Deep Generative Models for Library Augmentation in Multiple Endmember Spectral Mixture Analysis

Abstract

Multiple Endmember Spectral Mixture Analysis (MESMA) is one of the leading approaches to perform spectral unmixing (SU) considering variability of the endmembers (EMs). It represents each EM in the image using libraries of spectral signatures acquired a priori. However, existing spectral libraries are often small and unable to properly capture the variability of each EM in practical scenes, which compromises the performance of MESMA. In this paper, we propose a library augmentation strategy to increase the diversity of existing spectral libraries, thus improving their ability to represent the materials in real images. First, we leverage the power of deep generative models to learn the statistical distribution of the EMs based on the spectral signatures available in the existing libraries. Afterwards, new samples can be drawn from the learned EM distributions and used to augment the spectral libraries, improving the overall quality of the SU process. Experimental results using synthetic and real data attest the superior performance of the proposed method even under library mismatch conditions.

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Cited by in corpus (5)

• Spectral Variability in Hyperspectral Data Unmixing: A Comprehensive Review
• Model-Based Deep Autoencoder Networks for Nonlinear Hyperspectral Unmixing
• Spectral Variability Augmented Sparse Unmixing of Hyperspectral Images
• Kalman Filtering and Expectation Maximization for Multitemporal Spectral Unmixing
• Fast Unmixing and Change Detection in Multitemporal Hyperspectral Data