Most paddy rice fields in Asia are comprised of small parcels of land, and the weather conditions during the growing season are usually cloudy. This study develops a geographic information system (GIS) object-based post classification (GOBPC) that combines low-cost remotely sensed and GIS data to precisely map paddy rice fields in the intensively cultivated but fragmented growing areas which are characteristic of Asia. FORMOSAT-2 multispectral images have an 8-meter resolution and a one-day recurrence, making them ideal for mapping such areas. Multitemporal images are examined to distinguish the different growth characteristics between paddy rice and other types of ground cover. The pixel-based hybrid classification technique is used with both the unsupervised and supervised approach to distinguish the paddy rice fields from their surroundings. In addition to the pixel-based approach, we also use GOBPC to deal with over-fragmented parcels of land and to reduce the incidence of misclassification caused by speckle or mixed pixels (mixels) in the images. A comparison is made with the pixel-based technique. The Kappa index of agreement obtained with the GOBPC reaches 0.095 to 0.291, and there is a statistically significant improvement in the user and producer accuracy for all the classes () with McNemar’s test in the four study areas. The proposed GOBPC approach is shown to be useful in highly fragmented rice growing areas and may have the potential for other agricultural applications.