Implementation and comparative analysis of rough set, Artificial Neural Network (ANN) and Fuzzy-Rough classifiers for satellite image classification

Geospatial information we gather through different sensors and from the concepts of the geographic objects, is generally vague, imprecise and uncertain. Also, the imprecision becomes obvious due to the multi-granular structure of the multi-sensor satellite images and that leads to error accumulation at every stage in geo-processing. It has been observed that the ground truth data, forming a prime decision system, an essential ingredient for a supervised learning, may itself contain redundant / inconsistent / conflicting information. Moreover, there may be superfluous attributes that warrants a fast mechanism to identify & discard them and at the same time keep the information content compatible to the original data set. Recently the rough set theory - proposed by Zdzislaw Pawlak, has emerged as an effective measure to resolve imprecise knowledge, analysis of conflicts, evaluation of data dependencies and generating rules. In this study, we have applied the rough set theory, to handle the imprecision due to granularity of the structure of the satellite image. The objective is how the decision system required for any supervised classification, is made consistent and free from superfluous attributes. We compared the results of performing land cover classification of the LISS-IIcirc image pertaining to Alwar (Rajasthan) area by the rough set, artificial neural networks, and rough-fuzzy theory. Our findings are that, in the era of Internet GIS, time and accuracy is the prime requirement in classification and interpretation of images for any critical application. Rough set and rough-fuzzy theory offer a better and transparent choice to have faster, comparable and effective results.