[Orthogonal Vector Projection Algorithm for Spectral Unmixing].

Spectrum unmixing is an important part of hyperspectral technologies, which is essential for material quantity analysis in hyperspectral imagery. Most linear unmixing algorithms require computations of matrix multiplication and matrix inversion or matrix determination. These are difficult for programming, especially hard for realization on hardware. At the same time, the computation costs of the algorithms increase significantly as the number of endmembers grows. Here, based on the traditional algorithm Orthogonal Subspace Projection, a new method called. Orthogonal Vector Projection is prompted using orthogonal principle. It simplifies this process by avoiding matrix multiplication and inversion. It firstly computes the final orthogonal vector via Gram-Schmidt process for each endmember spectrum. And then, these orthogonal vectors are used as projection vector for the pixel signature. The unconstrained abundance can be obtained directly by projecting the signature to the projection vectors, and computing the ratio of projected vector length and orthogonal vector length. Compared to the Orthogonal Subspace Projection and Least Squares Error algorithms, this method does not need matrix inversion, which is much computation costing and hard to implement on hardware. It just completes the orthogonalization process by repeated vector operations, easy for application on both parallel computation and hardware. The reasonability of the algorithm is proved by its relationship with Orthogonal Sub-space Projection and Least Squares Error algorithms. And its computational complexity is also compared with the other two algorithms', which is the lowest one. At last, the experimental results on synthetic image and real image are also provided, giving another evidence for effectiveness of the method.

[Effective endmembers based bilinear unmixing model].

An effective endmembers based bilinear unmixing algorithm is prompted in the present paper together with an end-member subset selection algorithm as well. Firstly, the endmembers are ranked according to their distance to the mixed pixel, involving the Euclidean distance and spectral angle. And then, an effective subset of the endmembers is abstracted considering both the ranking result and the change of error. The algorithm reduces the influence of endmembers which are not component of the mixed pixel, decrease the number of endmembers involved in unmixing and improve the accuracy of abundance. The test results for simulation image prove that the algorithm would provide a lower reconstructing error. And the analysis results of actual airborne hyperspectral oil spill image further illustrate its effectiveness.

[Study of prediction models for oil thickness based on spectral curve].

Nowdays, oil spill accidents on sea occur frequently. It is a practical topic to estimate the amount of spilled oil, which is helpful for the subsequent processing and loss assessment. With the rapid development of hyperspectral remote sensing technology, estimating the oil thickness becomes possible. Firstly, a series of oil thicknesses are tested with the AvaSpec Spectrometer to get their corresponding spectral curves. And then the characteristics of the spectral curve are extracted to analyze their relationship with the oil thickness. The study shows that the oil thickness has large correlation with variables based on hyperspectral positions such as R(g), R(o), and vegetation indexes such as RDVI, TVI and Haboudane. Curve fitting, BP neural network and SVD iteration method were chosen to build the prediction models for oil thicknesses. Finally, the analysis and evaluation of each estimating model are provided.