摘要
Abstract
In recent years,methods based on convolutional neural network(CNN),especially the siamese network,have become the mainstream networks for remote sensing image change detection tasks.However,traditional siamese network models have inherent limitations in feature extraction and representation capabilities,making it difficult to adapt to the diversity of geometric shapes and spatial scales in change regions,leading to common issues such as false change misjudgments and missing real changes in detection results.To address the above technical bottlenecks,a high-performance pixel-level change detection model,Mix-Nested-Unet Change Detection(MNUNet-CD),is proposed.The model employs a Mix Residual Block for multi-dimensional feature extraction on dual-temporal image pairs,constructing a hierarchical feature representation system.It introduces a multi-scale feature fusion mechanism to achieve fine-grained capture of change patterns at different spatial resolutions.Additionally,a global attention module is designed to adaptively filter the feature space,enhancing the model's ability to represent key change features.Experimental results show that the proposed model demonstrates certain performance advantages on three public datasets(CDD,WHU-CD,and LEVIR-CD).Compared with the benchmark method(SNUNet-CD),it achieves F1 score improvements of 3.3%,0.7%,and 0.9%,respectively.关键词
遥感图像/图像变化检测/深度学习/孪生网络/全局注意力Key words
remote sensing image/image change detection/deep learning/siamese network/global attention分类
信息技术与安全科学
