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A Deep Multitask Semisupervised Learning Approach for Chlorophyll-a Retrieval from Remote Sensing Images
| Content Provider | MDPI |
|---|---|
| Author | Ilteralp, Melike Ariman, Sema Aptoula, Erchan |
| Copyright Year | 2021 |
| Description | 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. |
| Starting Page | 18 |
| e-ISSN | 20724292 |
| DOI | 10.3390/rs14010018 |
| Journal | Remote Sensing |
| Issue Number | 1 |
| Volume Number | 14 |
| Language | English |
| Publisher | MDPI |
| Publisher Date | 2021-12-22 |
| Access Restriction | Open |
| Subject Keyword | Remote Sensing Time Series Analysis Water Quality Convolutional Neural Network Regression Semisupervised Learning |
| Content Type | Text |
| Resource Type | Article |
