A Method for Building Large-Scale Landcover Semantic Segmentation Dataset of Remote Sensing Image

A gradual refinement strategy method to build a large-scale landcover semantic segmentation dataset of remote sensing image based on the existing monitoring data and historical orthophotos was proposed in this paper. Firstly, a fixed-size sliding window was used to slide on the remote sensing image and the coverage mask obtained from the monitoring vector data at the same time to extract the image slice and label slice, and the original dataset was composed of a large number of slices. Secondly, the original dataset was divided into several subsets by DBSCAN, and the error samples covered by clouds or shadows were effectively classified and deleted from the dataset. Then, the low-precision samples were detected and deleted from the dataset by an optimization and purification method based on iterative optimization intelligent model. Experimental results show that the method proposed in this paper could build a large-scale remote sensing image semantic segmentation dataset effectively based on historical orthophoto images and the monitoring vectors which were not strictly matching the images.