Over the past four decades, the satellite imaging sensors have acquired huge quantities of Earth- observation data. Content-based image retrieval allows for fast and effective queries of remote sensing images. Here, we take the following two issues into consideration. Firstly, different features and their combination should be chosen for different land covers. Secondly, for the block dividing strategy and the complexities of the remote sensing images, it can not effectively retrieve some small target areas scattered in multiple nontarget blocks. Aiming at the above two issues, a new region-based retrieval method with adaptive image segmentation is proposed. In order to improve the accuracy of remote sensing image segmentation, feature selection and weighing is performed by two-stage clustering, and image segmentation is accomplished based on the chosen features and mean shift procedure. Meanwhile, for the homogeneous characteristics of remote sensing land covers, a new regional representation and matching scheme are adopted to perform image retrieval. Experimental results on retrieving various land covers show that the method can avoid the impact of traditional blocking strategies, and can achieve an average percentage of 19% higher precision with the same level of recall rate, than the relevance feedback method for small target areas.