Aircraft segmentation in remote sensing images based on multi-scale residual U-Net with attention

Abstract

Aircraft segmentation in remote sensing images (RSIs) is an important but challenging problem for both civil and military applications. U-Net and its variants are widely used in RSI detection, but they are not suitable for multi-scale aircraft segmentation in RSIs, due to the aircrafts in RSIs are relatively small with various orientations, different sizes, fuzzy illumination and shadow, obscure boundary and irregular background. To overcome this problem, a multi-scale residual U-Net with attention (MSRAU-Net) model is constructed for multi-scale aircraft segmentation in RSIs. A multi-scale convolutional module, two modified Respaths and two kinds of attention modules are designed and introduced into MSRAU-Net to extract the multi-scale feature and make the feature fusion between the contraction path and the expansion path more efficient. Different from U-Net, MSRAU-Net replaces the convolutional block of U-Net with the Inception residual block to help the U-Net architecture coordinate the features learned from aircrafts with different scales, and the residual module and attention module are introduced into the modified Respath to deepen the network layers and solve the gradient disappearing problem while extracting the more effective feature from RSIs. The experiments on the RSI dataset validate that MSRAU-Net outperforms the other networks, in particular for detecting the small aircrafts. Compared with attention U-Net and MultiMixUNet, the precision of MSRAU-Net is improved by 9.25 and 3.36, respectively.