Marine reclamation feature analysis based on GF-3 SAR remote sensing imagery

Marine Reclamation detection can realize the rational exploitation and effective utilization of coastal resources. Gaofen 3 (GF-3), as a new launched full polarimetric SAR satellite, can be applied into many fields of oceanic environment and marine sea-area use. In this paper, GF-3 SAR imagery are adopted for marine reclamation detection. Different kinds of reclamation in SAR imagery are analyzed compared with GF-2 multispectral data. A coastline detection algorithm is proposed to coastline extraction based on GF-3 imagery. Jinzhou reclamation experiment demonstrates the effectiveness of GF-3 SAR data.

[1]  Jianchao Fan,et al.  A hybrid particle swarm optimization algorithm for coastline SAR image automatic detection , 2016, 2016 12th World Congress on Intelligent Control and Automation (WCICA).

[2]  Giampaolo Ferraioli,et al.  Unsupervised Coastal Line Extraction From SAR Images , 2013, IEEE Geoscience and Remote Sensing Letters.

[3]  Michael Schmitt,et al.  Automatic coastline detection in non-locally filtered tandem-X data , 2015, 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS).

[4]  Jianchao Fan,et al.  Remote sensing images coastline detection based on geometric active contour models , 2015, 2015 Sixth International Conference on Intelligent Control and Information Processing (ICICIP).

[5]  Zhao-Liang Li,et al.  Quantitative Analysis of Shoreline Changes in Western Taiwan Coast Using Time-Series SAR Images , 2016, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[6]  William Perrie,et al.  Target Detection on the Ocean With the Relative Phase of Compact Polarimetry SAR , 2013, IEEE Transactions on Geoscience and Remote Sensing.

[7]  Fei Li,et al.  A Novel Region-Merging Approach for Coastline Extraction From Sentinel-1A IW Mode SAR Imagery , 2016, IEEE Geoscience and Remote Sensing Letters.

[8]  Baba C. Vemuri,et al.  Shape Modeling with Front Propagation: A Level Set Approach , 1995, IEEE Trans. Pattern Anal. Mach. Intell..

[9]  Chunming Li,et al.  Distance Regularized Level Set Evolution and Its Application to Image Segmentation , 2010, IEEE Transactions on Image Processing.

[10]  Giampaolo Ferraioli,et al.  Markovian Change Detection of Urban Areas Using Very High Resolution Complex SAR Images , 2014, IEEE Geoscience and Remote Sensing Letters.

[11]  Xiaofeng Li,et al.  Performance Analysis and Validation of Waterline Extraction Approaches Using Single- and Dual-Polarimetric SAR Data , 2015, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.