Spatiotemporal Subpixel Mapping Based on Priori Remote Sensing Image With Variation Differences

Subpixel mapping (SPM) could handle the mixed pixels in coarse original spectral image (COSI) to obtain the fine land-cover class mapping result. In recent years, with the auxiliary spatiotemporal information provided by the same region fine prior spectral image (FPSI), spatiotemporal subpixel mapping (SSPM) has shown greater potential than the traditional SPM methods. However, the inaccurate spatiotemporal information of the FPSI is rarely effective identified due to variation differences in the current SSPM methods, affecting the mapping accuracy. To address the abovementioned issues, SSPM based on priori remote sensing image with variation differences (CVDBI) is proposed. First, the coarse abundance images of COSI and the fine thematic images of FPSI are obtained by unmixing COSI and classifying FPSI. Second, the degradation observation model (DOM) is established to use downsampling matrix to correlate the coarse abundance images of COSI with the ideal thematic images of COSI, and the variation difference observation model (VDOM) is established to use variation difference factor to correlate the fine thematic images of FPSI with the ideal thematic images of COSI. Third, a separable convex optimization model is established for DOM and VDOM. This model optimizes the variation difference factor and the ideal thematic images of COSI. Finally, we use the alternating direction method of multipliers to solve the separable convex optimization problem to produce the final mapping result. Experimental results on the three spectral images show that the proposed CVDBI yields the more accurate mapping result than the traditional SPM methods.

Automated summary

In this paper, a spatiotemporal subpixel mapping (SSPM) method based on priori remote sensing image with variation differences (CVDBI) is proposed to improve the mapping accuracy of fine land-cover class. The experimental results show that the proposed CVDBI outperforms traditional SPM methods in terms of accuracy.