CFNet: A Cross Fusion Network for Joint Land Cover Classification Using Optical and SAR Images
Wenchao Kang, Yuming Xiang, Feng Wang, Hongjian You
Abstract
As two of the most widely used remote sensing images, optical and synthetic aperture radar (SAR) images show abundant and complementary information on the same target owing to their individual imaging mechanisms. Consequently, using optical and SAR images simultaneously can better describe the inherent features of the target, and thus, be beneficial for subsequent remote sensing applications. In this article, we propose a novel modular fully convolutional network model to improve the accuracy of land cover classification by fully exploiting the complementary features of the two sensors. We investigate where and how to fuse the two images in the joint classification network. A cross-gate module with a bidirectional information flow is proposed to achieve the best fusion performance. In addition, to validate the proposed model, we construct a multiclass land cover classification dataset. Exhaustive experiments show that the proposed joint classification network presents superior results than state-of-the-art classification models using single-sensor images.