Linear mixing and the estimation of ground cover proportions

Settle, J. J.; Drake, Nicholas

doi:10.1080/01431169308904402

Cited by 815 publications

(392 citation statements)

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“…The linear model is a special case of a nonlinear model that ignores multiple scattering [29]. In the linear model, it is assumed that the reflectance of a pixel is a linear combination of the reflectance of each endmember [30]. The weight of the reflectance of the feature type is determined by the ratio of each type to the area of the pixel:…”

Section: The Pixel Unmixing Methodsmentioning

confidence: 99%

The Retrieval of 30-m Resolution LAI from Landsat Data by Combining MODIS Products

et al. 2018

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Leaf area index (LAI) is a critical vegetation structural parameter in biogeochemical and biophysical ecosystems. High-resolution LAI products play an essential role in regional studies. Empirical methods, which normally use field measurements as their training samples and have been identified as the most commonly used approaches to retrieve structural parameters of vegetation from high-resolution remote-sensing data, are limited by the quality of training samples. Few efforts have been made to generate training samples from existing global LAI products. In this study, two methods (a homogeneous and pure pixel filter method (method A) and a pixel unmixing method (method B)) were developed to extract training samples from moderate-resolution imaging spectroradiometer (MODIS) surface reflectance and LAI products, and a support vector regression (SVR) algorithm trained by the samples was used to retrieve the high-resolution LAI from Landsat data at Baoding, situated in the Hebei Province in China, and Des Moines, situated in Iowa, United States. For the homogeneous and pure pixel filter method, two different sets of training samples were designed. One was composed of upscaled Landsat reflectance at the 500-m resolution and MODIS LAI products (dataset A1); the other was composed of MODIS reflectance and LAI products (dataset A2). With them, two inversion models were developed using SVR. For the pixel unmixing method, the training samples (dataset B) were extracted from unmixed MODIS surface reflectance and LAI products at 30-m resolution, and the third inversion model was obtained with them. LAI inversion results showed that good agreement with field measurements was achieved using these three inversion models. The R 2 (coefficient of determination) value and the root mean square error (RMSE) value were computed to assess the results. For all tests, the R 2 values are higher than 0.74 and RMSE values are less than 0.73. These tests showed that three models for the two methods combined with MODIS products can retrieve 30-m resolution LAI from Landsat data. The results of the pixel unmixing method was slightly better than that of the homogeneous and pure pixel filter method.

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Section: The Pixel Unmixing Methodsmentioning

confidence: 99%

The Retrieval of 30-m Resolution LAI from Landsat Data by Combining MODIS Products

et al. 2018

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“…The SLMM (1) is widely used in multi-spectral and hyper-spectral remote sensing, (Adams et al, 1993;Settle and Drake, 1993;Du et al, 2006;Du and Kopriva, 2008), where 3D image cube contains co-registered spectral images of the same scene. Within this application field, N represents the number of spectral bands; Simulation example in section 3.1 based on the computational model of the RGB image also demonstrates that condition number of the mixing matrix is increasing from 11.7 to 117 when angle between two spectral vectors decreases from 10 degrees to 1 degree.…”

Section: Slmm and Multi-spectral Imagingmentioning

confidence: 99%

Unsupervised decomposition of low-intensity low-dimensional multi-spectral fluorescent images for tumour demarcation

Kopriva

Peršin

2009

Medical Image Analysis

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“…where <.> denotes an average taken over all the cells in the single-stained controls (6). In a flow cytometry experiment the most straightforward way to find the matrix M, which represents the normalized spectral signatures of every used label (and subsequently to estimate a spillover matrix S), is by using a robust statistic (such as the median) to obtain average measurement values across all the detectors for every single-color control.…”

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confidence: 99%

Just compensation?

Rajwa¹

2011

Cytometry

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Linear mixing and the estimation of ground cover proportions

Cited by 815 publications

References 5 publications

The Retrieval of 30-m Resolution LAI from Landsat Data by Combining MODIS Products

The Retrieval of 30-m Resolution LAI from Landsat Data by Combining MODIS Products

Unsupervised decomposition of low-intensity low-dimensional multi-spectral fluorescent images for tumour demarcation

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