Monitoring and management of land use plays an important role in the economic development of agriculture regions around the world. In this regard, field boundary extraction for agriculture land parcels is crucial for further analysis and mapping. An effective image analysis needs to perform this task by automatically extracting the features with minimum operator efforts. Many researchers have attempted land use/land cover classification using IRS P6 LISS IV data. However little emphasis has been given to agriculture field boundary extraction. This paper explores the potential of IRS P-6 LISS IV dataset for agriculture field boundary extraction. The segmentation of field areas based on the tonal and textural gradients of the imagery is carried out. The segmented regions were classified to derive preliminary field boundaries. Finally, the derived field boundaries are geometrically refined using snakes. The use of snakes resulted in marked improvement in preliminary results. It was observed that traditional snakes can be effectively applied on single closed boundary at a time, which makes it suitable for single target detection. An attempt has been made in this study to develop looping snakes which can deal with multiple boundaries as will be the case with objects like agriculture fields.