A Noise Estimation Method for Hyperspectral Image Based on Stacked Autoencoder

The imaging spectrometer is limited by a short response time and narrow channel, resulting in a low signal-to-noise ratio of hyperspectral images. The accurate estimation of noise has a significant impact on some preprocessing and downstream tasks. The existing noise estimation methods for hyperspectral images are all focused on satellite and aviation data, and there is little research on hyperspectral images with high spatial resolution. For this type of image, This article proposes a noise estimation method based on a stacked autoencoder. Firstly, the image is divided into multiple uniform regions using the K-means algorithm, and then a stacked automatic encoder is set for each uniform region. Reconstruct the spectral signal on each pixel through the corresponding stacked automatic encoder. Calculate the residual between the reconstructed image and the original image to achieve signal-to-noise separation. Finally, the image is divided into a large number of subblocks, and the subblocks containing edges are removed. The remaining subblocks are used for noise estimation in this band. The applicability of some classic noise estimation methods was experimentally tested, and the effectiveness and stability of the proposed method were verified through simulation and real data experiments.