Abstract
Hyperspectral unmixing aims to obtain the hidden constituent materials and the corresponding fractional abundances from mixed pixels, and is an important technique for hyperspectral image (HSI) analysis. In this paper, two characteristics of the abundance variables, namely, the local spatial structural feature and the statistical distribution, are incorporated into nonnegative matrix factorization (NMF) to alleviate the non-convex problem of NMF and enhance the hyperspectral unmixing accuracy. An adaptive local neighborhood weight constraint is proposed for the abundance matrix by taking advantage of the spatial-spectral information of the HSI. The spectral information is utilized to calculate the similarities between pixels, which are taken as the measurement of the smoothness levels. Furthermore, because abrupt changes may appear in transition areas or outliers may exist in spatially neighboring regions, any inappropriate smoothness constraint on these pixels is removed, which can better express the local smoothness characteristic of the abundance variables. In addition, a separation constraint is used to prevent the result from over-smoothing, preserving the inner diversity of the same kind of material. Extensive experiments were carried out on both simulated and real HSIs, confirming the effectiveness of the proposed approach.
Highlights
Past decades have witnessed the great success of hyperspectral imaging in a wide range of applications, due to its capacity to synchronously acquire both spatial and spectral information [1,2].In hyperspectral images (HSIs), the spectral vector of each pixel contains hundreds or even thousands of elements, which provides rich spectral information to efficiently identify and distinguish different types of land cover [3]
Based on the above problems, we propose a novel double abundance characteristics constrained constrained2 nonnegative matrix factorization (NMF) (DAC2NMF) method, taking both the spatial structure information and the NMF (DAC NMF) method, taking both the spatial structure information and the statistical distribution statistical distribution of the abundances into consideration
We propose a constrained NMF method by considering two characteristics of the abundance variables, which is described in the following parts
Summary
Past decades have witnessed the great success of hyperspectral imaging in a wide range of applications, due to its capacity to synchronously acquire both spatial and spectral information [1,2]. Sparse theory based methods have been developed to find the optimal combinations from the library since the number of materials in one pixel is far less than the number of signatures in the library [20,21,22], and the low-rank constraint based on the utilization of spatial correlation has been used to enhance the unmixing result [23,24]. Use of the smoothness constraint is based on the precondition that themay pixels constrained the smoothness constraint is based on the precondition that the pixels being constrained are similar This thisprecondition preconditionisisviolated violated when abrupt changes appear in transition or outliers in the spatially neighboring regions, as shown in the close-ups of Figure. The spatial structure information is not fully explored by these methods sincesight they of thesight pixels are inappropriate for the smoothness constraint.
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