Abstract
Hyperspectral unmixing, aiming to estimate the fractional abundances of pure spectral signatures in each mixed pixel, has attracted considerable attention in analyzing hyperspectral images. Plenty of sparse unmixing methods have been proposed in the literature that achieved promising performance. However, many of these methods overlook the latent geometrical structure of the hyperspectral data which limit their performance to some extent. To address this issue, a double reweighted sparse and graph regularized unmixing method is proposed in this paper. Specifically, a graph regularizer is employed to capture the correlation information between abundance vectors, which makes use of the property that similar pixels in a spectral neighborhood have higher probability to share similar abundances. In this way, the latent geometrical structure of the hyperspectral data can be transferred to the abundance space. In addition, a double weighted sparse regularizer is used to enhance the sparsity of endmembers and the fractional abundance maps, where one weight is introduced to promote the sparsity of endmembers as a hyperspectral image typically contains fewer endmembers compared to the overcomplete spectral library and the other weight is exploited to improve the sparsity of the abundance matrix. The weights of the double weighted sparse regularizer used for the next iteration are adaptively computed from the current abundance matrix. The experimental results on synthetic and real hyperspectral data demonstrate the superiority of our method compared with some state-of-the-art approaches.
Highlights
Hyperspectral imaging has the capacity to record the same scene over many contiguous spectral bands including the visible, nearinfrared and shortwave infrared spectral bands, and it has been widely used in practical applications such as terrain classification, mineral detection and exploration [1,2], military target discrimination, environmental monitoring and pharmaceutical counterfeiting [3]
To fully utilize the underlying structure of hyperspectral images (HSIs) and sparsity property of abundance matrix, we propose a double reweighted sparse and graph regularized hyperspectral unmixing model
In the synthetic data experiments, we compare our method with some representative methods: (1) SUnSAL [21]; (2) CLSUnSAL [22]; (3) DRSU-total variation (TV) [4]; (4) GLUP-Lap [28]; (5) Graph-regularized unmixing method, abbreviated as GraphHU; and (6) Sparse graph-regularized unmixing method (set W1 = W2 as all ones matrices in problem (P2)), abbreviated as SGHU
Summary
Hyperspectral imaging has the capacity to record the same scene over many contiguous spectral bands including the visible, nearinfrared and shortwave infrared spectral bands, and it has been widely used in practical applications such as terrain classification, mineral detection and exploration [1,2], military target discrimination, environmental monitoring and pharmaceutical counterfeiting [3]. Nonlinear mixing model assumes that part of the source radiation is affected by multiple scattering effects through several endmembers in the scene before being collected at the sensor. The observed mixed pixel spectrum can be expressed as a linear combination of a collection of endmember signatures, which is weighted by their corresponding fractional abundances. Even though LMM is not always a precise model to characterize many real scenarios, it is generally recognized as an acceptable approximation of the light scattering and instrument mechanism. It has been widely used for HU due to its simplicity, effectiveness in different applications and computational tractability
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