Evaluation the performance of fully convolutional networks for building extraction compared with shallow models

With the development of machine learning, many researchers have used machine learning models for building extraction in high resolution remote sensing images. Especially for the recently proposed deep learning models, it has been widely used for building detection in the urban monitoring. In this study, the performances of images segmentation for building extraction based on Fully Convolutional Networks (FCN) model and shallow models are qualitatively and quantitatively compared. Firstly, the public aerial dataset of Massachusetts building dataset[1] are preprocessed to extract features. Then, we trained shallow models and deep model on Massachusetts building dataset. Moreover, the trained FCN model and shallow models are used to extract the building on the same image. Finally, we compared performances between shallow models and deep model. It is found that FCN gives the highest recall 0.63, precision 0.62, and F-measure rate 0.63. The qualitative and quantitative analysis of the building extraction results fully demonstrates that FCN gives the best performance compared with traditional shallow models.