Linear Hyperspectral Unmixing Using L0-norm Approximations and Nonnegative Matrix Factorization

Spectral unmixing (SU) is a technique to characterize mixed pixels of the hyperspectral images measured by remote sensors. Most of the existing spectral unmixing algorithms are developed using the linear mixing models. Since the number of endmembers/materials present at each mixed pixel is normally scanty compared with the number of total endmembers (the dimension of spectral library), the problem becomes sparse. This thesis introduces sparse hyperspectral unmixing methods for the linear mixing model through two different scenarios. In the first scenario, the library of spectral signatures is assumed to be known and the main problem is to find the minimum number of endmembers under a reasonable small approximation error. Mathematically, the corresponding problem is called the $\ell_0$-norm problem which is NP-hard problem. Our main study for the first part of thesis is to find more accurate and reliable approximations of $\ell_0$-norm term and propose sparse unmixing methods via such approximations. The resulting methods are shown considerable improvements to reconstruct the fractional abundances of endmembers in comparison with state-of-the-art methods such as having lower reconstruction errors. In the second part of the thesis, the first scenario (i.e., dictionary-aided semiblind unmixing scheme) will be generalized as the blind unmixing scenario that the library of spectral signatures is also estimated. We apply the nonnegative matrix factorization (NMF) method for proposing new unmixing methods due to its noticeable supports such as considering the nonnegativity constraints of two decomposed matrices. Furthermore, we introduce new cost functions through some statistical and physical features of spectral signatures of materials (SSoM) and hyperspectral pixels such as the collaborative property of hyperspectral pixels and the mathematical representation of the concentrated energy of SSoM for the first few subbands. Finally, we introduce sparse unmixing methods for the blind scenario and evaluate the efficiency of the proposed methods via simulations over synthetic and real hyperspectral data sets. The results illustrate considerable enhancements to estimate the spectral library of materials and their fractional abundances such as smaller values of spectral angle distance (SAD) and abundance angle distance (AAD) as well.

Photoelectron Imaging Following 2 þ 1 Multiphoton Excitation of HBr

The photodissociation and photoionization dynamics of HBr via low-n Rydberg and ion-pair states was studied by using 2 + 1 REMPI spectroscopy and velocity map imaging of photoelectrons. Two-photon excitation at about 9.4–10 eV was used to prepare rotationally selected excited states. Following absorption of the third photon the unperturbed F 1Δ(2) and i 3Δ(2) states ionize directly into the ground vibrational state of the molecular ion according to the Franck–Condon principle and upon preservation of the ion core. In case of the V 1Σ+(0+) ion-pair state and the perturbed E 1Σ+(0+), g 3Σ−(0+), and H 1Σ+(0+) Rydberg states the absorption of the third photon additionally results in a long vibrational progression of HBr+ in the X 2Π state as well as formation of electronically excited atomic photofragments. The vibrational excitation of the molecular ion is explained by autoionization of repulsive superexcited states into the ground state of the molecular ion. In contrast to HCl, the perturbed Rydberg states of HBr show strong participation of the direct ionization process, with ionic core preservation.