953758497d
Move arquivos de analise, apresentacao e outputs para subpastas dedicadas; remove caches orfaos e tex nao usados. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
121 lines
3.9 KiB
BibTeX
121 lines
3.9 KiB
BibTeX
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% Referências -- Artigo: Classificação de Imagens RGB com SVM
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% e Atributos Extraídos por Transfer Learning (MobileNetV2)
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% Curso CEAO-802 | Instituto Tecnológico de Aeronáutica
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% ======================================================================
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% -------- Referência-base do trabalho (galoa / SBSR 2023) --------
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@inproceedings{lacerda2023,
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author = {Lacerda, Marielcio Gon{\c{c}}alves and
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Habermann, Mateus and
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Lacerda, Camila Souza dos Anjos and
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Roos, Daniel Rodrigues and
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K{\"o}rting, Thales Sehn and
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Kux, Hermann Johann Heinrich},
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title = {Classifica{\c{c}}{\~a}o de Imagens Utilizando Imagens {RGB}
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e Termal Obtidas por {ARPS} de Pequeno Porte},
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booktitle = {Anais do XX Simp{\'o}sio Brasileiro de Sensoriamento Remoto (SBSR)},
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year = {2023},
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address = {Florian{\'o}polis, SC, Brasil},
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pages = {3368--3371},
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url = {https://proceedings.science/p/164924}
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}
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% -------- Teoria SVM --------
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@book{vapnik1995,
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author = {Vapnik, Vladimir N.},
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title = {The Nature of Statistical Learning Theory},
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publisher = {Springer},
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address = {New York},
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year = {1995},
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doi = {10.1007/978-1-4757-2440-0}
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}
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@article{cortes1995,
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author = {Cortes, Corinna and Vapnik, Vladimir},
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title = {Support-Vector Networks},
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journal = {Machine Learning},
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volume = {20},
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number = {3},
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pages = {273--297},
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year = {1995},
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doi = {10.1007/BF00994018}
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}
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% -------- SVM em Sensoriamento Remoto --------
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@article{melgani2004,
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author = {Melgani, Farid and Bruzzone, Lorenzo},
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title = {Classification of Hyperspectral Remote Sensing Images
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with Support Vector Machines},
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journal = {{IEEE} Transactions on Geoscience and Remote Sensing},
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volume = {42},
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number = {8},
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pages = {1778--1790},
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year = {2004},
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doi = {10.1109/TGRS.2004.831865}
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}
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@article{campsvalls2005,
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author = {Camps-Valls, Gustavo and Bruzzone, Lorenzo},
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title = {Kernel-Based Methods for Hyperspectral Image Classification},
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journal = {{IEEE} Transactions on Geoscience and Remote Sensing},
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volume = {43},
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number = {6},
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pages = {1351--1362},
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year = {2005},
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doi = {10.1109/TGRS.2005.846154}
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}
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% -------- Transfer Learning / Redes Convolucionais --------
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@inproceedings{sandler2018,
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author = {Sandler, Mark and Howard, Andrew and Zhu, Menglong and
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Zhmoginov, Andrey and Chen, Liang-Chieh},
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title = {{MobileNetV2}: Inverted Residuals and Linear Bottlenecks},
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booktitle = {Proceedings of the {IEEE} Conference on Computer Vision
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and Pattern Recognition (CVPR)},
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year = {2018},
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pages = {4510--4520},
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doi = {10.1109/CVPR.2018.00474}
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}
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@article{cheng2020,
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author = {Cheng, Gong and Xie, Xingxing and Han, Junwei and
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Guo, Lei and Xia, Gui-Song},
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title = {Remote Sensing Image Scene Classification Meets Deep Learning:
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Challenges, Methods, Benchmarks, and Opportunities},
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journal = {{IEEE} Journal of Selected Topics in Applied Earth Observations
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and Remote Sensing},
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volume = {13},
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pages = {3735--3756},
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year = {2020},
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doi = {10.1109/JSTARS.2020.3005403}
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}
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% -------- Random Forest --------
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@article{breiman2001,
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author = {Breiman, Leo},
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title = {Random Forests},
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journal = {Machine Learning},
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volume = {45},
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number = {1},
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pages = {5--32},
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year = {2001},
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doi = {10.1023/A:1010933404324}
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}
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% -------- Linguagem R --------
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@manual{r_core,
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title = {R: A Language and Environment for Statistical Computing},
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author = {{R Core Team}},
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organization = {R Foundation for Statistical Computing},
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address = {Vienna, Austria},
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year = {2024},
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url = {https://www.R-project.org/}
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}
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