Urban scene segmentation model based on multi-scale shuffle features

The monitoring of urban land categories is crucial for effective land resource management and urban planning. To address challenges such as uneven parcel distribution, difficulty in feature extraction and loss of image information in urban remote sensing images, this study proposes a multi-scale feature shuffle urban scene segmentation model. The model utilizes a deep convolutional encoder-decoder network with BlurPool instead of MaxPool to compensate for missing translation invariance. GSSConv and SE module are introduced to enhance information interaction and filter redundant information, minimizing category misclassification caused by similar feature distributions. To address unclear boundary information during feature extraction, the model applies multi-scale attention to aggregate context information for better integration of boundary and global information. Experiments conducted on the BDCI2017 public dataset show that the proposed model outperforms several established segmentation networks in OA, mIoU, mRecall, P and Dice with scores of 83.1%, 71.0%, 82.7%, 82.7% and 82.5%, respectively. By effectively improving the completeness and accuracy of urban scene segmentation, this study provides a better understanding of urban development and offers suggestions for future planning.