Advertisement
Published in:
2015 | OriginalPaper | Chapter
Research of Remote Sensing Image Compression Technology Based on Compressed Sensing
Authors : T Yu, Shujun Deng
Published in: Advances in Image and Graphics Technologies
Publisher: Springer Berlin Heidelberg
Activate our intelligent search to find suitable subject content or patents.
Select sections of text to find matching patents with Artificial Intelligence. powered by
Select sections of text to find additional relevant content using AI-assisted search. powered by
Compressed Sensing (CS) theory is a new method of signal acquisition and processing proposed in recent years. With small amount of sampling data recovering original data to precisely reconstruct sparse signal or compression signal, the theory breaks though the restriction of Nyquist sampling theorem. CS can avoid enormous sampling data waste but also reduce the complexity of image coding. This paper reviews the basic theory of CS and its three key points, including signal sparse representation, design of measurement matrix and reconstruction algorithms. Then, the application of CS in the field of remote sensing image compression technology is studied. Using MATLAB software, we do a series of CS emulation experiments compared with the traditional compression methods. The results show that the proposed method has a good performance on the remote sensing image compression.