Stationary Wavelet Convolutional Network With Generative Feature Learning for Hyperspectral Unmixing

Hyperspectral unmixing (HU) can obtain subpixel-level ground object information, which is crucial for the fine advancement of imaging spectrum processing technology. Deep learning (DL) has been widely used in HU recently because of its ability to deeply mine complex relevant features in data. Existing DL unmixing methods usually operate only in the original spatial-spectral feature domain. However, due to noise, spectral variation, and other factors, it is difficult to fully mine effective features and easy to interfere with by only relying on the original domain. To get over these obstacles, we propose an innovative stationary wavelet convolutional network (SWC-Net) for HU. Stationary wavelet transform (SWT) is introduced in SWC-Net to extend the original feature domain to feature domains with different frequencies, which promotes the multiview extraction of information. What is more, a new generative self-supervised feature learning strategy based on wavelet perspective (GSFL-W) is proposed for SWC-Net. More robust features can be obtained by GSFL-W by introducing noisy perturbations into high-frequency inputs and forcing the network to generate the original inputs. The proposed SWC-Net surpasses the advanced approaches by sufficient experiments on one simulated and three real hyperspectral datasets. The code is publicly available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/UPCGIT/SWC-Net</uri>.