Vol. 18
Latest Volume
All Volumes
PIERM 126 [2024] PIERM 125 [2024] PIERM 124 [2024] PIERM 123 [2024] PIERM 122 [2023] PIERM 121 [2023] PIERM 120 [2023] PIERM 119 [2023] PIERM 118 [2023] PIERM 117 [2023] PIERM 116 [2023] PIERM 115 [2023] PIERM 114 [2022] PIERM 113 [2022] PIERM 112 [2022] PIERM 111 [2022] PIERM 110 [2022] PIERM 109 [2022] PIERM 108 [2022] PIERM 107 [2022] PIERM 106 [2021] PIERM 105 [2021] PIERM 104 [2021] PIERM 103 [2021] PIERM 102 [2021] PIERM 101 [2021] PIERM 100 [2021] PIERM 99 [2021] PIERM 98 [2020] PIERM 97 [2020] PIERM 96 [2020] PIERM 95 [2020] PIERM 94 [2020] PIERM 93 [2020] PIERM 92 [2020] PIERM 91 [2020] PIERM 90 [2020] PIERM 89 [2020] PIERM 88 [2020] PIERM 87 [2019] PIERM 86 [2019] PIERM 85 [2019] PIERM 84 [2019] PIERM 83 [2019] PIERM 82 [2019] PIERM 81 [2019] PIERM 80 [2019] PIERM 79 [2019] PIERM 78 [2019] PIERM 77 [2019] PIERM 76 [2018] PIERM 75 [2018] PIERM 74 [2018] PIERM 73 [2018] PIERM 72 [2018] PIERM 71 [2018] PIERM 70 [2018] PIERM 69 [2018] PIERM 68 [2018] PIERM 67 [2018] PIERM 66 [2018] PIERM 65 [2018] PIERM 64 [2018] PIERM 63 [2018] PIERM 62 [2017] PIERM 61 [2017] PIERM 60 [2017] PIERM 59 [2017] PIERM 58 [2017] PIERM 57 [2017] PIERM 56 [2017] PIERM 55 [2017] PIERM 54 [2017] PIERM 53 [2017] PIERM 52 [2016] PIERM 51 [2016] PIERM 50 [2016] PIERM 49 [2016] PIERM 48 [2016] PIERM 47 [2016] PIERM 46 [2016] PIERM 45 [2016] PIERM 44 [2015] PIERM 43 [2015] PIERM 42 [2015] PIERM 41 [2015] PIERM 40 [2014] PIERM 39 [2014] PIERM 38 [2014] PIERM 37 [2014] PIERM 36 [2014] PIERM 35 [2014] PIERM 34 [2014] PIERM 33 [2013] PIERM 32 [2013] PIERM 31 [2013] PIERM 30 [2013] PIERM 29 [2013] PIERM 28 [2013] PIERM 27 [2012] PIERM 26 [2012] PIERM 25 [2012] PIERM 24 [2012] PIERM 23 [2012] PIERM 22 [2012] PIERM 21 [2011] PIERM 20 [2011] PIERM 19 [2011] PIERM 18 [2011] PIERM 17 [2011] PIERM 16 [2011] PIERM 14 [2010] PIERM 13 [2010] PIERM 12 [2010] PIERM 11 [2010] PIERM 10 [2009] PIERM 9 [2009] PIERM 8 [2009] PIERM 7 [2009] PIERM 6 [2009] PIERM 5 [2008] PIERM 4 [2008] PIERM 3 [2008] PIERM 2 [2008] PIERM 1 [2008]
2011-06-22
SAR Image Matching Method Based on Improved Sift for Navigation System
By
Progress In Electromagnetics Research M, Vol. 18, 259-269, 2011
Abstract
In order to ensure that SAR scene matching aided navigation system can acquire the position errors and yawing errors simultaneously, we propose an image matching algorithm based on Scale Invariant Feature Transform (SIFT). However, the SIFT is proposed for optical image, and its performance degrades when used in SAR image. To enhance the adaptability of SIFT, two ways are employed. One is the application of a preprocessing on image pairs before matching. The other is the establishment of a scale and rotation restriction criteria on tie-points after SIFT matching. Compared with other matching methods, experiment results show that the proposed method is much more suitable for SAR image and successes in matching performance improvement. Furthermore, the method can meet the real-time requirement.
Citation
Sanhai Ren, Wenge Chang, and Xiangjun Liu, "SAR Image Matching Method Based on Improved Sift for Navigation System," Progress In Electromagnetics Research M, Vol. 18, 259-269, 2011.
doi:10.2528/PIERM11042705
References

1. James, E. B. and A. M. Charles, "Precision aided inertial navigation using SAR and digital map data," IEEE Position Location and Navigation Symposium, 490-496, Las Vegas, NV, USA, 1990.

2. Sheng, Y. W. and D. E. Alsdorf, "Automated georeferencing and orthorectification fo Amazon basin-wide SAR mosaics using SRTM DEM data," IEEE Trans. Geosci. Remote Sens., Vol. 43, No. 8, 1929-1940, 2005.
doi:10.1109/TGRS.2005.852160

3. Mikolajczyk, K. and C. Schmid, "An affne invariant interest point detector," ECCV 2002, 128-142, Berlin Heidelberg, Germany, 2002.

4. Mikolajczyk, K. and C. Schmid, "A performance evaluation of local descriptors," IEEE Trans. Pattern Anal. Mach. Intell., Vol. 27, No. 10, 1615-1630, 2005.
doi:10.1109/TPAMI.2005.188

5. Lowe, D., "Distinctive image features from scale-invariant keypoints," Int. J. Comput. Vis., Vol. 60, 91-110, 2004.
doi:10.1023/B:VISI.0000029664.99615.94

6. Grabner, M., H. Grabner, and H. Bischof, "Fast approximated SIFT," Asian Conferonce on Comput. Vis., 918-927, Hyderabad, India, 2006.

7. Ke, Y. and R. Sukthankar, "PCA-SIFT: A more distinctive representation for local image descriptors," Proceedings of IEEE Comput. Soci. Conference on Comput. Vis. Pattern. Recog., 506-513, Washington D.C., USA, 2004.

8. Lu, J., B. Wang, H. M. Gao, and Z. Q. Zhou, "SAR images matching based on local shape descriptors," IET International Radar Conference, 235-238, Guilin, China, 2009.

9. Yi, Z., Z. G. Cao, and X. Yang, "Multi-spectral remote image registration based on SIFT," Electron. Lett., Vol. 44, No. 2, 107-108, 2008.
doi:10.1049/el:20082477

10. Bastanlar, Y., A. Temizel, and Y. Yardimci, "Improved SIFT matching for image pairs with scale difference," Electron. Lett., Vol. 46, No. 5, 346-348, 2010.
doi:10.1049/el.2010.2548

11. Xie, H., L. Pierce, and F. Ulaby, "Statistical properties of logarithmically transformed speckle," IEEE Trans. Geosci. Remote Sens., Vol. 40, No. 3, 721-727, 2002.
doi:10.1109/TGRS.2002.1000333

12. Xie, H., L. Pierce, and F. Ulaby, "SAR speckle reduction using wavelet denoising and Markov random filed modeling," IEEE Trans. Geosci. Remote Sens., Vol. 40, No. 10, 2196-2212, 2002.
doi:10.1109/TGRS.2002.802473

13. James, E. B. and A. M. Charles, "Precision aided inertial navigation using SAR and digital map data," IEEE Position Location and Navigation Symposium, 490-496, Las Vegas, NV, USA, 1990.