Paper Info
Title
CFNet: A Cross Fusion Network for Joint Land Cover Classification Using Optical and SAR Images
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.
Year
DOI
Venue
2022
10.1109/JSTARS.2022.3144587
IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
Keywords
DocType
Volume
Optical sensors, Optical imaging, Radar polarimetry, Feature extraction, Adaptive optics, Convolution, Optical scattering, Fully convolutional network (FCN), joint land cover classification, optical remote sensing image, synthetic aperture radar (SAR)
Journal
15
ISSN
Citations 
PageRank 
1939-1404
0
0.34
References 
Authors
0
4
Name
Order
Citations
PageRank
Wenchao Kang100.34
Yuming Xiang2156.30
Feng Wang339137.89
Hongjian You410317.44