Nonlinear Spectral Mixture Analysis for Hyperspectral Imagery in an Unknown Environment

Raksuntorn, Nareenart; Du, Qian

doi:10.1109/lgrs.2010.2049334

Cited by 59 publications

(32 citation statements)

References 22 publications

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“…A number of remote sensing studies have estimated FVC in multispectral or hyperspectral images using a Linear Spectral Unmixing (LSU) approach with two or more endmembers [2][3][4][5][7][8][9][10][11][12]. Non-linear unmixing approaches also exist (e.g., [13][14][15]), but the linear approach is used most often due to its simplicity, rationality, and feasibility in practical applications [16]. The number of spectral bands in an image limits the number of endmembers that can be used for unmixing [17], so for images with relatively few spectral bands, a common approach is to assume that FVC can be estimated by the linear combination of two endmembers: bare soil and 100% green vegetation cover [3,6,7,12,18].…”

Section: Introductionmentioning

confidence: 99%

Remote Sensing of Fractional Green Vegetation Cover Using Spatially-Interpolated Endmembers

2012

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Abstract:Fractional green vegetation cover (FVC) is a useful parameter for many environmental and climate-related applications. A common approach for estimating FVC involves the linear unmixing of two spectral endmembers in a remote sensing image; bare soil and green vegetation. The spectral properties of these two endmembers are typically determined based on field measurements, estimated using additional data sources (e.g., soil databases or land cover maps), or extracted directly from the imagery. Most FVC estimation approaches do not consider that the spectral properties of endmembers may vary across space. However, due to local differences in climate, soil type, vegetation species, etc., the spectral characteristics of soil and green vegetation may exhibit positive spatial autocorrelation. When this is the case, it may be useful to take these local variations into account for estimating FVC. In this study, spatial interpolation (Inverse Distance Weighting and Ordinary Kriging) was used to predict variations in the spectral characteristics of bare soil and green vegetation across space. When the spatially-interpolated values were used in place of scene-invariant endmember values to estimate FVC in an Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) image, the accuracy of FVC estimates increased, providing evidence that it may be useful to consider the effects of spatial autocorrelation for spectral mixture analysis.

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Section: Introductionmentioning

confidence: 99%

Remote Sensing of Fractional Green Vegetation Cover Using Spatially-Interpolated Endmembers

2012

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“…For the effective analysis of precise spectral information in hyperspectral images, spectral unmixing or spectral mixture analysis, has been developed for its use in various applications to hyperspectral images (Heinz and Chang, 2001;Franke et al, 2009;Raksuntorn and Du, 2010). Spectral unmixing is the procedure by which the measured spectrum of a mixed pixel is decomposed into a collection of constituent spectra, or endmembers, and a set of corresponding fractions, or abundances, that indicate the proportions of each endmember present in the pixel (Keshava, 2003).…”

Section: Investigated Spectral Bandmentioning

confidence: 99%

Automatic Cross-calibration of Multispectral Imagery with Airborne Hyperspectral Imagery Using Spectral Mixture Analysis

Kim¹,

Choi²,

Anjin³

et al. 2015

Journal of the Korean Society of Surveying, Geodesy, Photogramm

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The analysis of remote sensing data depends on sensor specifications that provide accurate and consistent measurements. However, it is not easy to establish confidence and consistency in data that are analyzed by different sensors using various radiometric scales. For this reason, the cross-calibration method is used to calibrate remote sensing data with reference image data. In this study, we used an airborne hyperspectral image in order to calibrate a multispectral image. We presented an automatic cross-calibration method to calibrate a multispectral image using hyperspectral data and spectral mixture analysis. The spectral characteristics of the multispectral image were adjusted by linear regression analysis. Optimal endmember sets between two images were estimated by spectral mixture analysis for the linear regression analysis, and bands of hyperspectral image were aggregated based on the spectral response function of the two images. The results were evaluated by comparing the Root Mean Square Error (RMSE), the Spectral Angle Mapper (SAM), and average percentage differences. The results of this study showed that the proposed method corrected the spectral information in the multispectral data by using hyperspectral data, and its performance was similar to the manual cross-calibration. The proposed method demonstrated the possibility of automatic cross-calibration based on spectral mixture analysis. This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http:// creativecommons.org/licenses/by-nc/3.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

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“…Typical evolutions of the abundance vector α and its counterpart u are shown in Figure 1. Our approach was compared with the fully constrained least square method (FCLS) [10], the extended endmember matrix method (ExtM) [2], and our previously proposed K-Hype method [5]. The root mean square error of the estimated abundances was used to compare these algorithms.…”

Section: Experiments On Synthetic Datamentioning

confidence: 99%

“…For instance, bilinear models were considered to handle complex scenarios such as multilayered scenes [2,3], by introducing additional interaction terms in the linear model. An unmixing algorithm based on a manifold learning process was investigated in [4], under the assumption that hyperspectral data may be embedded into a low-dimensional manifold.…”

Section: Introductionmentioning

confidence: 99%

Estimating abundance fractions of materials in hyperspectral images by fitting a post-nonlinear mixing model

Chen

Richard

Honeiné

2013

2013 5th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS)

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Within the area of hyperspectral data processing, nonlinear unmixing techniques have emerged as promising alternatives for overcoming the limitations of linear methods. In this paper, we consider the class of post-nonlinear mixing models of the partially linear form. More precisely, these composite models consist of a linear mixing part and a nonlinear fluctuation term defined in a reproducing kernel Hilbert space, both terms being parameterized by the endmember spectral signatures and their respective abundances. These models consider that the reproducing kernel may also depend advantageously on the fractional abundances. An iterative algorithm is then derived to jointly estimate the fractional abundances and to infer the nonlinear functional term.Index Terms-Nonlinear unmixing, post-nonlinear mixing model, kernel methods, hyperspectral data processing

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Nonlinear Spectral Mixture Analysis for Hyperspectral Imagery in an Unknown Environment

Cited by 59 publications

References 22 publications

Remote Sensing of Fractional Green Vegetation Cover Using Spatially-Interpolated Endmembers

Remote Sensing of Fractional Green Vegetation Cover Using Spatially-Interpolated Endmembers

Automatic Cross-calibration of Multispectral Imagery with Airborne Hyperspectral Imagery Using Spectral Mixture Analysis

Estimating abundance fractions of materials in hyperspectral images by fitting a post-nonlinear mixing model

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