It is expensive in time or resources to obtain adequate labeled data for a new remote sensing image to be categorized. The cost of manual interpretation can be reduced if labeled samples collected from previous temporal images can be reused to classify a new image over the same investigated area. However, it is reasonable to consider that the distributions of the target data and the historical data are usually not identical. Therefore, the efficient strategy of transferring the beneficial information from historical images to the target image hits a bottleneck. In order to reuse sufficient historical samples to classify a given image with scarce labeled samples, this letter presents a novel transfer learning algorithm for remote sensing image classification based on extreme learning machine with weighted least square. This algorithm adds a transferring item to an objective function and adjusts historical and target training data with different weight strategies. Experiments on two sets of remote sensing images show that the presented algorithm reduces the requirement for target training samples and improves classification accuracy, timeliness, and integrity.