Land classification based on high resolution remote sensing images

Land information is an important information related to the timely adjustment of land management policy, which can significantly improve the efficiency of land management. The traditional land information collection completely depends on field survey of grass-roots personnel, which is not only time-consuming and laborious but also can not guarantee the accuracy of information. With the rapid development of remote sensing technology, using remote sensing image to obtain land information is an important means to conduct land investigation and detect whether crop planting area is up to the standard. At present, the method of artificial neural network (ANN) learning classification has achieved great success in the land classification, but there is still room for improvement in the classification accuracy. In this paper, a new Algorithm is introduced-shuffled frog leaping algorithm (SFLA), the important parameters of ANN are optimized. The traditional traversal method is improved to a combination of global optimization and local optimization, which can effectively reduce the search time, increase the generalization ability of the model and improve the classification accuracy. Through the experiment of remote sensing data of Fengqi town, Luochuan county, Yan'an city, Shaanxi province, it is shown that SFLA can effectively increase the accuracy of ANN in land information classification. It has achieved the paper research goal.