Change Detection of Remote Sensing Images with Multi-channel U-shaped Network
Change detection of remote sensing images is an important research direction in the field of remote sensing,which plays an important role in many fields such as agriculture,disaster assessment,and urban construction.At present,most change detection tasks are completed using deep learning methods,but many existing deep learning networks have problems such as weak image feature extraction ability and inability to finely distinguish between change regions.A deep U-shaped network MCFFNet with multi-channel and multi-scale feature fusion is proposed.Firstly,the Unet network is extended to a three-channel structure,and the pre-classification feature information and fusion features of the corresponding scale feature images are obtained during the down-sampling process.Then,during the up-sampling process,the feature information of the corresponding scale is fused.Finally,the feature map is mapped into a single optimal change detection result map through convolutional activation and other operations.Experiments on the commonly used datasets CDD and WHU in the field of remote sensing image change detection have achieved higher change detection accuracy than the methods for comparison.
国家科技期刊平台
NSTL
万方数据
维普
