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Hyperspectral unmixing is an essential research topic for spectral data analysis due to the existence of mixed pixels. Recently, many methods based on sparse nonnegative matrix factorization (NMF) have been widely used for unmixing by incorporating similarity preserving. However, most of them conduct the similarity learning and unmixing by using two separate steps, which may lead to the case that the learned similarity matrix is not the optimal one for unmixing. Thus, the performance of unmixing would be influenced and become undesirable. To alleviate this problem, we propose an adaptive relationship preserving-based sparse NMF (ARP-NMF) for hyperspectral unmixing. Typically, we regard similarity learning and unmixing as an alternative optimization process. During this process, the learned similarity weights and unmixing results can be mutually improved. Thus, our proposed method can learn the optimal similarity weights for unmixing and obtain better generalization ability for different hyperspectral images than traditional methods. Moreover, by using the spectral and spatial local structure, our ARP-NMF method effectively preserves structure consistency between pixels and abundances. Experimental results both on the synthetic data and the real data reveal that our proposed method outperforms several representative sparse NMF-based methods. [ABSTRACT FROM AUTHOR]

Due to the inconsistent imaging environment, the styles of multitemporal multispectral images (MSIs) are quite different, such as image brightness and transparency. For multitemporal MSIs with different styles, the “same object with different spectra” problem is one of the biggest challenges in change detection. To overcome the challenge, a novel unsupervised spatial–spectral feature learning (FL) framework based on style transformation (ST) (called STFL-CD) is proposed for MSI change detection in this article. For dual-temporal MSIs, the proposed STFl-CD algorithm consists of two phases: ST and spatial–spectral FL. Since the image styles are inconsistent under different imaging environments, the first innovation is to transform the image styles through unmixing and reconstruction. Through ST, the challenge of the “same object with different spectra” problem will be reduced fundamentally. By introducing the attention mechanism, the other innovation is to extract the joint spectral–spatial change features based on a 3-D convolutional neural network with spatial and channel attention. In addition, for multitemporal MSIs, a multitemporal version STFL-CD (MT-STFL-CD) framework is designed based on a recurrent neural network to learn the correlation features between multitemporal remote sensing images. Both of the visual and quantitative results on the real MSI datasets indicate that the proposed unsupervised STFL-CD frameworks have significant advantages on multitemporal MSI change detection. In particular, the performance of the proposed unsupervised STFL-CD algorithm is even comparable to that of the state-of-the-art supervised or semisupervised methods. [ABSTRACT FROM AUTHOR]