Number Of Multispectral Bands Research Articles

Volcanic Cloud Top Height Estimation Using the Plume Elevation Model Procedure Applied to Orthorectified Landsat 8 Data. Test Case: 26 October 2013 Mt. Etna Eruption

In this study, we present a method for extracting the volcanic cloud top height (VCTH) as a plume elevation model (PEM) from orthorectified Landsat 8 data (Level 1). A similar methodology was previously applied to raw Landsat-8 data (Level 0). But level 0 data are not the standard product provided by the National Aeronautics and Space Administration (NASA)/United States Geological Survey (USGS). Level 0 data are available only on demand and consist on 14 data stripes multiplied by the number of multispectral bands. The standard product for Landsat 8 is the ortho image, available free of charge for end-users. Therefore, there is the need to adapt our previous methodology to Level 1 Landsat data. The advantages of using the standard Landsat products instead of raw data mainly include the fast -ready to use- availability of the data and free access to registered users, which is of major importance during volcanic crises. In this study, we adapt the PEM methodology to the standard Landsat-8 products, with the aim of simplifying the procedure for routine monitoring, offering an opportunity to produce PEM maps. In this study, we present the method. Our approach is applied to the 26 October 2013 Mt. Etna episodes comparing results independent VCTH measures from the spinning enhanced visible and infrared imager (SEVIRI) and the moderate resolution imaging spectroradiometer (MODIS).

Hyperspectral Super-Resolution of Locally Low Rank Images From Complementary Multisource Data.

Remote sensing hyperspectral images (HSIs) are quite often low rank, in the sense that the data belong to a low dimensional subspace/manifold. This has been recently exploited for the fusion of low spatial resolution HSI with high spatial resolution multispectral images in order to obtain super-resolution HSI. Most approaches adopt an unmixing or a matrix factorization perspective. The derived methods have led to state-of-the-art results when the spectral information lies in a low-dimensional subspace/manifold. However, if the subspace/manifold dimensionality spanned by the complete data set is large, i.e., larger than the number of multispectral bands, the performance of these methods mainly decreases because the underlying sparse regression problem is severely ill-posed. In this paper, we propose a local approach to cope with this difficulty. Fundamentally, we exploit the fact that real world HSIs are locally low rank, that is, pixels acquired from a given spatial neighborhood span a very low-dimensional subspace/manifold, i.e., lower or equal than the number of multispectral bands. Thus, we propose to partition the image into patches and solve the data fusion problem independently for each patch. This way, in each patch the subspace/manifold dimensionality is low enough, such that the problem is not ill-posed anymore. We propose two alternative approaches to define the hyperspectral super-resolution through local dictionary learning using endmember induction algorithms. We also explore two alternatives to define the local regions, using sliding windows and binary partition trees. The effectiveness of the proposed approaches is illustrated with synthetic and semi real data.