SAR images change detection using proposed lamina attention-based noise-resistant UNet advance network (LANRUNet plus plus )

In the past few years, change detection techniques using Convolutional Neural Networks (CNNs) and conventional methods have garnered considerable attention in satellite image analysis. However, both categories of existing approaches have their respective shortcomings. For instance, methods based on CNNs tend to overlook the interactions amongst multilayer convolutions, while traditional methods may contain errors in the initial classification stage that limit the optimization of network. To address these limitations, we propose a new attentionbased, noise-resistant advanced UNet model named as Lamina Attention-Based Noise-Resistant UNet Advance Network (LANRUNet++)for detecting changes in satellite imagery. UNet++ is a unique neural network structure composed of an encoder and decoder, that are linked by a sequence of dense convolutional blocks nested within each other. A core principle underlying the proposed LANRUNet++ is to effectively bridge the semantic disparity between the encoder and decoder feature maps prior to their fusion. We present a lamina attention mechanism within UNet++, which dynamically assigns weights to features extracted from various convolutional layers. This facilitates improved incorporation of spatial context information. Moreover, we added a loss function that is robust to noise and can effectively minimize the influence of inaccurate annotations. As a result, our model becomes less susceptible to errors in initial classification outcomes. Furthermore, we added a Pixel to Density Ratio Parameter that further reduces noisy labels, improving the robustness of the model. We conducted a comprehensive performance assessment of LANRUNet++ on SAR images and conducted a comparative analysis against a range of cutting-edge techniques currently available. The results of our experiments demonstrate that our proposed approach surpasses other existing methods in its ability to detect changes, achieving a higher level of accuracy with various metrics.