Land cover classification of remotely sensed image with hierarchical iterative method

Land cover classification is one of the most wide ly used applications of remote sensing. The use ofmultitemporal remote sensing data in land cover clas sification is one of the effective methods of obtainingaccurate land cover/land use data. For a particularimage, different land cover types often show a similarspectral response and are difficult to separate. Theadvantage of using multitemporal data is that differentvegetation types show different spectral characteristicsin…     

Land cover classification of remotely sensed image with hierarchical iterative method

Based on the analysis of the single-stage classification results obtained by the multitemporal SPOT 5 and Landsat 7 ETM+ multispectral images separately and the derived variogram texture, the best data combinations for each land cover class are selected, and the hierarchical iterative classification is then applied for land cover mapping. The proposed classification method combines the multitemporal images of different resolutions with the image texture, which can greatly improve the classification accuracy. The method and strategies proposed in the study can be easily transferred to other similar applications.