Abstract

Local minima limitations in unsupervised approaches using K-means is still problematic in producing accurate land use/land cover classifications. In response, we developed algorithms of Simulated Annealing (SA) systems based on K-means. We hypothesized that SA-based systems can reduce the likelihood of converging on a local minimum. Two automated SA-based classification systems were developed and applied to a Landsat TM data: a single SA-based (S-SA) system and an integrated SA-based (I-SA) system, which reduces computational intensity. We hypothesized that the I-SA system could produce more efficient classifications than the S-SA system. Kappa statistical analysis on the resulting error matrices demonstrated that the SA-based system significantly improved the classification accuracy over that of the K-means algorithm when appropriate parameters combined as a cooling schedule were chosen. The knowledge and insights gained can facilitate the incorporation of SA random search procedures into other approaches that are similarly limited by local minimum problems to improve accuracy.

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