MsanlfNet: Semantic Segmentation Network With Multiscale Attention and Nonlocal Filters for High-Resolution Remote Sensing Images
Lin Bai, Xiangyuan Lin, Zhen Ye, Dongling Xue, Yao Cheng, Meng Hui
Abstract
With the development of deep learning, remote sensing image semantic segmentation has produced significant advances. The majority of existing methods use fully convolutional network (FCN) that lacks fine-grained multi-scale representation and fails to extract global context information. Thus, we improve FCN by adding two modules—multi-scale attention (MSA) and non-local filter (NLF). The MSA module enhances the network’s fine-grained multi-scale representation capability and allows modeling the inter-dependencies of feature maps among different channels. The NLF module can capture global context information by sequential using fast Fourier transform, parameter learnable filters and inverse fast Fourier transform. By using MSA module for encoder and NLF module for decoder in the FCN framework, MsanlfNet can obtain both fine-grained multi-scale spatial feature and global context information, thus achieving a balance between performance and computational effort. Experimental results on the remote sensing semantic segmentation public data sets demonstrate that our method can achieve better performance. The code is available at https://github.com/xyuanLin/MsanlfNet.