River detection based on feature fusion from synthetic aperture radar images

Abstract. 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.

[1]  L.L.F. Janssen,et al.  Accuracy assessment of satellite derived land - cover data : a review , 1994 .

[2]  Andreas Niedermeier,et al.  Detection of coastlines in SAR images using wavelet methods , 2000, IEEE Trans. Geosci. Remote. Sens..

[3]  Zhenming Peng,et al.  River detection using LBP and morphology in infrared image , 2019, International Symposium on Advanced Optical Manufacturing and Testing Technologies (AOMATT).

[4]  Sabine Süsstrunk,et al.  Frequency-tuned salient region detection , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

[5]  Manchun Li,et al.  River Detection in Remotely Sensed Imagery Using Gabor Filtering and Path Opening , 2015, Remote. Sens..

[6]  Youkyung Han,et al.  Image Fusion-Based Change Detection for Flood Extent Extraction Using Bi-Temporal Very High-Resolution Satellite Images , 2015, Remote. Sens..

[7]  K. Olaf Niemann,et al.  Semi-Automated Extraction of Rivers from Digital Imagery , 2002, GeoInformatica.

[8]  Karl Pearson F.R.S..  LIII. On lines and planes of closest fit to systems of points in space , 1901 .

[9]  P. Argouris,et al.  Photogrammetric Week 1993: 20–25 September 1993, Stuttgart, Germany , 1994 .

[10]  Jocelyn Chanussot,et al.  Fuzzy fusion techniques for linear features detection in multitemporal SAR images , 1999, IEEE Trans. Geosci. Remote. Sens..

[11]  Alberto Refice,et al.  A Bayesian Network for Flood Detection Combining SAR Imagery and Ancillary Data , 2016, IEEE Transactions on Geoscience and Remote Sensing.

[12]  Yuhan Liu,et al.  River detection in high-resolution SAR data using the Frangi filter and shearlet features , 2019, Remote Sensing Letters.

[13]  Biswajeet Pradhan,et al.  Data Fusion Technique Using Wavelet Transform and Taguchi Methods for Automatic Landslide Detection From Airborne Laser Scanning Data and QuickBird Satellite Imagery , 2016, IEEE Transactions on Geoscience and Remote Sensing.

[14]  Li Wang,et al.  A Novel Method of River Detection for High Resolution Remote Sensing Image Based on Corner Feature and SVM , 2012, ISNN.

[15]  Liqing Zhang,et al.  Saliency Detection: A Spectral Residual Approach , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[16]  Fang Wei,et al.  Targets Detection in SAR Image Used Coherence Analysis Based on S-Transform , 2011 .

[17]  Erfu Yang,et al.  Visual Saliency Modeling for River Detection in High-Resolution SAR Imagery , 2018, IEEE Access.

[18]  Jinping Sun,et al.  River detection algorithm in SAR images based on edge extraction and ridge tracing techniques , 2011 .

[19]  Konstantinos G. Nikolakopoulos,et al.  Joint use of multitemporal and multisensor remote sensing data, PCA, and GIS techniques for the detection of changes at the lower Alfios River Channel, Greece , 2004, SPIE Remote Sensing.

[20]  Jocelyn Chanussot,et al.  Automatic Detection of Rivers in High-Resolution SAR Data , 2012, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[21]  Alejandro F. Frangi,et al.  Muliscale Vessel Enhancement Filtering , 1998, MICCAI.

[22]  S. Lehner,et al.  Monitoring big river estuaries using SAR images , 2001, IGARSS 2001. Scanning the Present and Resolving the Future. Proceedings. IEEE 2001 International Geoscience and Remote Sensing Symposium (Cat. No.01CH37217).

[23]  Demetri Terzopoulos,et al.  Snakes: Active contour models , 2004, International Journal of Computer Vision.

[24]  H. Hotelling.  Analysis of a complex of statistical variables into principal components. , 1933 .

[25]  MIN ZHAO,et al.  SEISMIC MULTI-ATTRIBUTE FUSION USING FAST INDEPENDENT COMPONENT ANALYSIS AND ITS APPLICATION , 2019 .

[26]  Dario Izzo,et al.  Super-resolution of PROBA-V images using convolutional neural networks , 2019, Astrodynamics.

[27]  Saurabh Prasad,et al.  Decision Fusion of Textural Features Derived From Polarimetric Data for Levee Assessment , 2012, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[28]  G. Cao,et al.  Automatic detection of main road network in dense urban area using microwave SAR images , 2007 .

[29]  Shijun Liu,et al.  Bridge Detection and Recognition in Remote Sensing SAR Images Using Pulse Coupled Neural Networks , 2010 .

[30]  Jie Zhang,et al.  Time-Frequency Analysis of SAR Image Based on Generalized S-Transform , 2009, 2009 International Conference on Measuring Technology and Mechatronics Automation.

[31]  N. Otsu.  A threshold selection method from gray level histograms , 1979 .

[32]  Björn Waske,et al.  Classifying Multilevel Imagery From SAR and Optical Sensors by Decision Fusion , 2008, IEEE Transactions on Geoscience and Remote Sensing.