Application of High-Spatial IKNOS Remote Sensing Images in Land Use Classification and Change Monitoring

With the merge of the meter based high spatial remote sensing satellite, the sources for updating of the large scale land use base maps were provided. The practical technique to update the large scale land use base maps with the meter based high spatial remote sensing images was studies. The method of the land use/land cover classification and change information extraction system based on knowledge is used. First, the NDVI and semi variogram texture characteristics are used to segment the building area, vegetation area, bare land and water area, grass & forest land, road area, the results act as hierachy controller; Second, the spectrum, vegetation indexes and texture characteristics knowledge are applied to classify these regions specifically. At the same time, the region growth technique and spatial entity knowledge are used to modify the classification results; Third, the comparison of land use base map and the remote sensing classification can identify the change regions and correct the classification errors. The experiment results of the test region in Fangshan county of Beijing demonstrate the accuracy of the classification and change information extraction are relatively high. The Kappa coefficient is 0.912,the overall accuracy is 0.938 and change information error is 13.69%. The visual digital target can be supplied through the classification and change information extraction based on knowledge. This research can help reduce the work task and accelerate the visual screen updating process. So it will be widely applied in updating the land use base maps during the survey of land resources.