In consideration of the difficulty for ship detection in SAR images when the ship targets are blurred in speckle noise and clutter, a novel method for ship detection is proposed in this paper. In this approach, the discrete Shearlet features are adopted to capture the intrinsic geometrical features of ship target with discontinuities points and threshold detection method is used to get the ship targets. The SAR image is decomposed by Discrete Shearlet Transform (DST) in multiple scales to get different sub-bands, and the Shearlet coefficients of images are obtained in different sub-bands with different directions. As Shearlet coefficients of the target and the background have completely different performance properties in the high-frequency sub-bands in different directions. The Shearlet coefficients of the ship targets exhibit local maxima characteristics in high-frequency subbands in different directions, while the extreme values of Shearlet coefficients in the background are difficult to simultaneously appear in different directions. Experiments on SAR images with sea backgrounds and multiple ship targets situation have been performed. Comparison with wavelet and CFAR detection methods, the results demonstrate that the proposed method is competitive in detection rate and shape preservation.
This paper presents the design, data processing and experimental results of the C band SAR developed at the Institute of Electronics, Chinese Academy of Sciences (IECAS). This C band SAR aims at oil spill monitoring and adopts a compact, low power consumption and lightweight design based on microstrip antenna, solid state power amplifier and 2- axis gimbals allowing installation on UAV. To further process SAR images, the ground support system is employed to accomplish SAR image processing, geometric correction and geocoding, annotation and shape characteristics analysis of pollution. The first flight test has been performed using C band SAR on Harbin Y-12. Large area SAR image with pollution region of sea surface was obtained. With the ground support system, perimeter, area, shape complexity factor and center latitude and longitude coordinates of pollution region are presented in the monitoring report. The experimental results demonstrate the feasibility of C band SAR and the usability of ground support system in marine monitoring. In the future, the C band SAR should be mounted on the UAV platform to perform oil spill monitoring test.
In addition to the inherent speckle noise, the Synthetic Aperture Radar (SAR) images exhibit low contrast and low signal-to-noise ratio (SNR), in some cases such as ocean monitoring with fierce wind. Given this problem, a novel method for target feature enhancement based on Discrete Shearlet Transform (DST) and multi-scale analysis theory is proposed in this paper. This approach captures the intrinsic geometrical features of target with discontinuities points in the SAR images effectively. In this work, the SAR image is decompose in multiple scales to get different sub-bands, the shearlet coefficients of images in different sub-bands with different directions are fusion. As the scale increases, the shearlet coefficient maximum of the target also increases, while the shearlet coefficient maximum of the speckle and clutter decreases. Therefore, the high-frequency features of different scales in different directions are fused, which makes not only the target enhanced but also the speckle and clutter suppressed. Experiments on ocean SAR images with strong speckle and clutter have been performed. Comparison with traditional wavelet approach, the results demonstrate that the proposed method is competitive in target feature enhancement and clutter suppression.
Automatic and fast image interpretation is important for Synthetic Aperture Radar (SAR) image exploitation. Generally,
image interpretation refers to target detection and information extraction. To generate information report automatically
and efficiently, we developed and demonstrated a fast and automatic information system for SAR image in this paper.
The proposed information system consists of four components respectively used for detection, discrimination,
information production and database management. To evaluate the performance of the system, we tested it on our own
SAR images placed tank targets downloaded from the DARPA MSTAR public distribution. Experiment results show the
proposed system is effective and advantageous in real-time applications.