A new approach to multispectral and hyperspectral image analysis is presented. This method, called convex cone analysis (CCA), is based on the fact that some physical quantities such as radiance are nonnegative. The vectors formed by discrete radiance spectra are linear combinations of nonnegative components, and they lie inside a nonnegative, convex region. The object of CCA is to find the bou...

This paper considers an experimental approach for assessing algorithms used to exploit remotely sensed data. The approach employs synthetic images that are generated using physical models to make them more realistic while still providing ground truth data for quantitative evaluation. This approach complements the common approach of using real data and/or simple model-generated data. To demonstr...

Hyperspectral imaging instruments are capable of collecting hundreds of images, corresponding to different wavelength channels, for the same area on the surface of the Earth. One of the main problems in the analysis of hyperspectral data cubes is the presence of mixed pixels, which arise when the spatial resolution of the sensor is not enough to separate spectrally distinct materials. Hyperspec...

and efficient solution to the unmixing problem. independence great discrimination ability on unlike signatures, giving a robust The model has great noise robustness, a correlation rate and endmember number always low Error Ratios for all cases. components, endmember number and proportion on the mixture, providing network behaviour vs. the Signal-to-Noise Ratio, correlation rate between order to...

In this paper, we present a comparative study of several unsupervised unmixing algorithms to anomaly detection in hyperspectral images. The algorithms are called minimum volume constrained non-negative matrix factorization (MVCNMF) [1], gradient descent maximum entropy (GDME) [2] and unsupervised fully constrained least squares (UFCLS) [3] . Several variants of the above algorithms were also im...

Submerged macrophytes give important information about a lake ́s trophic state and its ecosystem. Aquatic macrophytes can therefore serve as useful indicators of water pollution along the littoral zones. The spectral signatures of various macrophyte species were investigated to determine whether species could be discriminated by remote sensing. The spectral reflectance of macrophytes collected f...

Spectral unmixing (SU) is a data processing problem in hyperspectral remote sensing. The significant challenge in the SU problem is how to identify endmembers and their weights, accurately. For estimation of signature and fractional abundance matrices in a blind problem, nonnegative matrix factorization (NMF) and its developments are used widely in the SU problem. One of the constraints which w...

This paper describes a new algorithm for feature extraction on hyperspectral images based on blind source separation (BSS) and distributed processing. I use Independent Component Analysis (ICA), a particular case of BSS, where, given a linear mixture of statistical independent sources, the goal is to recover these components by producing the unmixing matrix. In the multispectral/hyperspectral i...

Imaging spectroscopy, also known as hyperspectral imaging, is a new technique that has gained tremendous popularity in many research areas, including satellite imaging and aerial reconnaissance. In particular, NASA is continuously gathering high-dimensional image data from the surface of the earthwith hyperspectral sensors such as the Jet Propulsion Laboratory’s Airborne Visible-Infrared Imagin...