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Irrigation district canal system with modern water-saving irrigation technology has a significant impact on rational distribution of water and the safety of water supply. However，the resolution of the commonly used remote sensing image of irrigation area is not high, which brings difficulties to the extraction and mapping of the drainage system. The high-precision ortho-image, elevation and slope data collected by UAVs were taken together as data sources. Features with strong canal discriminative ability were obtained from them to construct a training set. The classification system was trained via the support vector machine to segment canals from images. Then, the extraction results were denoised, connected and optimized, and the canal extraction of UAV high resolution multi-source data was realized. The results showed that the canal extraction method can identify the branch canal in the irrigation area. Meanwhile，competitive performance was achieved in the continuity of the canal, the bucket and the part of canal system. The precision was up to 89.35%. The extraction error was mainly caused by the deposition of canopy mud in the lower canal system which made the terrain features not easy to be recognized. In conclusion, the method proposed provided a new solution for the extraction of irrigation and drainage canal and can be applied to the actual agricultural production.