Land cover classification is one of the most important applications of POLSAR images. In this paper, a hybrid biogeography-based optimization support vector machine (HBBOSVM) has been introduced to classify POLSAR images of RADARSAT 2 in band C acquired from San Francisco, USA. The main purpose of this classification is to minimize the number of features and maximize classification accuracy. The proposed method consists of three main steps: preprocessing, feature selection and classification. As preprocessing, radiometric calibration, speckle reduction and feature extraction have been performed. In the proposed HBBO, the combination of onlooker bee of artificial bee colony (ABC) and migration operator of biogeography-based optimization has been applied in order to optimal feature selection. Then, SVM has been used to classify the pixels into specific labels of land-covers. The ground truth samples have been generated by google earth image, Pauli RGB image, high resolution image and national land cover database (NLCD 2006). The performance of HBBOSVM has been compared with BBOSVM, ABCSVM, particle swarm optimization support vector machine (PSOSVM) and the results of previous studies. In addition, the performance of HBBO is evaluated upon 20 well-known benchmark problems. According to the obtained results, the overall accuracy and average accuracy of HBBOSVM are 96.01% and 93.37% respectively which is the best result in comparison with other results. The HBBOSVM has better performance than other algorithms in terms of overall accuracy, kappa coefficient, average accuracy, convergence trend, and stability. In addition, the HBBO can be considered as a successful meta-heuristic for benchmark problems. This paper displays that the combined approach of optimization and machine learning methods provides powerful results.

土地覆被分类是POLSAR图像最重要的应用之一。本文介绍了一种基于混合生物地理学的优化支持向量机（HBBOSVM），用于对从美国旧金山获得的C波段中RADARSAT 2的POLSAR图像进行分类。该分类的主要目的是最大程度地减少特征数量并最大化分类精度。所提出的方法包括三个主要步骤：预处理，特征选择和分类。作为预处理，已经执行了辐射校准，斑点减少和特征提取。在提出的HBBO中，人工蜂群围观蜂（ABC）与基于生物地理学的优化的迁移算子相结合，以优化特征选择。然后，支持向量机已用于将像素分类为特定的土地覆盖物标签。地面真相样本是由Google地球图像，Pauli RGB图像，高分辨率图像和国家土地覆盖数据库生成的（NLCD 2006）。已将HBBOSVM的性能与BBOSVM，ABCSVM，粒子群优化支持向量机（PSOSVM）以及以前的研究结果进行了比较。此外，还根据20个众所周知的基准问题评估了HBBO的性能。根据获得的结果，HBBOSVM的整体准确度和平均准确度分别为96.01％和93.37％，这是与其他结果相比最好的结果。在整体准确性，kappa系数，平均准确性，收敛趋势和稳定性方面，HBBOSVM具有比其他算法更好的性能。此外，HBBO可被视为基准问题的成功的元启发式方法。本文表明，优化和机器学习方法相结合的方法可提供有力的结果。