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A Novel Harris Feature Detection-Based Registration for Remote Sensing Image

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Abstract

In view of the significant intensity difference between remote sensing image pairs, weak robustness, and insufficient key point correspondence, the novel remote sensing image registration method is proposed. Firstly, a nonlinear scale space is established by means of the anisotropic diffusion equation and fast explicit diffusion. Then, an improved gradient calculation method is used to calculate the gradient amplitude of the nonlinear scale-space image to establish the gradient amplitude space of the nonlinear scale space, and the multiscale Harris method is used to detect the feature points in the gradient amplitude space. The experimental results show that this feature extraction method can consider the boundaries and smoothness of objects and reduce the problem of gray-level difference to increase the number of feature points with potential of being correctly matched, and the distribution of feature points is relatively uniform. In addition, the improved gradient calculation method can effectively reduce the impact of nonlinear intensity differences on image registration. Overall, the algorithm can effectively solve the problem of registration difficulties caused by the significant grayscale difference between multisource remote sensing images and enhance the robustness. Compared with other advanced algorithms, this one has higher accuracy and more correct correspondence relations, and the registration performance has been significantly improved.

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References

  • Alcantarilla, P. F., Bartoli, A., & Davidson, A. J. (2012). KAZE features, pp. 214–227.

  • Alcantarilla, P. F., Nuevo, J., & Bartoli, A. (2013). Fast explicit diffusion for accelerated features in nonlinear scale spaces. In BMVC 2013—Electronic Proceedings of the British Machine Vision Conference 2013 (pp. 13.1–13.11).

  • Bay, H., Tuytelaars, T., & Van Gool, L. (2006). SURF: Speeded up robust features (pp. 404–417).

  • Cai, L., Shi, W., Hao, M., Zhang, H., & Gao, L. (2018). A multi-feature fusion-based change detection method for remote sensing images. Journal of the Indian Society of Remote Sensing, 46(12), 2015–2022.

    Article  Google Scholar 

  • Chen, S., Li, X., Zhao, L., & Yang, H. (2018). Medium-low resolution multisource remote sensing image registration based on SIFT and robust regional mutual information. International Journal of Remote Sensing, 39(10), 3215–3242.

    Article  Google Scholar 

  • Chertock, A., Kurganov, A., & Petrova, G. (2009). 4L:Lll8–1139. 17. (314), 2009.

  • Dou, J., Qin, Q., & Tu, Z. (2018). Robust image matching based on the information of SIFT. Optik, 171, 850–861.

    Article  Google Scholar 

  • Fan, J., Wu, Y., Wang, F., Zhang, Q., Liao, G., & Li, M. (2014). SAR image registration using phase congruency and nonlinear diffusion-based SIFT. IEEE Geoscience and Remote Sensing Letters, 12(3), 562–566.

    Google Scholar 

  • Guo, Q., He, M., & Li, A. (2018). High-resolution remote-sensing image registration based on angle matching of edge point features. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 11(8), 2881–2895.

    Article  Google Scholar 

  • Kupfer, B., Netanyahu, N. S., & Shimshoni, I. (2015). An efficient SIFT-based mode-seeking algorithm for sub-pixel registration of remotely sensed images. IEEE Geoscience and Remote Sensing Letters, 12(2), 379–383.

    Article  Google Scholar 

  • Li, Q., Qi, S., Shen, Y., Ni, D., Zhang, H., & Wang, T. (2015). Multispectral image alignment with nonlinear scale-invariant keypoint and enhanced local feature matrix. IEEE Geoscience and Remote Sensing Letters, 12(7), 1551–1555.

    Article  Google Scholar 

  • Li, Q., Wang, G., Liu, J., & Chen, S. (2009). Robust scale-invariant feature matching for remote sensing image registration. IEEE Geoscience and Remote Sensing Letters, 6(2), 287–291.

    Article  Google Scholar 

  • Lowe, D. G. (2004). Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision, 60(2), 91–110.

    Article  Google Scholar 

  • Ma, D., & Lai, H. (2019). Remote sensing image matching based improved ORB in NSCT domain. Journal of the Indian Society of Remote Sensing, 47(5), 801–807.

    Article  Google Scholar 

  • Ma, W., Wen, Z., Wu, Y., Jiao, L., & Member, S. (2017). Remote sensing image registration with modified. IEEE Geoscience and Remote Sensing Letters, 14(1), 3–7.

    Article  Google Scholar 

  • Michel, J., Tupin, F., Gousseau, Y., Delon, J., & Dellinger, F. (2014). SAR-SIFT: A SIFT-like algorithm for SAR images. IEEE Transactions on Geoscience and Remote Sensing, 53(1), 453–466.

    Google Scholar 

  • Mikolajczyk, K., & Schmid, C. (2005). A performance evaluation of local descriptors. IEEE Transactions on Pattern Analysis and Machine Intelligence, 27(10), 1615–1630.

    Article  Google Scholar 

  • Perona, P., & Malik, J. (1990). Scale-space and edge detection using anisotropic diffusion. IEEE Transactions on Pattern Analysis and Machine Intelligence, 12(7), 629–639.

    Article  Google Scholar 

  • Qu, Z., Bu, W., & Liu, L. (2018). The algorithm of seamless image mosaic based on A-KAZE features extraction and reducing the inclination of image. IEEJ Transactions on Electrical and Electronic Engineering, 13(1), 134–146.

    Article  Google Scholar 

  • Weixing, W., Ting, C., Sheng, L., & Enmei, T. (2015). Remote sensing image automatic registration on multi-scale Harris–Laplacian. Journal of the Indian Society of Remote Sensing, 43(3), 501–511.

    Article  Google Scholar 

  • Wu, Y., Ma, W., Gong, M., Su, L., & Jiao, L. (2014). A novel point-matching algorithm based on fast sample consensus for image registration. IEEE Geoscience and Remote Sensing Letters, 12(1), 43–47.

    Article  Google Scholar 

  • Ye, F., Su, Y., Xiao, H., Zhao, X., & Min, W. (2018). Remote sensing image registration using convolutional neural network features. IEEE Geoscience and Remote Sensing Letters, 15(2), 232–236.

    Article  Google Scholar 

  • Zhu, X., & Bao, W. (2019). Investigation of remote sensing image fusion strategy applying PCA to wavelet packet analysis based on IHS transform. Journal of the Indian Society of Remote Sensing, 47(3), 413–425.

    Article  Google Scholar 

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Acknowledgements

This work is financially supported by the National Natural Science Foundation of China (Nos. U1803261 and U1903213).

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Authors and Affiliations

  1. College of Information Science and Engineering, Xinjiang University, Tianshan District, Ürümqi, 830046, China

    Yali Wang, Huicheng Lai, Zhenhong Jia & Liejun Wang

  2. Autonomous Region Key Laboratory of Signal Detection and Processing, Ürümqi, China

    Huicheng Lai

  3. College of Electronic Engineering, Tsinghua University, Haidian District, Beijing, 100084, China

    Hongbing Ma

Authors
  1. Yali Wang
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  2. Huicheng Lai
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  3. Hongbing Ma
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  4. Zhenhong Jia
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  5. Liejun Wang
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Correspondence to Huicheng Lai.

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Wang, Y., Lai, H., Ma, H. et al. A Novel Harris Feature Detection-Based Registration for Remote Sensing Image. J Indian Soc Remote Sens 48, 1245–1252 (2020). https://doi.org/10.1007/s12524-020-01151-2

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  • DOI: https://doi.org/10.1007/s12524-020-01151-2

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