Volume XLII-3
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3, 669-672, 2018
https://doi.org/10.5194/isprs-archives-XLII-3-669-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3, 669-672, 2018
https://doi.org/10.5194/isprs-archives-XLII-3-669-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

  30 Apr 2018

30 Apr 2018

DETECTING WATER BODIES IN LANDSAT8 OLI IMAGE USING DEEP LEARNING

W. Jiang1,2, G. He1,3,4, T. Long1,3,4, and Y. Ni1,5 W. Jiang et al.
  • 1Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing, China
  • 2University of the Chinese Academy of Sciences, Beijing, China
  • 3Key Laboratory of Earth Observation Hainan Province, Sanya, China
  • 4Sanya Institute of Remote Sensing, Sanya, China
  • 5Key Laboratory of Spatial Data Mining & Information Sharing of Ministry of Education, Spatial Information Research Center of Fujian Province, Fuzhou University, Fuzhou, China

Keywords: Water body, Landsat 8, Multi-layer perceptron, Deep learning, Maximum likelihood

Abstract. Water body identifying is critical to climate change, water resources, ecosystem service and hydrological cycle. Multi-layer perceptron(MLP) is the popular and classic method under deep learning framework to detect target and classify image. Therefore, this study adopts this method to identify the water body of Landsat8. To compare the performance of classification, the maximum likelihood and water index are employed for each study area. The classification results are evaluated from accuracy indices and local comparison. Evaluation result shows that multi-layer perceptron(MLP) can achieve better performance than the other two methods. Moreover, the thin water also can be clearly identified by the multi-layer perceptron. The proposed method has the application potential in mapping global scale surface water with multi-source medium-high resolution satellite data.