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Multimodal and Multiresolution Data Fusion for High-Resolution Cloud Removal: A Novel Baseline and Benchmark

MCML Authors

Xiaoxiang Zhu

Prof. Dr.

Principal Investigator

Data Science in Earth Observation

Abstract

Cloud removal (CR) is a significant and challenging problem in remote sensing, and in recent years, there have been notable advancements in this area. However, two major issues remain hindering the development of CR: the unavailability of high-resolution imagery for existing datasets and the absence of evaluation regarding the semantic meaningfulness of the generated structures. In this article, we introduce M3R-CR, a benchmark dataset for high-resolution CR with multimodal and multiresolution data fusion. M3R-CR is the first public dataset for CR to feature globally sampled high-resolution optical observations, paired with radar measurements and pixel-level land-cover annotations. With this dataset, we consider the problem of CR in high-resolution optical remote-sensing imagery by integrating multimodal and multiresolution information. In this context, we have to take into account the alignment errors caused by the multiresolution nature, along with the more pronounced misalignment issues in high-resolution images due to inherent imaging mechanism differences and other factors. Existing multimodal data fusion-based methods, which assume the image pairs are aligned accurately at the pixel level, are thus not appropriate for this problem. To this end, we design a new baseline named Align-CR to perform the low-resolution synthetic aperture radar (SAR) image-guided high-resolution optical image CR. It gradually warps and fuses the features of the multimodal and multiresolution data during the reconstruction process, effectively mitigating concerns associated with misalignment. In the experiments, we evaluate the performance of CR by analyzing the quality of visually pleasing textures using image reconstruction (IR) metrics and further analyze the generation of semantically meaningful structures using a well-established semantic segmentation task. The proposed Align-CR method is superior to other baseline methods in both areas.

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