Abstract
This article addresses the scarcity of labeled data in multitemporal remote sensing image analysis, and especially in the context of Chlorophyll-a (Chl-a) estimation for inland water quality assessment. We propose a multitask CNN architecture that can exploit unlabeled satellite imagery and that can be generalized to other multitemporal remote sensing image analysis contexts where the target parameter exhibits seasonal fluctuations. Specifically, Chl-a estimation is set as the main task, and an unlabeled sample’s month classification is set as an auxiliary network task. The proposed approach is validated with multitemporal/spectral Sentinel-2 images of Lake Balik in Turkey using in situ measurements acquired during 2017–2019. We show that harnessing unlabeled data through multitask learning improves water quality estimation performance.
Highlights
We propose to achieve this via a multitask double-branch convolutional neural networks (CNNs), whose main task is Chl-a level regression via training with labeled input, and whose auxiliary task is the classification of unlabeled samples to its month of acquisition
CNN [28] performs to multilayer perceptrons (MLP) and single-task CNN models in that its performance is limited with the samples of the labeled dataset
We proposed the first neural network architecture for water quality estimation that can exploit unlabeled satellite imagery
Summary
Besides containing invaluable aquatic habitats and rich biodiversity, lake water constitutes a significant resource with a wide range of civil and industrial purposes, ranging from urban water supply and agricultural irrigation to fishery and recreation. Chlorophyll-a (Chl-a) concentration is one of the most commonly used water quality parameters. The traditional measurement of Chl-a concentration is a prolonged and labor-intensive process. It involves arduous and repeated sample acquisitions from the field and their subsequent laboratory analysis. The end results, in addition, only reflect the water quality of a handful of measurement sites at most, at often large temporal intervals, or even fewer, in cases where the lake is geographically inaccessible [4]
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