Volume XLII-4/W18
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-4/W18, 19–24, 2019
https://doi.org/10.5194/isprs-archives-XLII-4-W18-19-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-4/W18, 19–24, 2019
https://doi.org/10.5194/isprs-archives-XLII-4-W18-19-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

  18 Oct 2019

18 Oct 2019

HYPERSPECTRAL CHANGE DETECTION IN WETLAND AND WATER-BODY AREAS BASED ON MACHINE LEARNING

M. Ahangarha, S. T. Seydi, and R. Shahhoseini M. Ahangarha et al.
  • School of surveying and Geospatial Engineering, University of Tehran, Tehran, Iran

Keywords: Wet land, Machine learning, Change Detection, Random forest, Hyperspectral

Abstract. Wetlands and water-bodies are transitional lands between terrestrial and aquatic ecosystems that give many advantages. The presence of phenomena activities and artificial human activities cause changes on the Earth’s surface. This process causes the changes in land cover type especially in wetlands and water-bodies’ area. For monitoring and assessing resources like wetlands and water-bodies, it is necessary to be aware of these changes. Change detection and attribution of wetlands and water-bodies change over time present more challenges for correctly analysing remote sensing imagery. Hyperspectral images now have potential applications in many scientific areas due to their high spectral resolution and so their good information contents. The aim of this study is to propose a procedure for determining land surface changes within the semi-arid wetland and surrounding upland areas using the new method by combining machine Learning method for detecting change using EO-1 Hyperion satellite hyperspectral imagery. The study area is Shadegan wetlands in the south-west of Iran in Khuzestan province. The most critical water resources of the province are depleted and contain unprecedented levels of toxic waste. In addition, the results show the superiority of the implemented method to extract change map with overall accuracy by a margin of nearly 94% using multi-temporal hyperspectral.