MSRF-Net: Multiscale Receptive Field Network for Building Detection From Remote Sensing Images

Extracting buildings from remote sensing images plays an important role in urban development planning, disaster assessment, and mapping. Convolutional neural network (CNN) has been widely applied to building extraction because of its powerful deep semantic feature extraction ability. However, the existing CNN-based building extraction methods are difficult to accurately extract multiscale buildings with accurate edges because of the limitation of feature receptive fields and the loss of spatial detail information. For the above problems, this article proposes a multiscale receptive field network (MSRF-Net) to accurately extract multiscale buildings from remote sensing images. MSRF-Net includes multiscale receptive field feature encoder (MRFF-Encoder) and multipath decoder. In the MRFF-Encoder, a multiscale attentional down (MSAD) module and asymmetric residual inception (ARI) module are proposed to capture multiscale receptive field features. In the multipath decoder, convolutions with different kernel sizes and dilations are used in three parallel paths to learn localization-preserved multiscale features with multiscale receptive field. What is more, the features of different branches and MRFF-Encoder are fused by the proposed feature combination module, which contribute to capture context information of multiscale receptive field while recovering the resolution of feature space. The experimental results show that compared with the latest MAP-Net, MSRF-Net has achieved F1 score growth of 1.14%, 0.42%, and 1.11% and IoU score growth of 1.68%, 0.76%, and 1.64%, respectively, on Massachusetts dataset, Wuhan University (WHU) dataset, and the Typical Cities Building dataset.