Land-cover classification with high-resolution remote sensing images using transferable deep models
（1）伪标签产生方法
a deep Convolutional Neural Networks (CNNs) is first pre-trained with a well-annotated land-cover dataset, referred to as the source data.
Then, given a target image with no labels, the pre-trained CNN model is utilized to classify the image in a patch-wise manner.
The patches with high confidence are assigned with pseudo-labels and employed as the queries to retrieve
related samples from the source data. The pseudo-labels confirmed with the retrieved results are regarded as
supervised information for fine-tuning the pre-trained deep model.
（2）多尺度的方法，图像分片处理的方法
