Online Unmixing of Multitemporal Hyperspectral Images accounting for Spectral Variability

Abstract

Hyperspectral unmixing is aimed at identifying the reference spectral signatures composing an hyperspectral image and their relative abundance fractions in each pixel. In practice, the identified signatures may vary spectrally from an image to another due to varying acquisition conditions, thus inducing possibly significant estimation errors. Against this background, hyperspectral unmixing of several images acquired over the same area is of considerable interest. Indeed, such an analysis enables the endmembers of the scene to be tracked and the corresponding endmember variability to be characterized. Sequential endmember estimation from a set of hyperspectral images is expected to provide improved performance when compared to methods analyzing the images independently. However, the significant size of hyperspectral data precludes the use of batch procedures to jointly estimate the mixture parameters of a sequence of hyperspectral images. Provided that each elementary component is present in at least one image of the sequence, we propose to perform an online hyperspectral unmixing accounting for temporal endmember variability. The online hyperspectral unmixing is formulated as a two-stage stochastic program, which can be solved using a stochastic approximation. The performance of the proposed method is evaluated on synthetic and real data. A comparison with independent unmixing algorithms finally illustrates the interest of the proposed strategy.

27 pages, 11 figures, accepted in IEEE Trans. Image Process., 2016

References in corpus (2)

• Hyperspectral unmixing with spectral variability using a perturbed linear mixing model
• Estimating the Intrinsic Dimension of Hyperspectral Images Using an Eigen-Gap Approach

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• Hyperspectral image unmixing with LiDAR data-aided spatial regularization
• Dynamical Hyperspectral Unmixing with Variational Recurrent Neural Networks
• Kalman Filtering and Expectation Maximization for Multitemporal Spectral Unmixing
• Fast Unmixing and Change Detection in Multitemporal Hyperspectral Data
• Online Graph-Based Change Point Detection in Multiband Image Sequences