Journal of Geo-information Science ›› 2015, Vol. 17 ›› Issue (9): 1092-1102.doi: 10.3724/SP.J.1047.2015.01092

• Orginal Article • Previous Articles     Next Articles

Correction of DMSP/OLS Night-time Light Images and Its Application in China

CAO Ziyang1,2(), WU Zhifeng3,*(), KUANG Yaoqiu1, HUANG Ningsheng1   

  1. 1. Key Laboratory of Marginal Sea Geology, Guangzhou Institute of Geochemistry, Chinese Academy of Sciences, Guangzhou 510640, China
    2. University of Chinese Academy of Sciences, Beijing 100049, China
    3. School of Geographical Sciences, Guangzhou University, Guangzhou 510006, China
  • Received:2014-12-08 Revised:2015-01-04 Online:2015-09-10 Published:2015-09-07
  • Contact: WU Zhifeng;
  • About author:

    *The author: SHEN Jingwei,


DMSP/OLS (Defense Meteorological Satellite Program Operational Linescan System) night-time light images can objectively reflect the intensity of human activities; therefore they were widely used in a variety of fields for urban remote sensing. However, the raw night-time dataset cannot be used directly in these researches due to the lack of inflight calibration, thus it needs to be further corrected. There are two problems existed in the long-time series of DMSP/OLS night-time light image dataset that should be addressed in the image correction procedure. First, every image in the raw night-time light image dataset cannot directly compare with each other due to the issue of discontinuity; second, there is a pixel saturation phenomenon existed in every image of the raw night-time light image dataset. In order to solve these problems, a method based on invariant region was proposed. This method included the intercalibration, the saturation correction, and the continuity correction procedures among all the images from the raw images dataset. All the night-time light images of China, which were extracted from the raw images dataset, were corrected using this method. Finally, this correction method was evaluated by analyzing the relationships between the night-time light images and the corresponding gross domestic product (GDP) data and the corresponding electric power consumption data respectively. Through the analysis toward the evaluated results, two main conclusions were acquired. One was that this method had solved the problem of discontinuity in the raw image dataset; the other one was that this method could reduce the pixel saturation phenomenon that existed in every images of the raw night-time light image dataset. However, this method has not completely solved the problem of pixel saturation. How to perfectly solve this problem is the core issue for future research on night-time light data application.

Key words: DMSP/OLS, night-time light images, China, correction method, GDP