The DOI matches its Crossref record; 143 of the 143 references it cites were found in a registry or an index.
Deep Learning for Land Use and Land Cover Classification Based on Hyperspectral and Multispectral Earth Observation Data: A Review
Open accessAbstract
Lately, with deep learning outpacing the other machine learning techniques in classifying images, we have witnessed a growing interest of the remote sensing community in employing these techniques for the land use and land cover classification based on multispectral and hyperspectral images; the number of related publications almost doubling each year since 2015 is an attest to that. The advances in remote sensing technologies, hence the fast-growing volume of timely data available at the global scale, offer new opportunities for a variety of applications. Deep learning being significantly successful in dealing with Big Data, seems to be a great candidate for exploiting the potentials of such complex massive data. However, there are some challenges related to the ground-truth, resolution, and the nature of data that strongly impact the performance of classification. In this paper, we review the use of deep learning in land use and land cover classification based on multispectral and hyperspectral images and we introduce the available data sources and datasets used by literature studies; we provide the readers with a framework to interpret the-state-of-the-art of deep learning in this context and offer a platform to approach methodologies, data, and challenges of the field.
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Mutual Attention Inception Network for Remote Sensing Visual Question Answering
Zheng, X., Wang, B., Du, X., & Lu, X. (2022) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2021.3079918 · Cited by 159
Resolution Enhancement of Remotely Sensed Land Surface Temperature: Current Status and Perspectives
Mao, Q., Peng, J., & Wang, Y. (2021) · Remote Sensing · doi:10.3390/rs13071306 · Cited by 74
Self-Supervised Spatio-Temporal Representation Learning of Satellite Image Time Series
Dumeur, I., Valero, S., & Inglada, J. (2024) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2024.3358066 · Cited by 39
Hidden Path Selection Network for Semantic Segmentation of Remote Sensing Images
Yang, K., Tong, X.-Y., Xia, G.-S., Shen, W., & Zhang, L. (2022) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2022.3197334 · Cited by 22
Metadata
- Authors
- Vali, A., Comai, S., & Matteucci, M.
- Venue
- Remote Sensing
- Publisher
- MDPI AG
- Published
- 2020-08-03
- Volume
- 12
- Issue
- 15
- Pages
- 2495
- ISSN
- 2072-4292 (ISSN-L)
- OpenAlex
- W3047317383
- Cited by
- 455
- References
- 143
- FWCI
- 24.06
- Type
- journal article
- Language
- en
- Open access
- gold
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- Vali, A., Comai, S., & Matteucci, M. (2020). Deep Learning for Land Use and Land Cover Classification Based on Hyperspectral and Multispectral Earth Observation Data: A Review. Remote Sensing. https://doi.org/10.3390/rs12152495
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