SANet: A Sea–Land Segmentation Network Via Adaptive Multiscale Feature Learning

Abstract

Sea-land segmentation of remote sensing images is of great significance to the dynamic monitoring of coastlines. However, the types of objects in the coastal zone are complex, and their spectra, textures, shapes, and distribution features are different. Therefore, sea-land segmentation for various types of coastlines is still a challenging task. In this article, a scale-adaptive semantic segmentation network, called SANet, is proposed for sea-land segmentation of remote sensing images. SANet has made two innovations on the basis of the classic encoder-decoder structure. First, to integrate the spectral, textural, and semantic features of ground objects at different scales, we designed an adaptive multiscale feature learning module (AML) to replace the conventional serial convolution operation. The AML module mainly contains a multiscale feature extraction unit and an adaptive feature fusion unit. The former can capture the multiscale detailed information and contextual semantic information of objects from an early stage, while the latter can adaptively fuse feature maps of different scales. Second, we adopted the squeeze-and-excitation module to bridge the corresponding layers of the codec so that SANet can selectively emphasize the features of the weak sea-land boundaries. Experiments on a set of Gaofen-1 remote sensing images demonstrated that SANet achieved more accurate segmentation results and obtained sharper boundaries than other methods for various natural and artificial coastlines.

Keywords

• Adaptive learning
• atrous convolution
• remote sensing image
• residual block
• sea–land segmentation
• squeeze-excitation module