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.