Abstract: Abstract: The growing population and accelerating urbanization have caused much illegal occupation of farmland, which seriously threat to national food security, social stability and economic development of our country. Farmland information extraction has become a hot issue in agricultural research field in the world. In addition, farmland mapping is closely related to the food security and is one of the most concerned issues of government departments. However, traditional technology of surveying and mapping is time consuming and labor costing, which is unable to adapt to the precise and effective information acquisition of farmland. The high resolution remote sensing imagery can provide more details of ground truth than low resolution imagery. However, the information mining in high resolution remote sensing imagery faces a big challenge caused by the complex ground environment. Farmland blocks in high resolution remote sensing imagery have various shapes, complicated texture and heterogeneous spectrum. The shape information is one of the most important content of farmland mapping. In this study, high resolution remote sensing imagery from QuickBird was used to precisely extract farmland in hilly area. And the method of farmland extraction combining multi-scale segmentation and optimal scale selection was put forward. Firstly, gradient image is generated by using Sobel gradient operator. In order to enhance the edge information and reduce the irrelevant information for farmland extraction, the multi-scale gradient images are filtered with anisotropic diffusion operator. Secondly, effective scale range of multi-scale gradient images is determined through the entropy difference analysis, which can reduce the amount of calculation of the multi-scale analysis in next stage. Thirdly, a marker driven watershed transform based on minima extension and minima imposition is applied to segment the multi-scale gradient images to produce multi-scale shape information of farmland with precious boundaries. Finally, the optimal scale identification for multi-scale segmentation is obtained by unsupervised segmentation evaluation method based on the global Moran'I and variance to automatically get the farmland block without manual intervention. The experimental results show that the multi-scale segmentation and optimal scale identification approach can be used to accurately discriminate farmland in hilly area. Farmland extraction accuracy of the proposed method is 73.06% which is 22.40% higher than the Mean-shift segmentation method with a better performance in farmland extraction in hilly area. The results can basically meet the requirements of drawing and revision for the large scale thematic mapping of farmland, showing that the proposed method can provide a technical assistance for the surveying and mapping of farmland.