SAR Image Segmentation Based on Wavelet Transform Combined with Gabor Filter and SDD Threshold Selection
文献类型：会议论文
| 作者 | Liu YY(刘宇阳)1,2; Wang ZZ(王振洲)2 ; Cong Y(丛杨)2
|
| 出版日期 | 2018 |
| 会议日期 | August 1-4, 2018 |
| 会议地点 | Toyama, JAPAN |
| 页码 | 1-7 |
| 英文摘要 | The synthetic aperture radar (SAR) image is generated by an aperture radar system that can show the observation of the object information without touching the target. One of the key issues in the Extraction and segmentation of the key targets in SAR images containing a lot of feature information. The Gabor ¯lter uses its own texture features and image texture features to response to distinguish textures. It is very suitable for SAR images with obvious texture features. Inspired by this, this paper proposes a SAR image segmentation method based on Wavelet Transform combined with Gabor ¯lter and Slop di®erence distribution (SDD) threshold selection. It contains the following parts: 1) Use the Gabor ¯lter to segment the image's texture feature; 2) Use texture smoothing as image preprocessing and use a robust SDD threshold selection method to segment the gray feature of the image; 3) Use Wavelet Transform combined two segmented images to obtain the ¯nal target segmentation. For segmentation of di®erent types of SAR images, this method has strong robustness. By manually adjusting the parameters in the SDD method and the weighting parameters in the fusion process, more accurate segmentation results can be obtained. |
| 产权排序 | 1 |
| 会议录 | 2018 Progress In Electromagnetics Research Symposium (PIERS)
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| 语种 | 英语 |
| 源URL | [http://ir.sia.cn/handle/173321/26062]  |
| 专题 | 沈阳自动化研究所_机器人学研究室 |
| 通讯作者 | Cong Y(丛杨) |
| 作者单位 | 1.University of Chinese Academy of Sciences, China 2.State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, China |
| 推荐引用方式 GB/T 7714 | Liu YY,Wang ZZ,Cong Y. SAR Image Segmentation Based on Wavelet Transform Combined with Gabor Filter and SDD Threshold Selection[C]. 见:. Toyama, JAPAN. August 1-4, 2018. |
入库方式： OAI收割
来源：沈阳自动化研究所
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