New modified fuzzy C-means algorithm for synthetic aperture radar (SAR) image segmentation

2000 ◽  
Author(s):  
Punya Thitimajshima
Keyword(s):  
Image Segmentation ◽  
Synthetic Aperture Radar ◽  
Synthetic Aperture ◽  
Sar Image ◽  
Fuzzy C Means ◽  
Sar Image Segmentation ◽  
Fuzzy C Means Algorithm ◽  
Aperture Radar

Robust non-local fuzzy c-means algorithm with edge preservation for SAR image segmentation

2013 ◽  
Vol 93 (2) ◽  
pp. 487-499 ◽  
Author(s):  
Jie Feng ◽  
L.C. Jiao ◽  
Xiangrong Zhang ◽  
Maoguo Gong ◽  
Tao Sun
Keyword(s):  
Image Segmentation ◽  
Sar Image ◽  
Edge Preservation ◽  
Fuzzy C Means ◽  
Sar Image Segmentation ◽  
Non Local ◽  
Fuzzy C Means Algorithm

scholarly journals A New Synthetic Aperture Radar (SAR) Imaging Method Combining Match Filter Imaging and Image Edge Enhancement

Sensors ◽  
2018 ◽  
Vol 18 (12) ◽  
pp. 4133 ◽  
Author(s):  
Bing Sun ◽  
Chuying Fang ◽  
Hailun Xu ◽  
Anqi Gao
Keyword(s):  
Image Processing ◽  
Image Segmentation ◽  
Synthetic Aperture Radar ◽  
Synthetic Aperture ◽  
Imaging Method ◽  
Sar Image ◽  
Sar Images ◽  
Gradient Operator ◽  
Sar Imaging ◽  
Aperture Radar

In general, synthetic aperture radar (SAR) imaging and image processing are two sequential steps in SAR image processing. Due to the large size of SAR images, most image processing algorithms require image segmentation before processing. However, the existence of speckle noise in SAR images, as well as poor contrast and the uneven distribution of gray values in the same target, make SAR images difficult to segment. In order to facilitate the subsequent processing of SAR images, this paper proposes a new method that combines the back-projection algorithm (BPA) and a first-order gradient operator to enhance the edges of SAR images to overcome image segmentation problems. For complex-valued signals, the gradient operator was applied directly to the imaging process. The experimental results of simulated images and real images validate our proposed method. For the simulated scene, the supervised image segmentation evaluation indexes of our method have more than 1.18%, 11.2% and 11.72% improvement on probabilistic Rand index (PRI), variability index (VI), and global consistency error (GCE). The proposed imaging method will make SAR image segmentation and related applications easier.

scholarly journals Micro-Motion Estimation of Maritime Targets Using Pixel Tracking in Cosmo-Skymed Synthetic Aperture Radar Data—An Operative Assessment

2019 ◽  
Vol 11 (14) ◽  
pp. 1637 ◽  
Author(s):  
Filippo Biondi ◽  
Pia Addabbo ◽  
Danilo Orlando ◽  
Carmine Clemente
Keyword(s):  
Synthetic Aperture Radar ◽  
Wide Band ◽  
Radar Data ◽  
Satellite System ◽  
Synthetic Aperture ◽  
Sar Image ◽  
Synthetic Aperture Radar Data ◽  
Micro Motion ◽  
Novel Strategy ◽  
Aperture Radar

In this paper, we propose a novel strategy to estimate the micro-motion (m-m) of ships from synthetic aperture radar (SAR) images. To this end, observe that the problem of motion and m-m detection of targets is usually solved using synthetic aperture radar (SAR) along-track interferometry through two radars spatially separated by a baseline along the azimuth direction. The approach proposed in this paper for m-m estimation of ships, occupying thousands of pixels, processes the information generated during the coregistration of several re-synthesized time-domain and not overlapped Doppler sub-apertures. Specifically, the SAR products are generated by splitting the raw data according to a temporally small baseline using one single wide-band staring spotlight (ST) SAR image. The predominant vibrational modes of different ships are then estimated. The performance analysis is conducted on one ST SAR image recorded by COSMO-SkyMed satellite system. Finally, the newly proposed approach paves the way for application to the surveillance of land-based industry activities.

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