Processing of Hyperspectral Data using Wavelet Transform

Remote sensor technology has encouraged series of research work in the area of signal and image processing. This is because the application of remote sensor has made it possible to obtain different types of signals and images from different places all over the world. In most cases, data obtained from hyperspectral images are found to be too voluminous and noisy. This, to a certain extent affects the accuracy of the result obtained when such signals or images are further processed for some applications. Previous research works have not sufficiently addressed this fundamental problem. Therefore, this research work is out to make use of Wavelet Transform for  processing signals obtained from hyperspectral images with a view to denoise and reduce the data dimensionality without losing part of its content. Having undergone the process of denoising, the quality of the image or signal is drastically improved in terms of its clarity and size. This produces a better result when such signal is used for some applications. The system was implemented using MatLab wavelet tool. Hence, the result obtained is found to be better than the previous ones. The result also produced an hyperspectral spectrum/signal that has been thoroughly denoised and dimensionally reduced to an acceptable size within a very short computational time

A Non-Local Structure Tensor Based Approach for Multicomponent Image Recovery Problems

Non-Local Total Variation (NLTV) has emerged as a useful tool in variational methods for image recovery problems. In this paper, we extend the NLTV-based regularization to multicomponent images by taking advantage of the Structure Tensor (ST) resulting from the gradient of a multicomponent image. The proposed approach allows us to penalize the non-local variations, jointly for the different components, through various $\ell_{1,p}$ matrix norms with $p \ge 1$. To facilitate the choice of the hyper-parameters, we adopt a constrained convex optimization approach in which we minimize the data fidelity term subject to a constraint involving the ST-NLTV regularization. The resulting convex optimization problem is solved with a novel epigraphical projection method. This formulation can be efficiently implemented thanks to the flexibility offered by recent primal-dual proximal algorithms. Experiments are carried out for multispectral and hyperspectral images. The results demonstrate the interest of introducing a non-local structure tensor regularization and show that the proposed approach leads to significant improvements in terms of convergence speed over current state-of-the-art methods

Hyperspectral Image Unmixing Incorporating Adjacency Information

While the spectral information contained in hyperspectral images is rich, the spatial resolution of such images is in many cases very low. Many pixel spectra are mixtures of pure materials’ spectra and therefore need to be decomposed into their constituents. This work investigates new decomposition methods taking into account spectral, spatial and global 3D adjacency information. This allows for faster and more accurate decomposition results