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Spatiotemporal Assessment of Satellite Image Time Series for Land Cover Classification Using Deep Learning Techniques: A Case Study of Reunion Island, France
Abstract
Current Earth observation systems generate massive amounts of satellite image time series to keep track of geographical areas over time to monitor and identify environmental and climate change. Efficiently analyzing such data remains an unresolved issue in remote sensing. In classifying land cover, utilizing SITS rather than one image might benefit differentiating across classes because of their varied temporal patterns. The aim was to forecast the land cover class of a group of pixels as a multi-class single-label classification problem given their time series gathered using satellite images. In this article, we exploit SITS to assess the capability of several spatial and temporal deep learning models with the proposed architecture. The models implemented are the bidirectional gated recurrent unit (GRU), temporal convolutional neural networks (TCNN), GRU + TCNN, attention on TCNN, and attention of GRU + TCNN. The proposed architecture integrates univariate, multivariate, and pixel coordinates for the Reunion Island’s landcover classification (LCC). the evaluation of the proposed architecture with deep neural networks on the test dataset determined that blending univariate and multivariate with a recurrent neural network and pixel coordinates achieved increased accuracy with higher F1 scores for each class label. The results suggest that the models also performed exceptionally well when executed in a partitioned manner for the LCC task compared to the temporal models. This study demonstrates that using deep learning approaches paired with spatiotemporal SITS data addresses the difficult task of cost-effectively classifying land cover, contributing to a sustainable environment.
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Authors (5)
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Naik Nitesh Navnath
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Kandasamy Chandrasekaran
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Venkatesan Meenakshi Sundaram
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Prabhavathy Panneer
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Full text
- Publication version
- Accepted or Published Version
- DOI:
- Digital Object Identifier (open in new tab) 10.3390/rs14205232
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Details
- Category:
- Articles
- Type:
- artykuły w czasopismach
- Published in:
-
Remote Sensing
no. 14,
ISSN: 2072-4292 - Language:
- English
- Publication year:
- 2022
- Bibliographic description:
- Navnath N. N., Chandrasekaran K., Stateczny A., Sundaram V. M., Panneer P.: Spatiotemporal Assessment of Satellite Image Time Series for Land Cover Classification Using Deep Learning Techniques: A Case Study of Reunion Island, France// Remote Sensing -Vol. 14,iss. 20 (2022), s.5232-
- DOI:
- Digital Object Identifier (open in new tab) 10.3390/rs14205232
- Sources of funding:
-
- Free publication
- Verified by:
- Gdańsk University of Technology
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