Background and Objective: Due to the high spatial resolution of Geoeye data, due to the wider distribution of pixels, the output maps in Neural network algorithm and Forest cover index (FCD) classification methods are more sensitive and with more pixel detail. Consideri
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Background and Objective: Due to the high spatial resolution of Geoeye data, due to the wider distribution of pixels, the output maps in Neural network algorithm and Forest cover index (FCD) classification methods are more sensitive and with more pixel detail. Considering the large amount of information in new sensors, the aim of this study is to develop and improve the performance of more complex classification algorithms for the interpretation of modern satellite data.
Material and Methodology: FCD model base classification is based on four main indicators: sensitive to shadow, uncovered soil, vegetation conditions and density, and without the need for a training sample. The Neural network algorithm operates with high sensitivity to the original image bands and the bands created and added to the image, as well as training samples. Training samples were determined in the summer of 2016-2017 from series 5 and 6 of 30 Ramsar watersheds.
Finding: Using this method, an accuracy of 24.5% was obtained for the FCD method and 26.2% for the Neural network method. Due to the high resolution of the data used, the output map developed in this method is associated with a high density of polygons.
Discussion & Conclusion: Due to the range of pixels in the output maps of the two methods, an extended method has been proposed to produce a more accurate map, due to the high spatial resolution of the Geoeye sensor. In this method, by reclassifying within the maximum frequency range of pixels, the demarcation of polygons in much smaller and more accurate dimensions is considerable.
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