A Deep Multitask Semisupervised Learning Approach for Chlorophyll-a Retrieval from Remote Sensing Images

A Deep Multitask Semisupervised Learning Approach for Chlorophyll-a Retrieval from Remote Sensing Images

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...

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Bibliographic Details
Main Authors: Melike Ilteralp, Sema Ariman, Erchan Aptoula
Format: Article
Language:English
Published: MDPI AG 2021-12-01
Series:Remote Sensing
Subjects:
time series analysis
water quality
convolutional neural network
regression
semisupervised learning
Online Access:https://www.mdpi.com/2072-4292/14/1/18

Internet

https://www.mdpi.com/2072-4292/14/1/18

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