Classificação Semissupervisionada do Uso e Cobertura do Solo baseada em Dados Multiespectrais no Sul do Piauí
Resumo
Neste artigo, foi realizado um trabalho para mapear atividades agrícolas na região de Urucuía, utilizando técnicas de aprendizado semissupervisionado. Por meio desse método, um conjunto de dados parcialmente rotulados foi utilizado, o que possibilitou uma análise mais abrangente da região. Os modelos utilizados demonstraram resultados satisfatórios, mostrando-se eficientes na identificação e classificação das diversas classes definidas na área de estudo.
Palavras-chave:
Classificação, semissuperivionada, Bandas, Espectrais
Referências
Adam, E., Mutanga, O., Odindi, J., and Abdel-Rahman, E. M. (2014). Land-use/cover classification in a heterogeneous coastal landscape using RapidEye imagery: evaluating the performance of random forest and support vector machines classifiers. International Journal of Remote Sensing, 35(10):3440–3458.
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.
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.
Publicado
19/10/2023
Como Citar
LIMA, Bruno Vicente Alves de; FIGUEREDO, Elayne da Silva.
Classificação Semissupervisionada do Uso e Cobertura do Solo baseada em Dados Multiespectrais no Sul do Piauí. In: ENCONTRO UNIFICADO DE COMPUTAÇÃO DO 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.
