Building detection based on searching of the optimal kernel shapes pruning method on Res2-Unet

Abstract

In recent years, advances in remote sensing technology have made it feasible to use satellite data for large-scale building detection. Moreover, the building detection from multispectral satellite photography data is necessary; however, it is difficult to recovery the accurate building footprint from the high-resolution pictures. Because the deep learning networks contains high computational cost and over-parameterized. Therefore, network pruning has been used to reduce the storage and computations of convolutional neural network (CNN) models. In this article, we proposed the pruning technique to prune the CNN network from Res2-Unet model for accurately detecting the buildings. Initially, the CNN network is pruned by utilizing the searching of the optimal kernel shapes technique. It is employed to carry out stripe-wise pruning and automatically find the ideal kernel shapes. Then the data quantification is applied to enhance the proposed model and also reduce the complexity. Finally, the enhanced Res2-Unet model is used for the building detection. Moreover, WHU East Asia Satellite and the Massachusetts building dataset are the two available datasets used to access the suggested framework. Compare to the existing models, the proposed model gives better performance.