Abstract:

The high-standard farmland area in China has currently reached a considerable scale. The low efficiency of manual interpretation has called for more precise and real-time remote sensing monitoring for the construction of large scale farmland in the country. Because of the large monitoring area and high precision demand, a set of automatic remote sensing monitoring classification method is needed to develop. The study area is located in Dongguan area of Guangdong Province, South China. Using GF-2 remote sensing images (15^th February, 2017), the fractal-based image segmentation combined with the BP neural network remote sensing classification method is studied, which is supported by the artificial interpretation and field verification. The experimental results show that the overall precision of the classification method is 80.112,2% and the Kappa coefficient is 0.761,1,which indicates that the overall precision of fractal image segmentation and BP neural network remote sensing classification method is higher. This can better meet the needs of remote sensing monitoring after the construction of the high standard farmland.A nationwide adoption of this method can provide technical support for the real-time monitoring of the high standard farmland.

Key words: remote sensing monitoring, farmland use, BP neural network, fractal image segmentation