Abstract: Intelligent optimization algorithms has attracted considerable attention on the remote sensing image classification. As one of swarm intelligent optimization algorithms, the ant colony algorithm has been applied in the remote sensing data classification. However, the conventional Ant-Miner algorithm takes long computation time with a slow convergence in mining remote sensing image classification rules. We propose a new ant colony algorithm based on conventional Ant-Miner algorithm. Firstly, the conventional Ant-Miner algorithm is modified about the strategy of pheromone update by using new pheromone concentration update item and pheromone evaporation coefficient. Then, the mutation operator is introduced in algorithm solve. The new ant colony algorithm accelerates effectively the evolutionary process and shorten the calculation time. In order to verify the new algorithm, this study selects the Changsha city as a case study area and use Landsat TM image in 2006. The results indicate that the new algorithm obtains the simpler forms of classification rule and reduce the computation time. The remote sensing image classification using improved Ant-Miner algorithm is more accurate and efficient than conventional Ant-Miner algorithm and decision tree.