A new method for surface water extraction using multi-temporal Landsat 8 images based on maximum entropy model

European Journal of Remote Sensing

Huiran Gao¹  Wanchang Zhang¹  Chuanhua Li²  Xiaodong Wu³  Hao Chen⁴  Wangping Li⁵  Zhihong Li⁵  Yu Wang⁵  Zhaoye Zhou⁵  Junming Hao⁵ 

[1] Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, Haidian, China;College of Geography and Environmental Science, Northwest Normal University, Lanzhou, Gansu, China;Cryosphere Research Station on the Qinghai-Tibet Plateau, State Key Laboratory of Cryospheric Science Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou, Ganus, China;Institute of Surface-Earth System Science, School of Earth System Science, Tianjin University, Tianjin, Nankai, China;School of Civil Engineering, Lanzhou University of Technology, Lanzhou, Gansu, China;

关键词: Maximum entropy model;    spectral matching;    remote sensing;    Landsat 8_OLI;    surface water extraction;    normalized difference water index;   

DOI  :  10.1080/22797254.2022.2062054

来源: DOAJ

【 摘 要 】

The spectral matching algorithm based on the discrete particle swarm optimization algorithm (SMDPSO) sometimes overestimates extracted surface water areas. Here we constructed a new method (MEDPSO) by coupling discrete particle swarm optimization algorithm with maximum entropy model (MaxEnt) to extract water bodies using Landsat 8 Operational Land Imager (OLI) images. To compare the accuracy of the modified normalized difference water index (MNDWI), SMDPSO, and MEDPSO, we selected six areas , i.e. thermokarst lakes, Coongie Lakes National Park, the Amazon River, urban water bodies mixed with buildings, Erhai Lake that is surrounded by mountains, and high-altitude lakes. Our results show that the average overall accuracy of the MEDPSO for the six areas is 97.4%, which is higher than those of MNDWI and SMDPSO. The average commission errors and omission errors of MEDPSO (6.4% and 0.8%) are lower than those of MNDWI and SMDPSO. The MEDPSO has a higher accuracy because the maximum entropy model is a machine learning method that uses all the bands of Landsat imagery and four surface water indices in the calculation of the probability of surface water. Our study established a novel, high-precision water extraction method.

【 授权许可】

Unknown