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Extracting cropland parcels from high-resolution remote sensing images is an important issue for dynamic land-use monitoring, precision agriculture and other fields. However, cropland spectra change frequently in time and spatial space. The application of multi-spectral image classification in cropland extraction, not only leads to misclassification with other vegetation easily, but also results in broken parcels caused by salt and pepper effect. Texture is an important feature of satellite images, which takes into account pixel gray scale difference and the spatial relationship between neighboring pixels. In order to overcome the impact of spectral variability, this paper presents an advanced cropland parcel extraction method based on texture analysis and multi-spectral image classification. Test on an ALOS (Advanced Land Observation Satellite) image shows that this method can effectively reduce the impact of spectral variations and obtain satisfactory results. But there still has some aspects which should be further improved in the future study, including: (1) some "noise" polygons still exist because the filter can not eliminate all the noise pixels completely; and (2) parcels generated by this approach can not reflect their subtle internal difference, such as inner boundary shaped by different crops.