In this paper we provide a brief review of recent advances in computational methods for hyperspectral image analysis with emphasis in hybrid approaches. Hyperspectral imagery acquisition and hyperspectral analysis are growing elds. The analysis of hyperspectral images will have an increasing impact in several application areas, i.e., Earth observation, planetology, food industry, quality proces...

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...

Spectral unmixing expresses the mixed pixels existing in hyperspectral images as product of endmembers and their corresponding fractional abundances, which has been widely used imagery analysis. However, endmember spectra even for from same material an image may include variability due to influence lighting conditions inherent properties materials within different pixels. Though <italic xmlns:m...

With the increase in availability of multitemporal hyperspectral images (HSIs), HSIs change detection (CD) methods, including pixel-level and subpixel-level based have attracted great attention recent years. However, widespread presence mixed pixels may make it difficult for methods to detect subtle changes; meanwhile, less utilization spatial information also lead limitations some methods. The...

Knowledge about the spectral variability in a field or region of interest becomes important when it comes to defining a representative spectrum of a certain spatial extent, used for example as an endmember in spectral unmixing techniques. An approach is presented using high spatial resolution panchromatic data to assess the spectral variability of a hyperspectral dataset. The spatial variabilit...

Model based analysis or explicit definition/listing of all models or assumptions used in the derivation of an image fusion method allows us to understand the rationale or properties of existing methods and shows a way for a proper usage or proposal/selection of new methods ‘better’ satisfying the needs of a particular application. Most existing pan-sharpening methods are based mainly on the two...

In this paper, we introduce a set of taxonomies that hierarchically organize and specify algorithms associated with hyperspectral unmixing. Our motivation is to collectively organize and relate algorithms in order to assess the current state-of-the-art in the field and to facilitate objective comparisons between methods. The hyperspectral sensing community is populated by investigators with dis...

This paper proposes the use of spectral unmixing and sparse reconstruction methods to restore a simulated dataset for the Environmental Mapping and Analysis Program (EnMAP), the forthcoming German spaceborne hyperspectral mission. The described method independently decomposes each image element into a set of representative spectra, which come directly from the image and have previously undergon...