Abstract
This paper proposes the use of spectral unmixing and sparse reconstruction methods to restore a simulated dataset for the Environmental Mapping and Analysis Program (EnMAP), the forthcoming German spaceborne hyperspectral mission. The described method independently decomposes each image element into a set of representative spectra, which come directly from the image and have previously undergone a low-pass filtering in noisy bands. The residual vector from the unmixing process is considered as mostly composed of noise and ignored in the reconstruction process. The first assessment of the results is encouraging, as the original bands taken into account are reconstructed with a high signal-to-noise ratio and low overall distortions. Furthermore, the same method could be applied for the inpainting of dead pixels, which could affect EnMAP data, especially at the end of the satellite’s life cycle.
Highlights
The future EnMAP (Environmental Mapping and Analysis Program) mission will be able to acquire images at ±30◦ off-nadir to achieve revisit times of up to four days
This paper proposes sparse unmixing-based denoising (SUBD), a modified version of UBD relying on sparse reconstruction techniques, which have been shown to outperform traditional unmixing approaches [4]
Sparse reconstruction is coupled with UBD by selecting as overcomplete spectral library a large set of pixels randomly selected from the image and preprocessed in order to present reduced noise contributions
Summary
The future EnMAP (Environmental Mapping and Analysis Program) mission will be able to acquire images at ±30◦ off-nadir to achieve revisit times of up to four days. For a remotely-sensed image acquired at typical spatial resolutions, the underlying assumption of sparse spectral unmixing is that only a limited number of pure materials can contribute to the backscattered energy measured in a single resolution cell. These methods are able to decompose the spectrum of each pixel in fractions related to only a few reference spectra. Sparse reconstruction is coupled with UBD by selecting as overcomplete spectral library a large set of pixels randomly selected from the image and preprocessed in order to present reduced noise contributions.
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