Research Of Classification For Mountain Remote Image

To obtain useful information from the remote sensing data has become a scientific puzzle from the remote sensing technology appearing.What information technology should be applied to obtain more accurate quantitative and multi-scale information has come to the research emphasis of remote sensing data.The classification research was most basic in the remote sensing information extraction.At present,the classification precision of remote sensing image data information was not high for the characteristics that the data volume was enormous and the information was complex.To increase the classification accuracy of the Zhang Jiakou mountain remote sensing.The three sections were studied and discussed.Firstly,the geometric distortion and radiation deviation were impossibly appeared at the imaging process of remote sensing has been systematic researched and the reasons has been analyzed.The Flat Field method was one of atmospheric correction ways has been used to redress radiation deviation and the geometric correction for remote sensing has been realized to eliminate the geometric distortion.The Zhang Jiakou panchromatic remote sensing image as the rectification image and the multi-spectral image has been coalesced to improve the image contrast and sharpness.The noises existing in fusion image have been eliminated by adaptive median filter based on noise detection.Secondly,the mixed pixels has been decomposed by independent component analysis algorithm,it effectively avoided the image pixels' misclassification and omission caused by different spectrum characteristics with the same objects and foreign objects with the same spectrums and reduced the correlation and redundancy between any two band images.Thirdly,the Iterative Self-Organizing Data Analysis Technique(ISODATA)and Bayesian classification algorithms principle has been analyzed,and the classification results of Zhang Jiakou mountain remote sensing image's has been evaluated.According to the features of vagueness and uncertainty of remote sensing image analysis,the fuzzy c-means clustering was proposed to achieve the classification result which were influenced the classification category number and initial clustering centers parameters,so the value of the two parameters were confirmed through constructing fuzzy equivalence matrix.Because using fuzzy c-means clustering algorithm needed to continuously calculate the distance between all pixels and clustering centers and the data volume was very big,the image segmentation was indispensable to increase the operating efficiency of the classification algorithm before achieving the classification.The over-segmentation problem would aroused by directly using marker-based watershed algorithm to decollate image,so the multi-scale morphological reconstruction technology has been put forward to relative the smoothly processing before it.