[1]许可乐,唐涛,蒋咏梅.一种SAR图像稳健特征点提取方法[J].智能系统学报,2013,8(4):287-298.[doi:10.3969/j.issn.1673-4785.201304038]
XU Kele,TANG Tao,JIANG Yongmei.A stable feature point extraction approach for SAR image registration[J].CAAI Transactions on Intelligent Systems,2013,8(4):287-298.[doi:10.3969/j.issn.1673-4785.201304038]
点击复制
XU Kele,TANG Tao,JIANG Yongmei.A stable feature point extraction approach for SAR image registration[J].CAAI Transactions on Intelligent Systems,2013,8(4):287-298.[doi:10.3969/j.issn.1673-4785.201304038]
一种SAR图像稳健特征点提取方法
《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷:
8
期数:
2013年第4期
页码:
287-298
栏目:
学术论文—机器学习
出版日期:
2013-08-25
- Title:
- A stable feature point extraction approach for SAR image registration
- 文章编号:
- 1673-4785(2013)04-0287-05
- Keywords:
- SAR; image registration; feature extraction; SIFT; Gabor filter
- 分类号:
- TP751.1
- 文献标志码:
- A
- 摘要:
- 针对合成孔径雷达(SAR)图像自动配准问题，提出了一种新的SAR图像特征点提取方法.先对SAR图像灰度值进行对数变换处理，将乘性噪声转化为加性噪声，然后利用Gabor滤波器取代高斯滤波器建立尺度空间，使SAR图像在低尺度仍较好地保留细节，增加了提取特征点数目，并设置了对比度双门限，有效地抑制了伪特征点，从而提高SAR图像配准的精度和速度.实验结果表明，SAR图像稳健特征点提取方法是有效的.
- Abstract:
- For the automatic registration of a synthetic aperture radar(SAR) image, we propose a feature point extraction approach. First, by examining the logarithmic transform for the grey level of the SAR image, it was discovered that the multiplicative noise can be transformed into the additional noise. Then the scale space of the image was constructed by substituting multiscale Gabor filter for Gaussian filter, which reveals the SAR image still has details better in a low scale, and increases the number of extracted feature points. Further, the double thresholds for contrast ratio are set up to discard the false feature point effectively, thereby increasing the precision and speed of SAR image registration. The results of the experiments demonstrate the applicability of approach to find feature points for stable SAR image registration.
- 参考文献/References:
-
［1］LOWE D G. Distinctive image features from scaleinvariant keypoints［J］. International Journal of Computer Vision, 2004, 60(2): 91-110.
［2］XIONG B, CHEN J M, KUANG G, et al. Estimation of the repeat-pass ALOS PALSAR interferometric baseline through direct least square ellipse fitting［J］. IEEE Transactions on Geoscience and Remote Sensing, 2012, 50(9): 1-8.
［3］SURI S, REINARTZ P. Mutualinformation-based registration of TerraSAR-X and Ikonos imagery in urban areas［J］. IEEE Transactions on Geoscience and Remote Sensing, 2010, 48(2): 939-949.
［4］KE Y, SUKTHANKAR R. PCA-SIFT: a more distinctive representation for local image descriptors［C］//Proceedings of Conference on Computer Vision and Pattern Recognition. Washington DC, USA, 2004: 1980-1987.
［5］LI Q L, WANG G Y, LIU J G, et al. Robust scale-invariant feature matching for remote sensing image registration［J］. IEEE Geoscience and Remote Sensing Letters, 2009, 6(2): 287-291.
［6］PALENICHKA R M, ZAREMBA M B. Automatic extraction of control points for the registration of optical satellite and LiDAR images［J］. IEEE Transactions on Geoscience and Remote Sensing, 2010, 48(7): 2864-2879.
［7］LU G, YAN Y, KOU Y, et al. Image registration based on criteria of feature point pair mutual information［J］. IET Image Process, 2011, 5(6): 560-566.
［8］SCHWIND P, SURI S, REINARTZ P, et al. Applicability of the SIFT operator for geometrical SAR image registration［J］. International Journal of Remote Sensing, 2010, 31(8): 1959-1980.
［9］WANG Shanhu, YOU Hongjian, FU Kun. BFSIFT: a novel method to find feature matches for SAR image registration［J］. IEEE Geoscience and Remote Sensing Letters, 2012, 9(4): 649-653.
［10］雷琳, 王壮, 粟毅. 基于多尺度Gabor滤波器组的不变特征点提取新方法［J］. 电子学报, 2009, 37(10): 2314-2319.?
LEI Lin, WANG Zhuang, SU Yi. A new invariant feature detector based on multiscale Gabor filter bank［J］. Acta Electronica Sinica, 2009, 37(10): 2314-2319.
［11］LIU Li, FIEGUTH P. Texture classification from random features［J］. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2011, 10(5): 1109-1120.
［12］DUAN C, MENG X, TU C，et a1． How to make local image features more eficient and distinctive［J］． IET Computer Vision， 2008, 2(3): 98-102.
- 相似文献/References:
-
[1]胡光龙,秦世引.动态成像条件下基于SURF和Mean shift的运动目标高精度检测[J].智能系统学报,2012,7(1):61.
HU Guanglong,QIN Shiyin.High precision detection of a mobile object under dynamic imaging based on SURF and Mean shift[J].CAAI Transactions on Intelligent Systems,2012,7():61.
[2]王鑫,田甜,田金文.基于背景建模的VideoSAR动目标阴影检测方法[J].智能系统学报,2022,17(1):59.[doi:10.11992/tis.202107019]
WANG Xin,TIAN Tian,TIAN Jinwen.Moving target shadow detection in VideoSAR based on background modeling[J].CAAI Transactions on Intelligent Systems,2022,17():59.[doi:10.11992/tis.202107019]
[3]黄粤豫,周航,陈业泓,等.借助弱纹理匹配的TEDS车底故障区域定位算法[J].智能系统学报,2024,19(3):670.[doi:10.11992/tis.202303006]
HUANG Yueyu,ZHOU Hang,CHEN Yehong,et al.TEDS underbody fault location algorithm in virtue of weak texture matching[J].CAAI Transactions on Intelligent Systems,2024,19():670.[doi:10.11992/tis.202303006]
备注/Memo
收稿日期：2013-04-15. 网络出版日期：2013-06-03.
通信作者：许可乐. E-mail：xukelele@163.com.
作者简介：
许可乐，男，1990年生，硕士研究生，主要研究方向为图像处理 .
更新日期/Last Update:
2013-09-23