Approaching semi-supervised collaborative learning model for remote sensing image analysis

Remote sensing image data often has many advantages in mapping, target recognition, object tracking, and so on. However, remote sensing image data often face problems such as big data, multiple dimensions, and time series. These things greatly affect the problem of remote sensing image analysis. In this paper, we extend the collaborative clustering algorithm for remote sensing image analysis by using a fuzzy semi-supervised technique. The proposed algorithm applies in the case of very few labeled data. In particular, the collaborative learning model can help reduce centralized computation at one site and instead process decentralized data at different sites. Thereby, it is possible to optimize the calculation ability with big data problems such as remote sensing image data. Moreover, by using a semi-supervised technique, it is possible to take advantage of a supervised clustering technique with a small amount of labeled data to improve the accuracy of clustering the results. Experiments on some actual remote sensing image data show that the proposed method gives significantly better results than the previously proposed methods. This result shows the potential of collaborative clustering in big data processing and improves the clustering quality by using semi-supervised fuzzy clustering with a small amount of labeled data. © 2022 IEEE.