DMSP/OLS稳定夜间灯光影像在中国的分类校正方法

A Method for Classified Correction of Stable DMSP/OLS Nighttime Light Imagery Across China

长时间序列DMSP/OLS夜间稳定灯光影像数据集存在饱和像元大量积聚在城市中心区域及影像之间缺少可比性的问题,导致数据集在进行空间分析及时间对比研究时会受到限制。为解决上述2个问题,本文在现有研究的基础上提出了一种基于不变目标区域的中国稳定灯光影像分类校正方法。该方法对数据集中每一期中国区域稳定灯光影像进行了饱和及非饱和像元的分类。对饱和像元,选择北京等5个城市作为不变目标区域,选择不存在饱和像元的辐射校准的灯光影像作为参考,基于不变目标区域的非饱和像元之间的相关关系也同样适用于饱和像元的假设,对饱和像元进行了校正;同时,基于多期辐射校准的灯光影像之间存在的相互校准关系,对饱和校正后的饱和像元进行了相互校准。对于非饱和像元,选择了长沙等13个城市作为不变目标区域;根据稳定灯光影像中非饱和像元DN值总和随时间变化的总体趋势,确定了13期稳定灯光影像作为参考,其他多期影像基于不变目标区域中非饱和像元之间的相关关系进行了相互校准。为验证校正结果的准确性,多种相互校准和饱和校正方法被引入进行比较。本文提出的分类校正方法与另外2种相互校准方法相比对F10、F12和F14卫星影像校准效果更好,另外2种方法对F15和F16卫星影像的校准结果更好,3种方法总体上都实现了对数据集影像的相互校准且使校正后的影像具有了可比性。与经过植被指数校准后的城市夜间灯光指数(The Vegetation Adjusted NTL Urban Index,VANUI)相比,分类校正方法解决了饱和像元聚集于城市中心区域的问题,减弱了像元饱和程度,校正结果更接近辐射校准的灯光影像。此外,在中国城市水平上,分类校正的稳定灯光影像与GDP和电力消耗值的相关关系与其他2种相互校准方法相比也是相对较好的,能更客观合理地反映区域经济发展的差异。

Long-time series stable DMSP/OLS nighttime light data lack comparability and include a lot of saturated pixels in the bright cores of urban areas. The two problems have limited applications of the dataset in spatial analysis and temporal comparison. To help address the two problems, this article developed a classified correction method for Stable Nighttime Light(SNL)imagery across China by using invariant regions for calibration. The SNL images are divided into saturated pixels and unsaturated pixels. For saturated pixels, five cities such as Beijing were selected as the invariant regions, and the radiance calibrated nighttime light images(RCNTL)without saturated pixels were selected as the reference. Based on the assumption that the correlation between unsaturated pixels of the invariant regions could also be applied to saturated pixels, the reference images were used to correct saturated pixels of the SNL images. Meanwhile, the saturation corrected saturated pixels were calibrated based on the interclibration relationship between the different RCNTL images. For unsaturated pixels, 13 cities including Changsha were selected as the invariant regions. According to the overall time change trend of the sum of the unsaturated pixel values, the 13 SNL images were determined as the references, and other SNL images were intercalibrated based on the correlation between unsaturated pixels in the invariant regions. To validate the accuracy of the calibration results, a variety of intercalibration and saturation correction methods were adopted for comparison. The classified correction method proposed in this paper was found better for the calibration of F10, F12 and F14 satellite imagery than the other two intercalibration methods.The other two methods had better calibration results for F15 and F16 satellite imagery. The three methods generally achieve intercalibration of the dataset imagery and make the intercalibrated images comparable.Compared with the Vegetation Adjusted NTL Urban Index(VANUI), the saturation correction result of the classified correction method solves the problem that the saturated pixels concentrate in the central area of cities.The result reduces the saturation of the pixels and is a closer fit to the RCNTL images. Besides, the corrected SNL images have relatively good correlations with GDP and power consumption values, and can reflect regional economic development differences more objectively.