Information Extraction Based On High-Resolution Remote Sensing Image

With the application of the high-resolution remote sensing image more and more popular,it is urgently require people to carry on research to extraction of the high-resolution remote sensing in order to meet the increasing application and study requirement of the information of high-resolution images. However,when used the traditional pixel-oriented method to classify the high-resolution remote sensing image,it can't fully utilize image information,should reduce the precision of classification and has slow speed. According to the characteristic of the high-resolution remote sensing image,the paper proposes to use the object-oriented method to classify high-resolution remotely sensed data. T paper uses a new method to determine the scale parameter in the extraction of house.. Enhances the speed of Determination.Taken the images of ShenZhen as an example,choosing the typical urban building area and high-way ,landuse abundant area as study areas,and regarding Ecognition software as the platform ,the paper carry on the classification experiment to study areas. First ,according to the characteristic of different surface features types ,choosing the optimum scale to segment the area to extract the object,constructing the classification system,extracting the characteristics or characteristic associations of the surface feature type,adopting the fuzzy classification to classify to surface types ,then getting the extraction result of study areas. At the end, the paper compares and appraises the extraction result between the method of object-oriented and pixel-oriented (the maximum likelihood classification). The result indicates that: the extracted surface features have higher shape and attribute consistency with true surface features when used object-oriented method; It has higher precision when used object-oriented method to classify the high-resolution image; The extraction result of object-oriented analysis is more easy to understand and explain.