From local to global unmixing of hyperspectral images to reveal spectral variability

International audienceThe linear mixing model is widely assumed when unmixing hyperspectral images, but it cannot account for endmembers spectral variability. Thus, several workarounds have arisen in the hyperspectral unmixing literature, such as the extended linear mixing model (ELMM), which authorizes endmembers to vary pixelwise according to scaling factors, or local spectral unmixing (LSU) where the unmixing process is conducted locally within the image. In the latter case however, results are difficult to interpret at the whole image scale. In this work, we propose to analyze the local results of LSU within the ELMM framework, and show that it not only allows to reconstruct global endmembers and fractional abundances from the local ones, but it also gives access to the scaling factors advocated by the ELMM. Results obtained on a real hyperspectral image confirm the soundness of the proposed methodology

Dynamical spectral unmixing of multitemporal hyperspectral images

In this paper, we consider the problem of unmixing a time series of hyperspectral images. We propose a dynamical model based on linear mixing processes at each time instant. The spectral signatures and fractional abundances of the pure materials in the scene are seen as latent variables, and assumed to follow a general dynamical structure. Based on a simplified version of this model, we derive an efficient spectral unmixing algorithm to estimate the latent variables by performing alternating minimizations. The performance of the proposed approach is demonstrated on synthetic and real multitemporal hyperspectral images.Comment: 13 pages, 10 figure

Silica in a Mars analog environment: Ka'u Desert, Kilauea Volcano, Hawaii

Airborne Visible/Near-Infrared Imaging Spectrometer (AVIRIS) data acquired over the Ka'u Desert are atmospherically corrected to ground reflectance and used to identify the mineralogic components of relatively young basaltic materials, including 250–700 and 200–400 year old lava flows, 1971 and 1974 flows, ash deposits, and solfatara incrustations. To provide context, a geologic surface units map is constructed, verified with field observations, and supported by laboratory analyses. AVIRIS spectral end-members are identified in the visible (0.4 to 1.2 μm) and short wave infrared (2.0 to 2.5 μm) wavelength ranges. Nearly all the spectral variability is controlled by the presence of ferrous and ferric iron in such minerals as pyroxene, olivine, hematite, goethite, and poorly crystalline iron oxides or glass. A broad, nearly ubiquitous absorption feature centered at 2.25 μm is attributed to opaline (amorphous, hydrated) silica and is found to correlate spatially with mapped geologic surface units. Laboratory analyses show the silica to be consistently present as a deposited phase, including incrustations downwind from solfatara vents, cementing agent for ash duricrusts, and thin coatings on the youngest lava flow surfaces. A second, Ti-rich upper coating on young flows also influences spectral behavior. This study demonstrates that secondary silica is mobile in the Ka'u Desert on a variety of time scales and spatial domains. The investigation from remote, field, and laboratory perspectives also mimics exploration of Mars using orbital and landed missions, with important implications for spectral characterization of coated basalts and formation of opaline silica in arid, acidic alteration environments

Hyperspectral colon tissue cell classification

A novel algorithm to discriminate between normal and malignant tissue cells of the human colon is presented. The microscopic level images of human colon tissue cells were acquired using hyperspectral imaging technology at contiguous wavelength intervals of visible light. While hyperspectral imagery data provides a wealth of information, its large size normally means high computational processing complexity. Several methods exist to avoid the so-called curse of dimensionality and hence reduce the computational complexity. In this study, we experimented with Principal Component Analysis (PCA) and two modifications of Independent Component Analysis (ICA). In the first stage of the algorithm, the extracted components are used to separate four constituent parts of the colon tissue: nuclei, cytoplasm, lamina propria, and lumen. The segmentation is performed in an unsupervised fashion using the nearest centroid clustering algorithm. The segmented image is further used, in the second stage of the classification algorithm, to exploit the spatial relationship between the labeled constituent parts. Experimental results using supervised Support Vector Machines (SVM) classification based on multiscale morphological features reveal the discrimination between normal and malignant tissue cells with a reasonable degree of accuracy

Blind hyperspectral unmixing using an Extended Linear Mixing Model to address spectral variability

International audienceSpectral Unmixing is one of the main research topics in hyperspectral imaging. It can be formulated as a source separation problem whose goal is to recover the spectral signatures of the materials present in the observed scene (called endmembers) as well as their relative proportions (called fractional abundances), and this for every pixel in the image. A Linear Mixture Model is often used for its simplicity and ease of use but it implicitly assumes that a single spectrum can be completely representative of a material. However, in many scenarios, this assumption does not hold since many factors, such as illumination conditions and intrinsic variability of the endmembers, induce modifications on the spectral signatures of the materials. In this paper, we propose an algorithm to unmix hyperspectral data using a recently proposed Extended Linear Mixing Model. The proposed approach allows a pixelwise spatially coherent local variation of the endmembers, leading to scaled versions of reference endmembers. We also show that the classic nonnegative least squares, as well as other approaches to tackle spectral variability can be interpreted in the framework of this model. The results of the proposed algorithm on two different synthetic datasets, including one simulating the effect of topography on the measured reflectance through physical modelling, and on two real datasets, show that the proposed technique outperforms other methods aimed at addressing spectral variability, and can provide an accurate estimation of endmember variability along the scene thanks to the scaling factors estimation