Linear unmixing decomposes a hyperspectral image into a collection of reflectance spectra of the materials present in the scene, called endmember signatures, and the corresponding abundance fractions at each pixel in a spatial area of interest. This paper introduces a new unmixing method, called Dependent Component Analysis (DECA), which overcomes the limitations of unmixing methods based on In...

This paper presents a nonlinear mixing model for joint hyperspectral image unmixing and nonlinearity detection. The proposed model assumes that the pixel reflectances are linear combinations of known pure spectral components corrupted by an additional nonlinear term, affecting the end members and contaminated by an additive Gaussian noise. A Markov random field is considered for nonlinearity de...

Spectral unmixing decomposes an hyperspectral image into a collection of reflectance spectra of the macroscopic materials present in the scene, called endmembers, and the corresponding abundance fractions of these constituents. The purpose of this paper is to compare the performance of several algorithms that process unsupervised endmember extraction from hyperspectral images in the visible and...

0018-9251/99/$10.00 @ 1999 IEEE Remotely sensed images have been used in a broad range of applications ranging from geology, agriculture, and global change detection to defense and law enforcement [ 11. They are generally acquired by multiple-band sensors operated from either a spaceborne or an airborne platform. Examples include satellite multispectral sensors such as Landsat 7-band Thematic M...

A modified multiple endmember spectral mixture analysis (MMESMA) approach is proposed for high-spatial-resolution hyperspectral imagery in the application of impervious surface mapping. Different from the original MESMA that usually selects one endmember spectral signature for each land-cover class, the proposed MMESMA allows the selection of multiple endmember signatures for each land-cover cl...

satellite imagery was used as a rapid and spatially explicit method to delineate crop residue cover and to estimate the use and intensity of conservation tillage. the potential of multispectral high-spatial resolution of worldview-2 local data was evaluated using 11 satellite spectral indices and linear spectral unmixing analysis (lsua). experimental plots were examined; residue cover was measu...

Due to the limitations of hyperspectral imaging systems, imagery (HSI) often suffers from poor spatial resolution, thus hampering many applications imagery. Hyperspectral super-resolution refers fusing HSI and MSI generate an image with both high spectral resolutions. Recently, several new methods have been proposed solve this fusion problem, most these assume that prior information Point Sprea...

Method for unmixing, category decomposition of the mixed pixel (MIXEL) of remote sensing satellite imagery data taking into account the effect due to Bi-Directional Reflectance Distribution Function: BRDF is proposed. Although there is not so small BRDF effect on estimation mixing ratios, conventional unmixing methods do not take into account the effect. Through experiments, the effect is clari...

Remote sensing image analysis can be carried out at the per-pixel (hard) and sub-pixel (soft) scales. The former refers to the purity of image pixels, while the latter refers to the mixed spectra resulting from all objects composing of the image pixels. The spectral unmixing methods have been developed to decompose mixed spectra. Data-driven unmixing algorithms utilize the reference data called...

Early detection of oral cancer and its curable precursors can improve patient survival and quality of life. Hyperspectral imaging (HSI) holds the potential for noninvasive early detection of oral cancer. The quantification of tissue chromophores by spectral unmixing of hyperspectral images could provide insights for evaluating cancer progression. In this study, non-negative matrix factorization...