Cloud Removal in Remote Sensing Using Sequential-Based Diffusion Models

first_page settings Order Article Reprints Open AccessArticle Cloud Removal in Remote Sensing Using Sequential-Based Diffusion Models by Xiaohu Zhao [ORCID] and Kebin Jia * [ORCID] Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China * Author to whom correspondence should be addressed. Remote Sens. 2023, 15(11), 2861; https://doi.org/10.3390/rs15112861 Received: 11 April 2023 / Revised: 24 May 2023 / Accepted: 26 May 2023 / Published: 31 May 2023 (This article belongs to the Special Issue Advancing Machine Learning for Remote Sensing to Enhance Spatio-Temporal Generalizability) Download Browse Figures Review Reports Versions Notes Abstract The majority of the optical observations collected via spaceborne optical satellites are corrupted by clouds or haze, restraining further applications of Earth observation; thus, exploring an ideal method for cloud removal is of great concern. In this paper, we propose a novel probabilistic generative model named sequential-based diffusion models (SeqDMs) for the cloud-removal task in a remote sensing domain. The proposed method consists of multi-modal diffusion models (MmDMs) and a sequential-based training and inference strategy (SeqTIS). In particular, MmDMs is a novel diffusion model that reconstructs the reverse process of denosing diffusion probabilistic models (DDPMs) to integrate additional information from auxiliary modalities (e.g., synthetic aperture radar robust to the corruption of clouds) to help the distribution learning of main modality (i.e., optical satellite imagery). In order to consider the information across time, SeqTIS is designed to integrate temporal information across an arbitrary length of both the main modality and auxiliary modality input sequences without retraining the model again. With the help of MmDMs and SeqTIS, SeqDMs have the flexibility to handle an arbitrary length of input sequences, producing significant improvements only with one or two additional input samples and greatly reducing the time cost of model retraining. We evaluate our method on a public real-world dataset SEN12MS-CR-TS for a multi-modal and multi-temporal cloud-removal task. Our extensive experiments and ablation studies demonstrate the superiority of the proposed method on the quality of the reconstructed samples and the flexibility to handle arbitrary length sequences over multiple state-of-the-art cloud removal approaches.