Remote sensing data imputation using deep learning for multispectral imagery
A study compares deep learning models with linear interpolation for imputing missing satellite data due to cloud cover in aquatic monitoring. CNN-based models, especially CNN, outperformed linear interpolation across four lakes. The imputed data improved the reliability of algal bloom detection using PlanetScope SuperDove imagery.
Article intelligence
Key points
- Deep learning models significantly outperform linear interpolation for filling gaps in multispectral satellite data.
- CNN achieved the best performance across most of the four studied lakes.
- Algal bloom indices derived from imputed data closely matched observations, validating the approach.
- The method enhances the completeness of optical satellite datasets for water quality monitoring.
Why it matters
This matters because deep learning models significantly outperform linear interpolation for filling gaps in multispectral satellite data.
Technical impact
May affect model selection, inference cost, product capability, and evaluation benchmarks.
[2605.24003] Remote sensing data imputation using deep learning for multispectral imagery
[Submitted on 19 May 2026]
Title:Remote sensing data imputation using deep learning for multispectral imagery
View a PDF of the paper titled Remote sensing data imputation using deep learning for multispectral imagery, by Shuang Liua and 2 other authors
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Abstract:Remote sensing techniques have been increasingly utilised in aquatic applications in recent years. A common challenge in using optical satellite data is the presence of missing observations due to cloud cover. These data gaps can lead to missed detection of critical events, such as algal blooms, in lakes of high interest to water authorities. As a result, enhancing the completeness of optical satellite datasets is crucial for improving the monitoring and prediction of algal blooms. In this study, we compared a traditional data imputation method (i.e., linear interpolation) with deep learning models for reconstructing missing spectral bands across four lakes with historical records of algal blooms. The deep learning models adopted include CNN-based architectures (i.e., CNN, Inception Resnet, and Autoencoder) and CNN-LSTM-based architectures (i.e., CNN-LSTM, Resnet-LSTM, and Autoencoder-LSTM). Our results demonstrated that deep learning models substantially outperformed the baseline linear interpolation method in imputing spectral band values within artificially masked regions. Among these models, CNN delivered the best performance across most lakes. Furthermore, we evaluated the performance of algal bloom indices (i.e., Green/Red and NDCI) derived from the imputed imagery by comparing them with the observed data. Our results demonstrate that deep learning models are effective for imputing missing data in PlanetScope SuperDove imagery, enabling more reliable applications in water monitoring.
Subjects:
Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Applications (stat.AP)
Cite as: arXiv:2605.24003 [cs.CV]
(or arXiv:2605.24003v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2605.24003
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Rohitash Chandra [view email] [v1] Tue, 19 May 2026 05:31:09 UTC (9,726 KB)
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