River detection based on feature fusion from synthetic aperture radar images

Synthetic aperture radar (SAR) data that can collect information day and night is widely applied in both military and civilian life for security, environmental, and geographical systems. However, detection of rivers in such images is still a challenging problem because rivers are complex with various directions and branches. We aim to detect rivers from SAR images and propose an algorithm combining saliency features, multifeature fusion, and active contour model. The proposed method first filters the image and extracts the global saliency features, which are different from traditional river detection approaches that are mostly based on edge information. A feature fusion technique based on principal component analysis is then applied to merge the saliency features to achieve optimal feature map. Finally, an active contour model is applied to detect the river. Our major contributions are characterizing the rivers by their saliency features, introducing a feature fusion method, and designing an improvement strategy. Experimental results and assessments show that the algorithm is effective and can achieve competitive performance compared with other methods. (C) 2020 Society of Photo-Optical Instrumentation Engineers (SPIE)