Semi-Supervised Remote Sensing Classification Via Associative Transfer

Youyou Li, Teng Long, Binbin He, Xiaodong Zhang, Xiaofang Liu

Abstract: Images classification is an essential field in remote sensing community. However, a variety of target shapes, as well as changing conditions during multiple time periods and different areas usually result in shifts in classification. This problem can affect classification results seriously. Although the affection is significant in remote sensing classification, very few people have considered this issue, and have solved it. In this paper, we introduce the associative domain adaptation (ADA) method to address this challenge. We apply this algorithm to two public remote sensing datasets. One is famous UC Merced dataset; another is NWPU-RESISC45 dataset which has a much more variance within the class. We then build a classification model by using UC Merced training images and labels as well as using training images from NWPU-RESISC45. This semi-supervised classification performance achieves an impressive test accuracy on the NWPU-RESISC45 test dataset.