A Scalable Unsupervised Classification Method Using Rough Set for Remote Sensing Imagery

Abstract

Reference to geographic scale and geographic space representation are characteristics of geospatial data. This work has discussed two issues related to satellite image data, namely huge size and mixed pixels. In clustering, an unsupervised classification and a set of similar objects are grouped together based on the similarity measures. The similarity between intracluster objects is high, whereas the similarity between intercluster objects is low. This paper proposes a clustering technique called spatial rough k-means that classifies the mixed pixels based on their spatial neighbourhood relationship. The authors compared the performance of different state-of-the-art clustering algorithms with that of proposed algorithms for image partitioning and map-reduce methods. The results show that the proposed algorithm has produced clusters of better quality than state-of-the-art algorithms in both the approaches used for handling the vast input data size. Experiments conducted on Landsat-TM 5 data of Delhi region demonstrate the effectiveness of the proposed work.