Research And Application Of The Methods In Removing Cloud Of Remote Sensing Image

The research work of cloud that makes the image quality lower is more important with the development of the optical images application, in particular, of the remote sensing data application. We can not capture the information from the most remote sensing data because there is cloud. So the methods of removing cloud from the remote sensing image must be researched. There are several methods for removing cloud in some conditions and they suit for back and white remote sensing images, such as the methods of Multi-spectrum image, Multi-image interpolation, Multi-source data fusing, and the method of Homomorphic filtering. The concrete methods of removing cloud for a single remote sensing image without any pre-information need research.The research of this paper consisted of three aspects, which are described of image elements, neighborhood regions and wavelet transform.First, the image model of the cloud noise had been build, and the sharp characteristics of the cloud noise had been analyzed. The paper suggested the new method for extracting cloud region and its shadow region. The new method is the method of Huyghens secondary. The local information of the remote sensing images in cloud and its shadow could be protruded by the new method. Considered the gradation various characteristics of the cloud, another method that was the method of higher-degree polynomial curve transform had been suggested. The hidden information in cloud region was exposed by the method.Second, the original quality of Homomorphic filtering method that removed lower frequency signal of original images was analyzed. The cloud in the remote sensing image is just the lower frequency signal of original images. It was researched that the Retinex theory proposed by E. Land on December 30, 1963. The algorithms of Retinex were studied by this paper. In particular, the Center / Surround algorithm that proposed by D. J. Jobson, Zia-ur Rahman, G. A. Woodell (JRW) was considered. The physics original quality of Retinex had been revealed. The advanced multi scale Retinex (AMSR) algorithm was put forward: After the image enhanced by multi scale Retinex the truncate interval of [μ- kσ,μ+ kσ] near the mean brightnessμin k times standard deviationσrange had been elected and stretched. If the production of the brightness multiply with the contrast degree for the stretched image was in [6 000, 10 000], the stretched image has high quality. As the same time, the conclusion has been proved: The method of highest quality for the underexposure color image was that it selected k=1 time standard deviation range near the mean brightness and stretched after enhanced by multi scale Retinex.In the process for the application of advanced multi scale Retinex algorithm in remote sensing images, there are three steps. The first step elected the complementary color image of the original image. The second step applied the method of the advanced multi scale Retinex algorithm to enhance the information in dark region that was just the cloud region. The last step elected the complementary color image of the enhanced image by the advanced multi scale Retinex. The results of experiments shown the method was effective.Finally, two of the application fields of the removing cloud methods had practiced. The cloud regions had been extended to the shadow of the grant builds under the sun. The method of removing cloud had been extended to remove fog, and so on. In the first application, this paper extracted the edge of pyramids in Egypt based on Canny algorithm and programs. The programs were in a software package that developed by our theme group was used to extract the shadow regions in remote sensing images. The shadow regions were placed by the same regions that enhanced by multi scale Retinex algorithm and the more information in the shadow was showed. The software package was improved. The characteristic point idea was proposed and the distinguish method between the real-broken point and quasi-broken point was described. The closed regions in the remote sensing images were extracted effectively. Another application was that the edge details were extracted in real time with wavelet transform based on DSP. The results of extracting edge details that used the project software programmed by CCS were good. The application was general preparing for the issues of removing fog in real time based on DSR...