To solve the problem of the spatial correlation for adjacent areas in traditional spectral unmixing methods, we propose an area-correlated spectral unmixing method based on Bayesian nonnegative matrix factorization. In the proposed method, the spatial correlation property between two adjacent areas is expressed by a priori probability density function, and the endmembers extracted from one of t...

Spectral unmixing techniques are widely used for hyperspectral data analysis and quantification. Many novel applications have been developed from the unmixing point of view, including surface constituent identification for land use mapping, disaster assessment, geology, biological process analysis and change detection (Keshava and Mustard, 2002). All existing unmixing approaches require a previ...

We propose a powerful alternative to customary linear spectral unmixing, with a new neural model, which achieves locally linear but globally non-linear unmixing. This enables unmixing with respect to a large number of endmembers, while traditional linear unmixing is limited to a handful of endmembers.

The analysis of hyperspectral images on the basis of the spectral decomposition of their pixels through the so called spectral unmixing process, has applications in tematic map generation, target detection and unsupervised image segmentation. The critical step is the determination of the endmembers used as the references for the unmixing process. We give a comprehensive enumeration of the metho...

The goal of this research was to investigate the potential of hyperspectral Hyperion (EO-1) data to derive fractional cover of rangeland components using constrained linear spectral mixture analysis. Hyperion image data were acquired over the Antelope Creek Ranch located in southern Alberta, Canada in July 2005. These image data were first corrected for the sensor artifacts such as spatial mis-...

Spectral unmixing is a popular technique for hyperspectral data interpretation. It focuses on estimating the abundance of pure spectral signature (called as endmembers) in each observed image signature. However, the identification of the endmembers in the original hyperspectral data becomes a challenge due to the lack of pure pixels in the scenes and the difficulty in estimating the number of e...

Spectral unmixing is an important task for remotely sensed hyperspectral data exploitation. The spectral signatures collected in natural environments are invariably a mixture of the pure signatures of the various materials found within the spatial extent of the ground instantaneous field view of the imaging instrument. Spectral unmixing aims at inferring such pure spectral signatures, called en...

Spectral unmixing is a key process in identifying spectral signature of materials and quantifying their spatial distribution over an image. The linear model is expected to provide acceptable results when two assumptions are satisfied: (1) The mixing process should occur at macroscopic level and (2) Photons must interact with single material before reaching the sensor. However, these assumptions...

Hyperspectral imaging sensors exibit high spectral resolution, but normally low spatial resolution. This leads to spectral signatures of pixels originating from different object types. Such pixels are called mixed pixels. Spectral unmixing methods can be employed to estimate the fractions of reflected light from the different objects within the pixel area. However, spectral unmixing does not pr...

This paper is concerned with deblurring and spectral analysis of ground-based astronomical images of space objects. A numerical approach is provided for deblurring and sparse unmixing of ground-based hyperspectral images (HSI) of objects taken through atmospheric turbulence. Hyperspectral imaging systems capture a 3D datacube (tensor) containing: 2D spatial information, and 1D spectral informat...