Motivation. Multi and Hyperspectral remote sensing imagery provide valuable insights regarding the composition of a scene and significantly facilitate tasks like object and material recognition, spectral unmixing and region clustering, among others [1], [2]. However, current remote sensing imaging architectures are unable to concurrently acquire high spatial and spectral resolution imagery, due...

Given a set of mixed spectral vectors, spectral mixture analysis (or spectral unmixing) aims at estimating the number of reference materials, also called endmembers, their spectral signatures, and their fractional abundances. A semi-supervised approach to deal with the linear spectral unmixing problem consists in assuming that the observed spectral vectors are linear combinations of a small num...

Spectral unmixing represents both an application per se and a pre-processing step for several applications involving data acquired by imaging spectrometers. However, there is still lack of publicly available reference sets suitable the validation comparison different spectral methods. In this paper, we introduce DLR HyperSpectral Unmixing (DLR HySU) benchmark dataset, over German Aerospace Cent...

Hyperspectral imagery (HSI) unmixing is a process that decomposes pixel spectra into a collection of constituent spectra (endmembers) and their correspondent abundance fractions. Without knowing any knowledge of HSI data, the unmixing problem is transformed into a blind source separation (BSS) problem. Several methods have been proposed to deal with the problem, like independent component analy...

In hyperspectral imagery one pixel typically consists of a mixture of the re ectance spectra of several materials, where the mixture coe cients correspond to the abundances of the constituting materials. We assume linear combinations of re ectance spectra with some additive normal sensor noise and derive a probabilistic MAP framework for analyzing hyperspectral data. As the material reectance c...

Data fusion can significantly increase accuracy of automated classification in remote sensing applications by combining data from different types of sensors. Particularly for hyperspectral imagery (HSI), complementing the hyperspectral data with topographical information in the form of a Digital Surface Model (DSM) generated by LiDAR data is promising to address problems with artifacts or disto...

Hyperspectral image compression has received considerable interest in recent years due to the enormous data volumes collected by imaging spectrometers for Earth Observation. JPEG2000 is an important technique for data compression, which has been successfully used in the context of hyperspectral image compression, either in lossless and lossy fashion. Due to the increasing spatial, spectral, and...

Remote sensing image analysis can be carried out at the per-pixel (hard) and sub-pixel (soft) scales. The former refers to the purity of image pixels, while the latter refers to the mixed spectra resulting from all objects composing of the image pixels. The spectral unmixing methods have been developed to decompose mixed spectra. Data-driven unmixing algorithms utilize the reference data called...

Spectral unmixing is an important task for remotely sensed hyperspectral data exploitation. The spectral signatures collected in natural environments are invariably a mixture of the pure signatures of the various materials found within the spatial extent of the ground instantaneous field view of the imaging instrument. Spectral unmixing aims at inferring such pure spectral signatures, called en...

Hyperspectral imagery (HSI) of the ocean-land interface, known as the littoral zone (LZ) can provide a valuable source of information for identification of underwater objects and materials, determination of water depth, and retrieval of water composition. The first step in the analysis is removal of atmospheric effects, resulting in surface reflectance spectra. The atmospheric removal is accomp...