Transferable Deep Learning from Time Series of Landsat Data for National Land-Cover Mapping with Noisy Labels: A Case Study of China

Large-scale land-cover classification using a supervised algorithm is a challenging task. Enormous efforts have been made to manually process and check the production of national land-cover maps. This has led to complex pre- and post-processing and even the production of inaccurate mapping products...

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Autores principales:	Xuemei Zhao, Danfeng Hong, Lianru Gao, Bing Zhang, Jocelyn Chanussot
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	classification deep learning Landsat multispectral national land-cover mapping transfer learning Science Q
Acceso en línea:	https://doaj.org/article/c09579ddb984493782dd2cff7140ba9d
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spelling	oai:doaj.org-article:c09579ddb984493782dd2cff7140ba9d2021-11-11T18:49:33ZTransferable Deep Learning from Time Series of Landsat Data for National Land-Cover Mapping with Noisy Labels: A Case Study of China10.3390/rs132141942072-4292https://doaj.org/article/c09579ddb984493782dd2cff7140ba9d2021-10-01T00:00:00Zhttps://www.mdpi.com/2072-4292/13/21/4194https://doaj.org/toc/2072-4292Large-scale land-cover classification using a supervised algorithm is a challenging task. Enormous efforts have been made to manually process and check the production of national land-cover maps. This has led to complex pre- and post-processing and even the production of inaccurate mapping products from large-scale remote sensing images. Inspired by the recent success of deep learning techniques, in this study we provided a feasible automatic solution for improving the quality of national land-cover maps. However, the application of deep learning to national land-cover mapping remains limited because only small-scale noisy labels are available. To this end, a mutual transfer network MTNet was developed. MTNet is capable of learning better feature representations by mutually transferring pre-trained models from time-series of data and fine-tuning current data. An interactive training strategy such as this can effectively alleviate the effects of inaccurate or noisy labels and unbalanced sample distributions, thus yielding a relatively stable classification system. Extensive experiments were conducted by focusing on several representative regions to evaluate the classification results of our proposed method. Quantitative results showed that the proposed MTNet outperformed its baseline model about 1%, and the accuracy can be improved up to 6.45% compared with the model trained by the training set of another year. We also visualized the national classification maps generated by MTNet for two different time periods to quantitatively analyze the performance gain. It was concluded that the proposed MTNet provides an efficient method for large-scale land cover mapping.Xuemei ZhaoDanfeng HongLianru GaoBing ZhangJocelyn ChanussotMDPI AGarticleclassificationdeep learningLandsatmultispectralnational land-cover mappingtransfer learningScienceQENRemote Sensing, Vol 13, Iss 4194, p 4194 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	classification deep learning Landsat multispectral national land-cover mapping transfer learning Science Q
spellingShingle	classification deep learning Landsat multispectral national land-cover mapping transfer learning Science Q Xuemei Zhao Danfeng Hong Lianru Gao Bing Zhang Jocelyn Chanussot Transferable Deep Learning from Time Series of Landsat Data for National Land-Cover Mapping with Noisy Labels: A Case Study of China
description	Large-scale land-cover classification using a supervised algorithm is a challenging task. Enormous efforts have been made to manually process and check the production of national land-cover maps. This has led to complex pre- and post-processing and even the production of inaccurate mapping products from large-scale remote sensing images. Inspired by the recent success of deep learning techniques, in this study we provided a feasible automatic solution for improving the quality of national land-cover maps. However, the application of deep learning to national land-cover mapping remains limited because only small-scale noisy labels are available. To this end, a mutual transfer network MTNet was developed. MTNet is capable of learning better feature representations by mutually transferring pre-trained models from time-series of data and fine-tuning current data. An interactive training strategy such as this can effectively alleviate the effects of inaccurate or noisy labels and unbalanced sample distributions, thus yielding a relatively stable classification system. Extensive experiments were conducted by focusing on several representative regions to evaluate the classification results of our proposed method. Quantitative results showed that the proposed MTNet outperformed its baseline model about 1%, and the accuracy can be improved up to 6.45% compared with the model trained by the training set of another year. We also visualized the national classification maps generated by MTNet for two different time periods to quantitatively analyze the performance gain. It was concluded that the proposed MTNet provides an efficient method for large-scale land cover mapping.
format	article
author	Xuemei Zhao Danfeng Hong Lianru Gao Bing Zhang Jocelyn Chanussot
author_facet	Xuemei Zhao Danfeng Hong Lianru Gao Bing Zhang Jocelyn Chanussot
author_sort	Xuemei Zhao
title	Transferable Deep Learning from Time Series of Landsat Data for National Land-Cover Mapping with Noisy Labels: A Case Study of China
title_short	Transferable Deep Learning from Time Series of Landsat Data for National Land-Cover Mapping with Noisy Labels: A Case Study of China
title_full	Transferable Deep Learning from Time Series of Landsat Data for National Land-Cover Mapping with Noisy Labels: A Case Study of China
title_fullStr	Transferable Deep Learning from Time Series of Landsat Data for National Land-Cover Mapping with Noisy Labels: A Case Study of China
title_full_unstemmed	Transferable Deep Learning from Time Series of Landsat Data for National Land-Cover Mapping with Noisy Labels: A Case Study of China
title_sort	transferable deep learning from time series of landsat data for national land-cover mapping with noisy labels: a case study of china
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/c09579ddb984493782dd2cff7140ba9d
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Transferable Deep Learning from Time Series of Landsat Data for National Land-Cover Mapping with Noisy Labels: A Case Study of China

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