关键词: 尺度上推, 遥感数据, 高斯金字塔, 高斯模糊, 降采样

Abstract:

Scale transformation has been a research hotspot in the field of remote sensing information science, and its processing methods are now limited to the traditional statistical methods. They consider less spatial structural information of data and can't satisfy the requirements of multi-scale expression. In view of scale inconsistency problems of remotely sensed data, this paper proposed a scaling up method based on image blurring characteristic of Gaussian pyramid. Firstly, this method made Gaussian blur using different filtering parameters for remotely sensed data, and it created several Gaussian blurring data in each layer of the pyramid model. Then Gaussian blurring data were processed by down-sampling constantly, and we obtained a series of different resolution of remotely sensed data. Accordingly, the multi-scale remotely sensed data could meet the spatial resolution requirements of practical application. In order to prove the effectiveness of the proposed method, this paper chose Landsat 7 ETM images and ASTER GDEM data to achieve scaling up. Also, we put forward quantitative evaluation indices including mean, variance, mean absolute error, root mean square error and conducted experiments to compare with the general methods of scaling up, such as nearest neighbor method, bilinear method and cubic convolution method. Besides, the performance of Gaussian pyramid method was measured through the results of contour sets between ASTER GDEM and SRTM DEM with the same resolution. Experimental results showed that Gaussian pyramid method in this paper can effectively realize the scaling up for continuous remotely sensed data, and the results are superior to other traditional methods. Gaussian pyramid method can also keep local characteristics of the details and maintain amount of information, spatial structural features of remotely sensed data. In summary, the using of Gaussian pyramid method in the field of remote sensing is a new attempt, and can provide necessary data preparation for multi-scale visual expression.

Key words: scale up, remotely sensed data, Gaussian pyramid, Gaussian blur, down-sampling