Semi-supervised Classification of Land Use and Cover based on Multispectral Data in Southern Piauí
Abstract
In this article, a study was conducted to map agricultural activities in the Urucuía region, employing semi-supervised learning techniques. Through this approach, a partially labeled dataset was utilized, enabling a more comprehensive analysis of the region. The applied models demonstrated satisfactory results, proving to be efficient in identifying and classifying the various predefined classes within the study area.
Keywords:
Classification, semi-supervised, Bands, Spectral
References
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Zhu, X. and Ghahramani, Z. (2002). Learning from Labeled and Unlabeled Data with Label Propagation. ProQuest Number: INFORMATION TO ALL USERS.
Aguiar, R. and Gomes, J. (2004). Projeto Cadastro de Fontes de Abastecimento por Água Subterrânea, Estado do Piauí: Diagnóstico do Município de Urucuí. Serviço Geológico do Brasil. CPRM, Fortaleza (13pp).
Blum, A. and Mitchell, T. (1998). Combining Labeled and Unlabeled Data with Co-Training. In Proceedings of the Eleventh Annual Conference on Computational Learning Theory, pages 92–100.
Colditz, R., Schmidt, M., Conrad, C., Hansen, M., and Dech, S. (2011). Land Cover Classification with Coarse Spatial Resolution Data to Derive Continuous and Discrete Maps for Complex Regions. Remote Sensing of Environment, 115(12):3264–3275.
Feranec, J., Jaffrain, G., Soukup, T., and Hazeu, G. (2010). Determining Changes and Flows in European Landscapes 1990–2000 Using Corine Land Cover Data. Applied Geography, 30(1):19–35.
IBGE (2017). Geográficas Imediatas e Regiões Geográficas Intermediárias: 2017/IBGE.
Koda, S., Melgani, F., and Nishii, R. (2019). Unsupervised Spectral-Spatial Feature Extraction with Generalized Autoencoder for Hyperspectral Imagery. IEEE Geoscience and Remote Sensing Letters.
Li, Y., Zhang, Y., Huang, X., Zhu, H., and Ma, J. (2017). Large-Scale Remote Sensing Image Retrieval by Deep Hashing Neural Networks. IEEE Transactions on Geoscience and Remote Sensing, 56(2):950–965.
Liu, P., Di, L., Du, Q., and Wang, L. (2018). Remote Sensing Big Data: Theory, Methods, and Applications.
Yao, X., Yang, L., Cheng, G., Han, J., and Guo, L. (2019). Scene Classification of High-Resolution Remote Sensing Images via Self-Paced Deep Learning. In IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium, pages 521–524. IEEE.
Yarowsky, D. (1995). Unsupervised Word Sense Disambiguation Rivaling Supervised Methods. In 33rd Annual Meeting of the Association for Computational Linguistics, pages 189–196.
Zhu, Q., Sun, X., Zhong, Y., and Zhang, L. (2019). High-Resolution Remote Sensing Image Scene Understanding: A Review. In IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium, pages 3061–3064. IEEE.
Zhu, X. and Ghahramani, Z. (2002). Learning from Labeled and Unlabeled Data with Label Propagation. ProQuest Number: INFORMATION TO ALL USERS.
Published
2023-10-19
How to Cite
LIMA, Bruno Vicente Alves de; FIGUEREDO, Elayne da Silva.
Semi-supervised Classification of Land Use and Cover based on Multispectral Data in Southern Piauí. In: UNIFIED COMPUTING MEETING OF PIAUÍ (ENUCOMPI), 16. , 2023, Piripiri/PI.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2023
.
p. 97-104.
DOI: https://doi.org/10.5753/enucompi.2023.26622.
