measurements acquired by the CASSI architecture

An sparsity-based approach for spectral image target detection from compressive measurements acquired by the CASSI architecture

Diseño de Patrones de Estratificación de Cultivos en el Departamento del Huila Colombia, Minería de Datos para una Agricultura de Precisión

Coded aperture compressive spectral imaging

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