A Review of Optical and SAR Image Deep Feature Fusion in Semantic Segmentation

Abstract

—With the advent of the era of high-resolution remote sensing, semantic segmentation methods for solving pixel-level classification have been widely studied. Deep learning has significantly advanced deep feature extraction methods, becoming widely employed in remote sensing image analysis. Deep feature fusion methods are able to effectively combine features from different sources. Optical and synthetic aperture radar (SAR) images stand out as primary data sources in remote sensing, offering complementary and consistent information. Fusion of deep semantic features of optical and SAR images can alleviate the limitations of single-source images in application and improve semantic segmentation accuracy. Therefore, this article reviews the research on deep fusion of optical and SAR images in semantic segmentation tasks from four aspects. First, we provide a summary of challenges and research methods pertinent to semantic segmentation of remote sensing images. Then the challenges and urgent needs of deep feature fusion of optical and SAR images are analyzed, and current research is summarized from the perspective of structural design by studying various feature fusion strategies. In addition, the compilation and in-depth analysis of open-source optical and SAR datasets suitable for semantic segmentation are undertaken, serving as fundamental resources for future research endeavors. Finally, the article identifies the major challenges summarized from the literature review in this field, outlining expectations and potential future directions for researchers.