Recent progress in canopy bidirectional reflectance distribution function (BRDF) model inversions has allowed accurate estimates of vegetation biophysical characteristics from remotely sensed multi-angle optical data. Since most current BRDF inversion methods utilize one-dimensional (1-D) models, surface homogeneity within an image pixel is implied. The Advanced Very High Resolution Radiometer ...

This paper introduces an automated spectral unmixing approach. This approach is based on multiple endmember spectral mixture analysis (MESMA) where the mixture model is iteratively improved using residual analysis and knowledge-based feature identification. A combined criterion for model selection and criteria to detect errors in the mixture model itself are also discussed, as well as methods t...

Coverage rates of vegetation and exposed bedrock are two key indicators of karst rocky desertification. In this study, the abundances of vegetation and exposed rock were retrieved from a hyperspectral Hyperion image using linear spectral unmixing method. The results were verified using the spectral indices of karst rocky desertification (KRDSI) and an integrated LAI spectral index: modified chl...

Land management practices affect the long term sustainability of agricultural soils. Growing Forward, Agriculture and Agri-Food Canada’s (AAFC) agricultural policy framework, has identified promoting environmentally responsible agriculture as one of the department’s priorities. Land management information requirements have become increasingly important to a number of programs and policies suppo...

BACKGROUND Recently, sparse unmixing has been successfully applied to spectral mixture analysis of remotely sensed hyperspectral images. Based on the assumption that the observed image signatures can be expressed in the form of linear combinations of a number of pure spectral signatures known in advance, unmixing of each mixed pixel in the scene is to find an optimal subset of signatures in a v...

Spectral unmixing is an important issue to analyze remotely sensed hyperspectral data. This involves the decomposition of each mixed pixel into its pure endmember spectra, and the estimation of the abundance value for each endmember. Although linear mixture models are often considered because of their simplicity, there are many situations in which they can be advantageously replaced by nonlinea...

In this article, the hyperspectral unmixing problem is solved with the nonnegative matrix factorization (NMF) algorithm. The regularized criterion is minimized with a hierarchical alternating least squares (HALS) scheme. Under the HALS framework, four constraints are introduced to improve the unmixing accuracy, including the sum-to-unity constraint, the constraints for minimum spectral dispersi...