VConstruct: Filling Gaps in Chl-a Data Using a Variational Autoencoder

TL;DR

This paper introduces VConstruct, a variational autoencoder-based method for reconstructing missing Chlorophyll-a satellite data, offering faster processing and multiple reconstructions, with accuracy comparable to existing methods.

Contribution

The paper presents a novel VAE-based approach for Chlorophyll-a data reconstruction that reduces computation time and enables multiple potential outputs.

Findings

VConstruct achieves accuracy close to DINEOF.

VConstruct significantly reduces computation time.

VConstruct can generate multiple reconstructions.

Abstract

Remote sensing of Chlorophyll-a is vital in monitoring climate change. Chlorphyll-a measurements give us an idea of the algae concentrations in the ocean, which lets us monitor ocean health. However, a common problem is that the satellites used to gather the data are commonly obstructed by clouds and other artifacts. This means that time series data from satellites can suffer from spatial data loss. There are a number of algorithms that are able to reconstruct the missing parts of these images to varying degrees of accuracy, with Data INterpolating Empirical Orthogonal Functions (DINEOF) being the current standard. However, DINEOF is slow, suffers from accuracy loss in temporally homogenous waters, reliant on temporal data, and only able to generate a single potential reconstruction. We propose a machine learning approach to reconstruction of Chlorophyll-a data using a Variational Autoencoder (VAE). Our accuracy results to date are competitive with but slightly less accurate than DINEOF. We show the benefits of our method including vastly decreased computation time and ability to generate multiple potential reconstructions. Lastly, we outline our planned improvements and future work.