Deep Spatiotemporal Subpixel Mapping Network by Integrating a Prior Fine Land Cover Map With Change Detection

Subpixel mapping (SPM) is a prevailing technique for addressing the mixed pixel issue by estimating the subpixel-scale distribution of land cover types within mixed pixels. Recently, spatiotemporal SPM (STSPM) has advanced traditional SPM by integrating prior fine spatial resolution (FR) images to alleviate the uncertainty in SPM results. However, most existing STSPM methods are typically model-driven or meticulously handcrafted, and they usually struggle to flexibly characterize the diverse and complex patterns of land cover patches. This article proposes a novel deep STSPM network (STSPMNet) that integrates a prior FR land cover map with bitemporal coarse spatial resolution (CR) remote sensing images using deep learning and change detection techniques. STSPMNet first combines super-resolution and semantic segmentation to achieve the end-to-end SPM from CR remote sensing images to FR land cover maps. A change-detection-based optimization approach is then developed to incorporate a prior FR land cover map with the estimated FR land cover probabilities to generate the final FR land cover map at the prediction date. Three experiments were conducted to evaluate the effectiveness of the proposed STSPMNet. Experimental results demonstrate that STSPMNet outperforms two SPM methods and two STSPM method by recovering more spatial details of land cover patches and generating higher accuracy metrics. Hence, STSPMNet offers a robust solution for producing FR land cover maps from CR remote sensing imagery.