Deep Learning para Classificação Supervisionada de Imagens CBERS-4A da Área Urbana de Rio Claro (SP)

Conteúdo do artigo principal

Danilo Marques Magalhães
https://orcid.org/0000-0001-9306-4326
Julya Paes de Souza
https://orcid.org/0009-0005-4162-0024
Edgar Auler Galvão de França
https://orcid.org/0009-0002-5625-8011

Resumo

O presente artigo tem por objetivo avaliar a acurácia do mapeamento do uso e cobertura da terra, realizado em um trecho da área urbana de Rio Claro (SP), a partir de técnicas de Deep Learning e utilizando uma imagem CBERS-4A (WPM) com 2 m de resolução espacial. Foi estruturada uma rede neural convolucional U-Net a partir de script em Python, utilizando as bibliotecas Keras e Tensor Flow. A verdade terrestre, utilizada para treinamento e verificação da acurácia do modelo, foi elaborada por meio de classificação supervisionada da mesma imagem no software ArcGIS Pro, utilizando o algoritmo Support Vector Machine (SVM) e procedimentos de pós-classificação, incluindo aplicação de filtro majoritário e edição manual de pixels. O resultado obtido pela U-Net foi comparado ao resultado obtido pelo SVM (sem pós-classificação), visando compreender se há ganhos de acurácia, tendo em vista o maior esforço humano, para a criação do ground truth, e computacional, para processamento dos dados, inerente às técnicas de Deep Learning. Para isso, ambos os resultados foram submetidos a avaliação de acurácia utilizando as métricas Overall Accuracy, Precision, Recall, F1 Score e Kappa. Constatou-se que o modelo U-Net apresenta melhor acurácia em todas elas, destacando-se o aumento da Precision de 0,48 (SVM) para 0,78 (U-Net). Tais resultados indicam o potencial das técnicas de Deep Learning para o mapeamento do uso e cobertura da terra em áreas urbanas a partir de imagens de alta resolução, o que pode contribuir, de modo significativo, para ações de planejamento e gestão territorial nos municípios brasileiros

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Detalhes do artigo

Seção

Sensoriamento Remoto

Biografia do Autor

Danilo Marques Magalhães, Universidade Estadual Paulista "Júlio de Mesquita Filho" (Unesp)

Danilo Marques de Magalhães, natural de Belo Horizonte-MG (1983), é Bacharel (2010), Mestre (2013) e Doutor (2021) em Geografia pela UFMG, tendo realizado estágio sanduíche na UniBo (Itália). É Professor Assistente Doutor do Departamento de Geografia e Planejamento Ambiental da Unesp de Rio Claro (SP) e do Programa de Pós-Graduação em Geografia da mesma instituição. Coordena o Grupo MAPEAR de Pesquisa e Extensão, orientando trabalhos na área de Sensoriamento Remoto e Sistemas de Informações Geográficas. Atualmente, coordena o projeto de pesquisa "Uso de Deep Learning para mapeamento de uso e cobertura da terra a partir de imagens CBERS-4A".

Como Citar

MAGALHÃES, Danilo Marques; DE SOUZA, Julya Paes; DE FRANÇA, Edgar Auler Galvão. Deep Learning para Classificação Supervisionada de Imagens CBERS-4A da Área Urbana de Rio Claro (SP). Revista Brasileira de Cartografia, [S. l.], v. 77, n. 0a, 2025. DOI: 10.14393/rbcv77n0a-75495. Disponível em: https://seer.ufu.br/index.php/revistabrasileiracartografia/article/view/75495. Acesso em: 13 jun. 2025.

Referências

Abdollahi, A., Pradhan, B., Shukla, N., Chakraborty, S., & Alamri, A. (2021). Multi-object segmentation in complex urban scenes from high-resolution remote sensing data. Remote Sensing, 13(18), 3710. https://doi.org/10.3390/rs13183710

Amani, M., Ghorbanian, A., Ahmadi, S. A., Kakooei, M., Moghimi, A., Mirmazloumi, S. M., Alizadeh Moghaddam, S. H., Mahdavi, S., Ghahremanloo, M., Parsian, S., Wu, Q., & Brisco, B. (2020). Google Earth Engine cloud computing platform for remote sensing big data applications: A comprehensive review. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 13, 5326–5350. https://doi.org/10.1109/JSTARS.2020.3011956

Braga, J. R. G., Peripato, V., Dalagnol, R., Ferreira, M. P., Tarabalka, Y., Aragão, L. E. O. C., Campos Velho, H. F. de, Shiguemori, E. H., & Wagner, F. H. (2020). Tree crown delineation algorithm based on a convolutional neural network. Remote Sensing, 12(8), 1288. https://doi.org/10.3390/rs12081288

Brownlee, J. (2019). Deep learning for computer vision: Image classification, object detection and face recognition in Python. Machine Learning Mastery.

Carranza-García, M., García-Gutiérrez, J., & Riquelme, J. (2019). A framework for evaluating land use and land cover classification using convolutional neural networks. Remote Sensing, 11(3), 274. https://doi.org/10.3390/rs11030274

Chi, M., Plaza, A., Benediktsson, J. A., Sun, Z., Shen, J., & Zhu, Y. (2016). Big data for remote sensing: Challenges and opportunities. Proceedings of the IEEE, 104(11), 2207. https://doi.org/10.1109/JPROC.2016.2598228

Chollet, F. (2021). Deep learning with Python (2nd ed.). Manning Publications.

Data Science Academy (DSA). (2022). Deep learning book. https://www.deeplearningbook.com.br/

Deng, T., Liu, X. & Mao, G. (2022). Improved YOLOv5 Based on Hybrid Domain Attention for Small Object Detection in Optical Remote Sensing Images. Eletronics, 11(17), 2657. https://doi.org/10.3390/electronics11172657

Foody, G. M. (2009). Sample size determination for image classification accuracy assessment and comparison. International Journal of Remote Sensing, 30(20), 5273-5291. https://doi.org/10.1080/01431160802582899

Ge, P., He, J., Zhang, S., Zhang, L., & She, J. F. (2019). An integrated framework combining multiple human activity features for land use classification. ISPRS International Journal of Geo-Information, 8(2), 90. https://doi.org/10.3390/ijgi8020090

Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. MIT Press.

Hao, X., Liu, L., Yang, R., Yin, L., Zhang, L. & Li, X. (2023). A Review of Data Augmentation Methods of Remote Sensing Image Target Recognition. Remote Sensing, 15(3), 827. https://doi.org/10.3390/rs15030827

Johnson, J. M., & Khoshgoftaaar, T. M. (2019). Survey on deep learning with class imbalance. Journal of Big Data, 6(1), 27. https://doi.org/10.1186/s40537-019-0192-0

Jozdani, S. E., Johnson, B. A., & Chen, D. (2019). Comparing deep neural networks, ensemble classifiers, and support vector machine algorithms for object-based urban land use/land cover classification. Remote Sensing, 11(14), 1713. https://doi.org/10.3390/rs11141713

Karimian, R., Rangzan, K., Karimi, D. & Einali, G. (2024). Spatiotemporal Monitoring of Land Use-Land Cover and Its Relationship with Land Surface Temperature Changes Based on Remote Sensing, GIS, and Deep Learning. J Indian Soc Remote Sens, 52, 2461. https://doi.org/10.1007/s12524-024-01958-3

Klippel, S. (2022). CBERS4A downloader QGIS plugin. https://github.com/sandroklippel/cbers4a

Kuras, A., et al. (2021). Hyperspectral and Lidar data applied to the urban land cover machine learning and neural-network-based classification: A review. Remote Sensing, 13(17), 3393. https://doi.org/10.3390/rs13173393

Li, Z., Wang, Y., Zhang, N., Zhang, Y., Zhao, Z., Xu, D., Ben, G., & Gao, Y. (2022). Deep Learning-Based Object Detection Techniques for Remote Sensing Images: A Survey. Remote sensing, 14(10), 2385. https://doi.org/10.3390/rs14102385

Lv, Z., Huang, H., Sun, W., Lei, T., Benediktsson, J. A. & Li, J. (2023). Novel Enhanced UNet for Change Detection Using Multimodal Remote Sensing Image. IEEE Geoscience and Remote Sensing Letters, 20, 1. 10.1109/LGRS.2023.3325439

Ma, L., Liu, Y., Zhang, X., Ye, Y., Yin, G., & Johnson, B. A. (2019). Deep learning in remote sensing applications: A meta-analysis and review. ISPRS Journal of Photogrammetry and Remote Sensing, 152, 166–177. https://doi.org/10.1016/j.isprsjprs.2019.05.019

Magalhães, D. M. (2024). Avaliação da acurácia da classificação supervisionada de imagens de sensoriamento remoto utilizando o software ArcGIS Pro. In N. I. Ladwig, T. Sutil, C. H. R. da Silva, & B. Giaccom (Eds.), Planejamento e gestão territorial (1st ed., pp. 141-164). Pedro e João. http://dx.doi.org/10.51795/9786526514276

Malik, K., Robertson, C., Braun, D., & Greig, C. (2021). U-Net convolutional neural network models for detecting and quantifying placer mining disturbances at watershed scales. International Journal of Applied Earth Observation and Geoinformation, 104, 102510. https://doi.org/10.1016/j.jag.2021.102510

Maxwell, A. E., Warner, T. A. & Guillén, L. A. (2021a). Accuracy Assessment in Convolutional Neural Network-Based Deep Learning Remote Sensing Studies—Part 1: Literature Review. Remote Sensing, 13(13), 2450. https://doi.org/10.3390/rs13132450

Maxwell, A. E., Warner, T. A. & Guillén, L. A. (2021b). Accuracy Assessment in Convolutional Neural Network-Based Deep Learning Remote Sensing Studies—Part 2: Recommendations and Best Practices. Remote Sensing, 13(13), 2591. https://doi.org/10.3390/rs13132591

Morales-Barquero, L., Lyons, M. B., Phinn, S. R., & Roelfsema, C. M. (2019). Trends in Remote Sensing Accuracy Assessment Approaches in the Context of Natural Resources. Remote Sensing, 11(19), 2305. https://doi.org/10.3390/rs11192305

Nigar, A., Li, Y., Jat Baloch, M. Y., Alrefaei, A. F. & Almutairi, M. H. (2024). Comparison of machine and deep learning algorithms using Google Earth Engine and Python for land classifications. Frontiers in Environmental Science, 12, 01. 10.3389/fenvs.2024.1378443

Odenyo, V. A. O., & Pettry, D. E. (1977). Land-use mapping by machine processing of LANDSAT-1 data. Photogrammetric Engineering and Remote Sensing, 43(4), 515–523.

Pabi, O. (2007). Understanding land-use/cover change process for land and environmental resources use management policy in Ghana. GeoJournal, 68(4), 369–383. https://doi.org/10.1007/s10708-007-9120-7

Parente, L., Taquary, E., Silva, A. P., Souza, C., & Ferreira, L. (2019). Next generation mapping: Combining deep learning, cloud computing, and big remote sensing data. Remote Sensing, 11(23), 2881. https://doi.org/10.3390/rs11232881

Picoli, M. C. A., Simoes, R., Chaves, M., Santos, L. A., Sanchez, A., Soares, A., Sanches, I. D., Ferreira, K. R., & Queiroz, G. R. (2020). CBERS data cube: A powerful technology for mapping and monitoring Brazilian biomes. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, V-3–2020, 533–539. https://doi.org/10.5194/isprs-annals-V-3-2020-533-2020

Ponti, M. A., & Costa, G. B. P. (2017). Como funciona o deep learning. In: Tópicos em gerenciamento de dados e informações (1st ed., p. 31). SBC.

Prakash, D. P., & Rao, A. S. K. R. (2017). Deep learning cookbook: Solve complex neural net problems with TensorFlow, H2O, and MXNET. Packt.

Sheykhmousa, M., Mahdianpari, M., Ghanbari, H., Mohammadimanesh, F., Ghamisi, P. & Homayouni, S. (2020). Support Vector Machine Versus Random Forest for Remote Sensing Image Classification: A Meta-Analysis and Systematic Review. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 13, 6308. 10.1109/JSTARS.2020.3026724.

Solórzano, J. V., Mas, J. F., Gao, Y., & Gallardo-Cruz, J. A. (2021). Land use land cover classification with U-Net: Advantages of combining Sentinel-1 and Sentinel-2 imagery. Remote Sensing, 13(18), 3600. https://doi.org/10.3390/rs13183600

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, 12(15), 2495. https://doi.org/10.3390/rs12152495

Vázquez, F. (2017, December 21). Deep learning made easy with deep cognition. Becoming Human: Artificial Intelligence Magazine. https://becominghuman.ai/deep-learning-made-easy-with-deep-cognition-403fbe445351

Wagner, F. H., Dalagnol, R., Tarabalka, Y., Segantine, T. Y. F., Thomé, R., & Hirye, M. C. M. (2020a). U-Net-Id, an instance segmentation model for building extraction from satellite images — Case study in the Joanópolis City, Brazil. Remote Sensing, 12(10), 1544. https://doi.org/10.3390/rs12101544

Wagner, F. H., Dalagnol, R., Tagle Casapia, X., Streher, A. S., Phillips, O. L., Gloor, E., & Aragão, L. E. O. C. (2020b). Regional mapping and spatial distribution analysis of canopy palms in an Amazon forest using deep learning and VHR images. Remote Sensing, 12(14), 2225. https://doi.org/10.3390/rs12142225

Wang, L., Zhang, M., Gao, X., & Shi, W. (2024). Advances and challenges in deep learning-based change detection for remote sensing images: A review through various learning paradigms. Remote Sensing, 16, 804. https://doi.org/10.3390/rs16050804

Yan, C., Fan, X., Fan, J., & Wang, N. (2022). Improved U-Net remote sensing classification algorithm based on multi-feature fusion perception. Remote Sensing, 14(5), 1118. https://doi.org/10.3390/rs14051118

Yang, Y., Wan, W., Huang, S., Lin, P., & Que, Y. (2017). A novel pan-sharpening framework based on matting model and multiscale transform. Remote Sensing, 9(4), 391. https://doi.org/10.3390/rs9040391

Yu, J., Zeng, P., Yu, Y., Yu, H., Huang, L., & Zhou, D. (2022). A combined convolutional neural network for urban land-use classification with GIS data. Remote Sensing, 14(5), 1128. https://doi.org/10.3390/rs14051128

Zhang, X., Du, L., Tan, S., Wu, F., Zhu, L., Zeng, Y., & Wu, B. (2021). Land use and land cover mapping using RapidEye imagery based on a novel band attention deep learning method in the Three Gorges Reservoir area. Remote Sensing, 13(6), 1225. https://doi.org/10.3390/rs13061225

Zhang, P., Wu, Y., Li, C., Li, R., Yao, H., Zhang, Y., Zhang, G. & Li, D. (2023). National-Standards- and Deep-Learning-Oriented Raster and Vector Benchmark Dataset (RVBD) for Land-Use/Land-Cover Mapping in the Yangtze River Basin. Remote Sensing, 14(15), 3907. https://doi.org/10.3390/rs15153907

Zhao, Z.-Q., Zheng, P., Xu, S.-T., & Wu, X. (2019). Object detection with deep learning: A review. IEEE Transactions on Neural Networks and Learning Systems. https://doi.org/10.1109/TNNLS.2019.2897602

Zhao, S., Tu, K., Ye, S., Tang, H., Hu, Y. & Xie, C. (2023). Land Use and Land Cover Classification Meets Deep Learning: A Review. Remote Sensing, 23(21), 8966. https://doi.org/10.3390/s23218966

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