Joint Bayesian endmember extraction and linear unmixing for hyperspectral imagery

This paper studies a fully Bayesian algorithm for endmember extraction and abundance estimation for hyperspectral imagery. Each pixel of the hyperspectral image is decomposed as a linear combination of pure endmember spectra following the linear mixing model. The estimation of the unknown endmember spectra is conducted in a unified manner by generating the posterior distribution of abundances and endmember parameters under a hierarchical Bayesian model. This model assumes conjugate prior distributions for these parameters, accounts for non-negativity and full-additivity constraints, and exploits the fact that the endmember proportions lie on a lower dimensional simplex. A Gibbs sampler is proposed to overcome the complexity of evaluating the resulting posterior distribution. This sampler generates samples distributed according to the posterior distribution and estimates the unknown parameters using these generated samples. The accuracy of the joint Bayesian estimator is illustrated by simulations conducted on synthetic and real AVIRIS images

Using the Minnaert-k parameter derived from CHRIS/PROBA data for forest heterogeneity mapping

CHRIS/PROBA is capable of sampling reflected radiation at five viewing angles over the visible and near-infrared regions of the solar spectrum with a relatively high spatial resolution (~17m). We exploited both the spectral and angular domain of CHRIS data in order to map the surface heterogeneity of an Alpine coniferous forest during winter. In the spectral domain, linear spectral unmixing of the nadir image resulted in a canopy cover map. In the angular domain, pixelwise inversion of the Rahman–Pinty–Verstraete (RPV) model at a single wavelength at the red edge (722 nm) yielded a map of the Minnaert-k parameter that provided information on surface heterogeneity at subpixel scale. Merging both maps resulted in a forest cover heterogeneity map, which contains more detailed information on canopy heterogeneity at the CHRIS subpixel scale than can be obtained from a single-source data set

Pixel Purity Index Algorithm and N-Dimensional Visualization For ETM+ Image Analysis: A Case of District Vehari

The hyperspectral image analysis technique one of the most advanced remote sensing tools has been used as a possible means of identifying from a single pixel or in the field of view of the sensor An important problem in hyperspectral image processing is to decompose the mixed pixels into the information that contribute to the pixel endmember and a set of corresponding fractions of the spectral signature in the pixel abundances and this problem is known as un-mixing The effectiveness of the hyperspectral image analysis technique used in this study lies in their ability to compare a pixel spectrum with the spectra of known pure vegetation extracted from the spectral endmember selection procedures including the reflectance calibration of Landsat ETM image using ENVI software minimum noise fraction MNF pixel purity index PPI and n-dimensional visualization The Endmember extraction is one of the most fundamental and crucial tasks in hyperspectral data exploitation an ultimate goal of an endmember extraction algorithm is to find the purest form of spectrally distinct resource information of a scene The endmember extraction tendency to the type of endmembers being derived and the number of endmembers estimated by an algorithm with respect to the number of spectral bands and the number of pixels being processed also the required input data and the kind of noise if any in the signal model surveying done Results of the present study using the hyperspectral image analysis technique ascertain that Landsat ETM data can be used to generate valuable vegetative information for the District Vehari Punjab Province Pakista

A spectral dissimilarity constrained nonnegative matrix factorization based cancer screening algorithm from hyperspectral fluorescence images

Bioluminescence from living body can help screen cancers without penetrating the inside of living body. Hyperspectral imaging technique is a novel way to obtain physical meaningful signatures, providing very fine spectral resolution, that can be very used in distinguishing different kinds of materials, and have been widely used in remote sensing field. Fluorescence imaging has proved effective in monitoring probable cancer cells. Recent work has made great progress on the hyperspectral fluorescence imaging techniques, which makes the elaborate spectral observation of cancer areas possible. So how to propose the proper hyperspectral image processing methods to handle the hyperspectral medical images is of practical importance. Cancer cells would be distinguishable with normal ones when the living body is injected with fluorescence, which helps organs inside the living body emit lights, and then the signals can be catched by the passive imaging sensor. Spectral unmixing technique in hyperspectral remote sensing has been introduced to detect the probable cancer areas. However, since the cancer areas are small and the normal areas and the cancer ares may not pure pixels so that the predefined endmembers would not available. In this case, the classic blind signals separation methods are applicable. Considering the spectral dissimilarity between cancer and normal cells, a novel spectral dissimilarity constrained based NMF method is proposed in this paper for cancer screening from fluorescence hyperspectral images. Experiments evaluate the performance of variable NMF based method and our proposed spectral dissimilarity based NMF methods: 1) The NMF methods do perform well in detect the cancer areas inside the living body; 2) The spectral dissimilarity constrained NMF present more accurate cancer areas; 3) The spectral dissimilarity constraint presents better performance in different SNR and different purities of the mixing endmembers. © 2012 IEEE

Key Information Retrieval in Hyperspectral Imagery through Spatial-Spectral Data Fusion

Hyperspectral (HS) imaging is measuring the radiance of materials within each pixel area at a large number of contiguous spectral wavelength bands. The key spatial information such as small targets and border lines are hard to be precisely detected from HS data due to the technological constraints. Therefore, the need for image processing techniques is an important field of research in HS remote sensing. A novel semisupervised spatial-spectral data fusion method for resolution enhancement of HS images through maximizing the spatial correlation of the endmembers (signature of pure or purest materials in the scene) using a superresolution mapping (SRM) technique is proposed in this paper. The method adopts a linear mixture model and a fully constrained least squares spectral unmixing algorithm to obtain the endmember abundances (fractional images) of HS images. Then, the extracted endmember distribution maps are fused with the spatial information using a spatial-spectral correlation maximizing model and a learning-based SRM technique to exploit the subpixel level data. The obtained results validate the reliability of the technique for key information retrieval. The proposed method is very efficient and is low in terms of computational cost which makes it favorable for real-time applications