Abstract
Recently, unmixing methods based on nonnegative tensor factorization have played an important role in the decomposition of hyperspectral mixed pixels. According to the spatial prior knowledge, there are many regularizations designed to improve the performance of unmixing algorithms, such as the total variation (TV) regularization. However, these methods mostly ignore the similar characteristics among different spectral bands. To solve this problem, this paper proposes a group sparse regularization that uses the weighted constraint of the L2,1 norm, which can not only explore the similar characteristics of the hyperspectral image in the spectral dimension, but also keep the data smooth characteristics in the spatial dimension. In summary, a non-negative tensor factorization framework based on weighted group sparsity constraint is proposed for hyperspectral images. In addition, an effective alternating direction method of multipliers (ADMM) algorithm is used to solve the algorithm proposed in this paper. Compared with the existing popular methods, experiments conducted on three real datasets fully demonstrate the effectiveness and advancement of the proposed method.
Highlights
Hyperspectral remote sensing images have a wide range of applications in the field of earth observation because of the rich spectral information [1,2,3]
The nonnegative matrix factorization (NMF)-based methods mentioned above have significantly improved the performance of unmixing
It is worth noting that the average and variance of the results of all experiments have been presented after 20 runs, which can illustrate the stability of algorithms
Summary
Hyperspectral remote sensing images have a wide range of applications in the field of earth observation because of the rich spectral information [1,2,3]. Some methods use the L2,1 regularization to simultaneously explore the sparse structure in the abundance map [16], which has been proven to have good performance in the unmixing algorithm. In [25], the clustering algorithm is used to explore the global similarity relationship of pixels in the HSI, and it is combined with the NMF framework to improve the performance of unmixing. The NMF-based framework reshapes the original 3-D HSI into a 2-D matrix before unmixing This method of image reshaping severely destroys the 3-D cube structure of HSI, and it is difficult to completely explore the missing spatial information even if spatial regularization constraints are subsequently used [26]. The above methods have good performance in hyperspectral unmixing, the NTF-based models still have room for improvement in combining spatial and spectral information.
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