Remote sensing image denoising based on attention mechanism and perceptual loss

Abstract:

Remote sensing image denoising plays a very important role in the subsequent classification and detection tasks for remote sensing images. In order to retain more the edge details information in the denoised images and enhance the discrimination for contaminated regions, in this paper, the authors propose a new Attention based Residual Encoder-Decoder network aided by VGG (ARED-VGG) for remote sensing image denoising, which combines attention mechanism, and perceptual loss. First, considering that the different ground objects have different sizes, the network utilizes both spatial and spectral information to extract multiscale features. Second, in order to better represent the extracted features and improve the denoising effect, the residual autoencoder network is used in image reconstruction stage. Moreover, convolution operations that are responsible to extract high-frequency features should be paid more attention to facilitating the location of noise-affected regions. Based on these considerations, the attention mechanism is introduced to adaptively modulate feature representation. Finally, perceptual loss is adopted to maintain the visual results consistent to the human perception. In the experiments, the proposed method demonstrates superior performance in noise suppression and detail preservation to NLM3D, BM4D, LRMR, HSID-CNN and 3DADCNN. Simulation experiments were carried out on the Washington DC mall remote sensing image data set, d the results of mean peak signal-to-noise ratio and mean structure similarity index of the proposed method are better than the other methods. Real experiments were carried out on the AVIRIS Indian pines data set. The classification results for the denoised images were evaluated. The overall classification accuracy and kappa coefficient were 96.90% and 0.9647, respectively. The ablation experiment was carried out on the network structure on both data sets the proposed network architecture achieved best results. This paper proposes a deep neural network based on attention mechanism and perceptual loss to denoise remote sensing images, which improves the recognition ability of the network, achieves good denoising performance, and effectively preserves the detailed information and spectral information of the image.