关键词: 夜间灯光影像, DMSP/OLS, 传统不变目标区域法, 校正, 拟合, GDP, 电力消耗, 对比检验

Abstract:

With the rapid development of remote sensing technology, the night light image of Defense Meteorological Satellite Program Scanning System (DMSP/OLS) has been more and more applied in the research of humanist economics. Since there are many problems in the original images, such as the saturation of DN value and inter-annual discontinuity, it is necessary to correct those data before using them. "Traditional invariant region method" is a widely-using correction method, but there are still some problems, such as not taking into account the weak changes from the prospect of a long-time scale of the target region and the selection of the reference year in a continuous correction. In this paper, the traditional invariant region method was improved. In order to reduce the impact on correction results, caused by the small changes of the target region in a long time scale, and to improve the saturation of the original images, the stable night light images of DMSP/OLS were saturation corrected with the help of selecting three group data (F12_1999, f14-15_2003 and F16_2006) as reference images. By the comparison between saturation corrected images and reference images, the reasonable reference year, F15_2006 (after the saturation correction), with the minimum error was selected, so as to carry out the continuous correction of the saturation corrected images. To verify the correction precision of images, in this paper, pixel DN value test was carried out at the national level. And linear regression test was carried out in the corresponding TDN value between GDP at the provincial level, electricity consumption at the provincial level, GDP at the prefecture-level and at the county level, at the same time, compared with the other published correction results such as Cao Z Y^[25], results shew that the average fit R2 between the improved DMSP/OLS stable night light images TDN and the city's GDP was 0.85 while the average fit R2 between the results with the traditional invariant region method and with city GDP was only 0.53. As time goes by, the fitting degree of the traditional invariant region method gradually decreased to 0.40 in 2013. However, the fitting degree using the improved correction method did not decrease. In 2013, the R2 was 0.88, which indicated that compared with the traditional invariant region method, the correction accuracy was significantly improved and the saturation problem was greatly improved. At the same time, the fitting degree of TDN and GDP on the county scale was about 0.6, indicating that the night light image data set on the county scale also had certain applicability. However, this approach did not completely solve the problem of pixel saturation. How to solve this problem perfectly is the core in the application research of noctilucent data in the future.

Key words: night light images, DMSP/OLS, traditional invariant region method correction, correction, fitting, GDP, electricity consumption, contrast test