The Object-oriented Classification And Application Of High-resolution Remote Sensing Image Based On The Optimal Scale

Now days, application of high resolution remote sensing image data, e.g. QuickBird, GeoEye-l,in land managing, planning and concerned application fields has been popularized increasingly. Generally, the classifacation of remote sensing images are significant to thematic information extraction, dynamic change detecting and thematic mapping. For actual demand in production, it’s reasonable to introduce high-efficiency approaches in generating information from the high resolution images. Compared with low resolution images, more spectrum, shape, texture, context and topology information are included in high resolution images, leading to inadaption of conventional information extraction methods that are on basis of image pixels. However, the object oriented method is capable to conquer such drawbacks in conventional methods for its adaptive capability in dealing with scale parameters of high resolution images because of the strategy of image segmentation.image segmentation is the premise and basis in object oriented information classification and extraction, and it is possible for image segmentation to significantly influence the accuracy of extracted information. Dependently, different segmentation parameters and scales lead to distinct segmented results (calling image objects), thus the appropriate parameters and scales must be determined in order to make the information extracting more efficient and accurate.In this paper, we analyze the pixel based and object oriented information extraction theory and their current research situation firstly, and indicate that the introducing of object oriented method in image analysis is to adapt the enhancement of image resolution. Based on the image segmentation parameter quantitative experiments, the role of each segmentation parameter in image object generating is summed up, and then the classification of visual interpretation method and optimal segmentation effect principle are used to demonstrate that the improved area contrast method can quickly calculate a feature optimal segmentation scale, furthermore, we produce an integrated strategy for information extraction with object oriented method: set a small scale for smaller features and the features are subsequently extracted; merge the smaller image objects in a small segmentation scale based on the homogeneity judgment standard, and the bigger features are then extracted; according to various extraction regulations, set up a rule mode based on spectral and texture information in sequence. Then, the classification of the study area is done used pixel oriented and object oriented technology,and the confusion matrix, Kappa coefficient and other indicators are examined to evaluate the accuracy of extraction results, respectively, verifying the advantages of object oriented information extraction technology in the processing of high resolution remote sensing images. Finally, we use the mechanism of remote sensing and the shadow correction method to compensate and restore the information in shadow areas, which is extracted by object-oriented information extraction technology from the second study area, Then use the object-oriented approach to re-image classification.from that it could improve the classification results of the application.