Hyperspectral unmixing (HU) is a very useful and increasingly popular preprocessing step for a wide range of hyperspectral applications. However, the HU research has been constrained a lot by three factors: (a) the number of hyperspectral images (especially the ones with ground truths) are very limited; (b) the ground truths of most hyperspectral images are not shared on the web, which may caus...

This letter proposes a fast yet efficient method to solve the hyperspectral unmixing problem in challenging unsupervised context, i.e., when endmember spectral signatures are unknown. First, coarse approximation of image is computed by spatially averaging neighboring pixels, which significantly reduces amount pixels be handled. reduced set unmixed derive solutions problem, estimates and corresp...

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Current shadow-aware hyperspectral unmixing methods often suffer from noisy abundance maps and inaccurate estimation of shadowed pixels, as these are characterized by low reflectance values signal-to-noise ratio. In order to achieve a shadow-insensitive estimation, in this article we propose novel spatial-spectral mixing model (S3AM). The approach models shadows considering diffuse solar illumi...

In hyperspectral imagery, differences in ground surface structures cause a large variation the optical scattering sunlit and (partly) shadowed pixels. The complexity of scene demands general spectral mixture model that can adapt to different scenarios surface. this article, we propose physics-based model, i.e., extended shadow multilinear mixing (ESMLM) accounts for typical presence shadows non...

Delineation of aquatic plants and estimation of its surface extent are crucial to the efficient control of its proliferation, and this information can be derived accurately with fine resolution remote sensing products. However, small swath and low observation frequency associated with them may be prohibitive for application to large water bodies with rapid proliferation and dynamic floating aqu...

The spectral features of hyperspectral images, such as the spectrum at each pixel or the abundance maps of the endmembers, describe the material attributes of the structures. However, the spectrum on each pixel, which usually has hundreds of spectral bands, is redundant for classification task. In this paper, we firstly use spectral unmixing to reduce the dimensionality of the hyperspectal data...

nessed the collection of measurements with significantly greater spectral breadth and resolution. It has been motivated by a desire to extract increasingly detailed information about the material properties of pixels in a scene for both civilian and military applications. While multispectral sensing has largely succeeded at classifying whole pixels, further analysis of the constituent substance...

In this paper, we present a statistical approach to spectral unmixing with unknown endmember spectra and unknown illuminant power spectrum. The method presented here is quite general in nature, being applicable to settings in which sub-pixel information is required. The method is formulated as a simultaneous process of illuminant power spectrum prediction and basis material reflectance decompos...

Spectral unmixing is a widely used technique in hyperspectral image processing and analysis. It aims to separate mixed pixels into the component materials their corresponding abundances. Early solutions spectral are performed independently on each pixel. Nowadays, investigating proper priors problem has been popular as it can significantly enhance performance. However, nontrivial handcraft powe...