Fractal Dimension based SAR Image Sparse Degrees Estimation
Hua Bo, Haiyun Gu, Lei Ren, Hong Xie
Available Online December 2016.
- https://doi.org/10.2991/iceeecs-16.2016.115How to use a DOI?
- Compressive sensing, Measurement matrix, Sparsity, Fractal dimension, SAR image
- Compressed sensing is surprisingly predicts that images, which allow a sparse representation by a suitable basis or a frame, can be recovered from what was previously considered highly incomplete linear measurements by using efficient algorithms. It is a novel research area of SAR images processing. As a result of the design of the observation matrix estimate depends on the image sparse degree, therefore, the estimation precision of the image sparse degree is an important factor to whether can accurate reconstruction images. In this paper, the relationship between the degrees of SAR image sparse and fractal dimension is researched. The experimental results show that the SAR image fractal dimension is proportional to the sizes of the observation matrix, which can be used to set the parameters of the observation matrix.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
TY - CONF AU - Hua Bo AU - Haiyun Gu AU - Lei Ren AU - Hong Xie PY - 2016/12 DA - 2016/12 TI - Fractal Dimension based SAR Image Sparse Degrees Estimation BT - 2016 4th International Conference on Electrical & Electronics Engineering and Computer Science (ICEEECS 2016) PB - Atlantis Press SN - 2352-538X UR - https://doi.org/10.2991/iceeecs-16.2016.115 DO - https://doi.org/10.2991/iceeecs-16.2016.115 ID - Bo2016/12 ER -