A Fusion Method for Multisource Land Cover Products Based on Superpixels and Statistical Extraction for Enhancing Resolution and Improving Accuracy

Abstract

The discrepancies in existing land cover data are relatively high, indicating low local precision and application limitations. Multisource data fusion is an effective way to solve this problem; however, the fusion procedure often requires resampling to unify the spatial resolution, causing a lower spatial resolution. To solve this problem, this study proposes a multisource product fusion mapping method of filtering training samples and product correction at a fine resolution. Based on the Superpixel algorithm, principal component analysis (PCA), and statistical extraction techniques, combined with the Google Earth Engine (GEE) platform, reliable land cover data were acquired. GEE and machine-learning algorithms correct the unreliable information of multiple products into a new land cover fusion result. Compared to the common method of extracting consistent pixels from existing products, our proposed method effectively removes nearly 38.75% of them, with a high probability of classification error. The overall accuracy of fusion in this study reached 85.80%, and the kappa coefficient reached 0.82, with an overall accuracy improvement of 11.75–24.17% and a kappa coefficient improvement of 0.16 to 0.3 compared to other products. For existing single-category products, we corrected the phenomenon of overinterpretation in inconsistent areas; the overall accuracy improvement ranged from 2.99% to 20.71%, while the kappa coefficient improvement ranged from 0.22 to 0.56. Thus, our proposed method can combine information from multiple products and serve as an effective method for large areas and even as a global land cover fusion product.