Uncertainty Information Processing Research Of Remote Sensing Image In Land-use Survey

Land-use survey in field consume time,energy and money efficiency is low. Today, with the development of remote sensing and geographic information system technology, making full use of these high-tech achievements to survey land resources has become possible for the people and by consensus.The uncertainty of remote sensing information will affect the remote sensing seriously, such as the function, the efficiency and the flexibility, which restricts the remote sensing information products and the further development.Taking the QuickBird image data of the Hongshui River valley Bute-you township of Ceheng County for an example, analyzing the uncertainty and the reasons in land-use information extraction, at the same time, in combination with other information, making remote sensing, geographical information systems as the main technical tools in the course of processing uncertainty information of images, which is more targeted and relative effectiveness, not only improve the quality of remote sensing image, enhance the GIS data quality obtained from the image data, but also reduce Image recognition errors.The main conclusions are as follows:(1) Making use of remote sensing image processing software, combined with topographic maps, GPS measurement points, DEM, and other relevant information to carry out geometric precision correction and ortho-corrected of the QuickBird images, which improved the data quality, reduced uncertainty of the image on the study area.(2) Using quantitative analysis method to analyse the remote sensing data calculating the standard deviation,entropy, space-related factors and spectral correlation coefficient of the band, found that the PCA analysis integration was the best way by contrast.The standard deviation, entropy and the correlation coefficient were higher than several other algorithms, after fusing, image in the visual, maintaining the spatial details information were better.In order to highlight vegetation, using the formula (float (b2)+float (b4))/2 to calculate ban information,then,the incomplete,uncertainties and the existence of more justice in the different environment were reduced.(3) Using remote sensing image processing software and MATLAB to process remote sensing images-noiseing coverd by little cloud. Using two methods to remove cloud, based on the R, G, B channels and HIS transformation, HIS-based transformation obtained better results, which made the bottom features clearly.The borders were more clear, the internal texture was more rich, facilitating the further accurate identification and interpretation.At the same time, making part of the image spectrum lost, cloud-free image in the background details were weakened. Based on the R, G, B-access to remove cloud, which improved the information-resolution of images, images background information was more abundant.(4)To Solute the uncertainty brought by the texture characteristics, using the geo-information for auxiliary processing. Features point, line were added to the TIN to generate high-precision digital elevation model. The river network information were generated by depression-free digital elevation model and the slope map were extracted by digital elevation model. The river network information, slope map and remote sensing images were superimposed analysis to determine the distinction between water erosion and the river,the steep slopes and the dryland. Useing the network river to determine the river and the water erosion, the accuracy rate was 100%. Useing slope map to distinguish between perennial dry and steep slopes, the accuracy of steep slopes was 92.1 percent, the accuracy of perennial dry was 94.2 percent.(5)Through the analysis of the spectral characteristics and mixed-pixel, found that the spatial distribution of the uncertainty are more on the broken terrain or a variety types of features at the junction. To reduce the uncertainty problem brought by "with the spectrum of differences", " Spectrum foreign body " or the mixed-pixel, using Model builder in ARCGIS for visual analysis, through three-dimensional visual, discussing the laws of knowledge reflected by spatial information. A number of space operations are concentrated in a hierarchical model to extract every category, make full use of geometry and structure information for analysis, reducing the uncertainty bring by "differences of the same spectrum", "foreign body in the same spectrum" and mixed-pixel.