A New Linear Mixture Model for Hyperspectral Image Analysis

Raksuntorn, Nareenart; Du, Qian

doi:10.1109/igarss.2008.4779332

Cited by 8 publications

(4 citation statements)

References 6 publications

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“…O Modelo Linear de Mistura Espectral (MLMLE) é amplamente utilizado na análise de imagens de sensoriamento (Shimabukuro & Ponzoni, 2019). É particularmente útil para imagens hiperespectrais devido ao fato de sua alta dimensionalidade permitir a recuperação de abundância de frações, mesmo quando o número de membros finais é grande (Raksuntorn & Du, 2008). Diante disso, os algoritmos de mistura espectral são ferramentas de análise para avaliações de gravidade de áreas queimadas por incêndio florestal (AQ), pois geram estimativas quantitativas de abundância das classes de cobertura do solo, sem a necessidade de uma calibração com dados de campo e com índices espectrais (Solans Vila & Barbosa, 2010).…”

Section: Introductionunclassified

Análise do Modelo Linear de Mistura Espectral na Avaliação de Incêndios Florestais no Parque Nacional do Araguaia, Tocantins, Brasil: Imagens EO-1/Hyperion e Landsat-7/ETM+

Silva

Pacheco

2020

Anu. Inst. Geoc.

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Section: Introductionunclassified

Análise do Modelo Linear de Mistura Espectral na Avaliação de Incêndios Florestais no Parque Nacional do Araguaia, Tocantins, Brasil: Imagens EO-1/Hyperion e Landsat-7/ETM+

Silva

Pacheco

2020

Anu. Inst. Geoc.

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“…Naturally occurring hydrocarbon (HC) substances, depending on the length of the carbon chain, occur in different forms; solid, liquid, and gas [1]. Liquid HCs found in nature consist of a complex mixture of various molecular weights; in addition nitrogen, sulfur, and oxygen exist in small quantities [2].…”

Section: Introductionmentioning

confidence: 99%

Quantification of Hydrocarbon Abundance in Soils Using Deep Learning with Dropout and Hyperspectral Data

et al. 2019

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Terrestrial hydrocarbon spills have the potential to cause significant soil degradation across large areas. Identification and remedial measures taken at an early stage are therefore important. Reflectance spectroscopy is a rapid remote sensing method that has proven capable of characterizing hydrocarbon-contaminated soils. In this paper, we develop a deep learning approach to estimate the amount of Hydrocarbon (HC) mixed with different soil samples using a three-term backpropagation algorithm with dropout. The dropout was used to avoid overfitting and reduce computational complexity. A Hyspex SWIR 384 m camera measured the reflectance of the samples obtained by mixing and homogenizing four different soil types with four different HC substances, respectively. The datasets were fed into the proposed deep learning neural network to quantify the amount of HCs in each dataset. Individual validation of all the dataset shows excellent prediction estimation of the HC content with an average mean square error of ~ 2 . 2 × 10 - 4 . The results with remote sensed data captured by an airborne system validate the approach. This demonstrates that a deep learning approach coupled with hyperspectral imaging techniques can be used for rapid identification and estimation of HCs in soils, which could be useful in estimating the quantity of HC spills at an early stage.

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“…In such a case, searching an optimal endmember set for each pixel becomes a problem since endmember combinations are too many to be tested. We have proposed new algorithms for optimal endmember set selection for a given pixel without any threshold requirement [13]. Algorithm 1 for EVLMM (referred to as EVLMM1) is based on searching the combination with fewer endmembers that produces all nonnegative abundances and the smallest MSE in pixel reconstruction, and Algorithm 2 for EVLMM (called EVLMM2) is a fast algorithm producing a suboptimal solution.…”

Section: Introductionmentioning

confidence: 99%

Fast algorithm for searching endmember set per pixel

Raksuntorn

2010

SPIE Proceedings

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Linear mixture model (LMM) is widely used in remote sensing image analysis. In the original LMM, it is assumed that each pixel is a linear combination of all the endmember signatures. For an image scene covering a large geospatial area, the number of endmembers is quite large but only a small subset of these endmembers may actually participate in the composition of a specific pixel. If all the endmembers are used in the spectral unmixing process, the result may contain additional estimation error. The Multiple Endmember Spectral Mixture Analysis (MESMA) approach has been proposed which allows the endmember subset to be varied from pixel to pixel. We have developed approaches that can automatically search for an endmember subset for each pixel from a large number of endmembers for the entire image scene, without the limitation on the number of endmembers to be included. Due to high computational complexity, in this paper, we will develop a fast searching algorithm by considering the neighborhood relationship among pixels. We will show that the resulting endmember sets can reduce pixel reconstruction error and improve the quality of estimated abundances (i.e., all the pixels have nonnegative abundances and most of pixels have sum-to-one abundances).

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A New Linear Mixture Model for Hyperspectral Image Analysis

Cited by 8 publications

References 6 publications

Análise do Modelo Linear de Mistura Espectral na Avaliação de Incêndios Florestais no Parque Nacional do Araguaia, Tocantins, Brasil: Imagens EO-1/Hyperion e Landsat-7/ETM+

Análise do Modelo Linear de Mistura Espectral na Avaliação de Incêndios Florestais no Parque Nacional do Araguaia, Tocantins, Brasil: Imagens EO-1/Hyperion e Landsat-7/ETM+

Quantification of Hydrocarbon Abundance in Soils Using Deep Learning with Dropout and Hyperspectral Data

Fast algorithm for searching endmember set per pixel

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