Blind and fully constrained unmixing of hyperspectral images
This addresses hyperspectral image analysis for remote sensing applications, but is incremental as it builds on existing unmixing methods with new noise-handling models.
The paper tackles blind hyperspectral image unmixing without using a dictionary or prior knowledge of materials, developing two noise-aware models that enforce sum-to-one and nonnegativity constraints on abundances. Experiments on synthetic and real data show the approach is effective.
This paper addresses the problem of blind and fully constrained unmixing of hyperspectral images. Unmixing is performed without the use of any dictionary, and assumes that the number of constituent materials in the scene and their spectral signatures are unknown. The estimated abundances satisfy the desired sum-to-one and nonnegativity constraints. Two models with increasing complexity are developed to achieve this challenging task, depending on how noise interacts with hyperspectral data. The first one leads to a convex optimization problem, and is solved with the Alternating Direction Method of Multipliers. The second one accounts for signal-dependent noise, and is addressed with a Reweighted Least Squares algorithm. Experiments on synthetic and real data demonstrate the effectiveness of our approach.