An effective hybrid approach to remote sensing image classification

The paper presents a hybrid fuzzy classifier for effective land use land cover mapping. It discusses a Bayesian way of incorporating spatial contextual information into the Fuzzy Noise Classifier (FNC). The FNC was chosen, as it detects noise using spectral information more efficiently than its fuzzy counterparts. The spatial information at the level of second order pixel neighbourhood was modelled using Markov Random Fields (MRFs). Spatial contextual information was added to the MRF using different adaptive interaction functions. These help to avoid over-smoothening at the class boundaries. The hybrid classifier was applied to Advanced Wide Field Sensor (AWiFS) and Linear Imaging Self Scanning Sensor-III (LISS-III) images from a rural area in India. Validation was done with a Linear Imaging Self Scanning Sensor-IV (LISS-IV) image from the same area. The highest increase in accuracy among the adaptive functions was equal to 4.1% and 2.1% for AWiFS and LISS-III images, respectively. The paper concludes that incorporation of spatial contextual information into the fuzzy noise classifier helps in achieving a more realistic and accurate classification of satellite images.