Methods And Applications Of Remote Sensing Image Quality Improvement

With the rapid development of satellite remote sensing technology, people increase the demanding of the remote sensing imaging quality. In recent years, with the continuous development of technology, there is a great demand on high quality and high resolution image in national security and scientific research. In recent years, how to get the clear images which meet the application requirements, has become a hot topic at home and abroad.In the process of image acquisition, transmission and storage, impulse noise is inevitably introduced into remote sensing images. Impulse noise not only reduces the image visual quality but also affects the practical applications. Impulse noise removal is an indispensable step in remote sensing image quality improvement. The procedure of the existing denoising algorithms includes: detecting impulse noise from corrupted image; using noise-free pixels within a local window to calculate the centered noised pixel in a statistical way; traversing the whole noised image using sliding window to restore clear image. Although these methods have made significant de-noising effect, but there is still much room for improvement. First, the denoising effect of this kind of algorithm is very dependent on the accuracy of noise detection. Therefore, the existing methods always spend a lot computational expense in designing high precision and accuracy noise detection methods. Second, such algorithms only consider the local statistical characteristics of the image while ignoring the non-local similarity of the image. The image self-similarity is not fully utilized.Aiming at those problems, this paper proposes an impulse noise removal method based on non-local to local means. The key distinction between the proposed method and existing methods is that this method effective use of the image self-similarity. The first step is noise detection. Then, non-local means is used to remove noise on noisy pixels, and the nonlocal similar information was subjected to analysis. Then, the improved local means denoising algorithm is used again to achieve better denoising effect. The algorithm is robust to detection accuracy. So, adding a simple histogram noise detection algorithm can greatly increase the use of noise-free pixels, and better protect the image details and texture feature. The experimental results demonstrate that the proposed method produces better results than existing denoising methods both subjectively and objectively.Another main factor to the decline in remote sensing image quality is image blur caused by a variety of system parameter error. Countries are investing heavily to do a lot of research to enhance remote sensing image quality, and pay a lot of manpower and material resources. It makes the camera resolution of remote sensing imaging system has reached a high level. However, improving lens resolution makes image motion blur effect on image quality is getting worse. The influence of the sub-pixel level blur caused by various system parameters errors can not to be ignored,and become the main reason for the high resolution remote sensing image. How to reduce the sub-pixel level blur caused by various system parameters errors is the key to improve the quality of remote sensing images.As the existing blur detection system can not reach the sub-pixel level precision, and can't restore the various system parameters errors which cause the coupling blur. To solve this problem, in this paper, taking TDI-CCD as an example, first, for a system parameter error, a controlled signal is sent to the remote camera system, to increase the system parameter error. In this way, the blur length caused by this system parameter error would be increased to a suitable value, which can be detected effectively, and stand out from the coupling aliasing blur. Then, estimating motion value from the blurred image, the system parameter error is derived. Finally, the estimated of the system parameter error is send to the spaceborne camera system as a second control signal, to carry out the parameter correction, from the front of the system to improve camera image quality. Through experiment simulation data, this paper verify the feasibility of this method, and its detection precision is not affected by other sub-pixel level system error when improve one kind of parameter errors alone,. It is detection error can be reduced to less than 0.1.In summary, aiming at the problem of how to improve the quality of remote sensing image, this paper do the research and analysis respectively to remove noise and accurately detect image motion. We can effectively remove the impulse noise in remote sensing image,while reduse the fuzzy degree of remote sensing images from the source.