@InProceedings{MirandaSiSaSaKöAl:2022:HiReDa,
               author = "Miranda, Mateus de Souza and Silva, Lucas Fernando Alvarenga e and 
                         Santos, Samuel Felipe dos and Santiago J{\'u}nior, Valdivino 
                         Alexandre de and K{\"o}rting, Thales Sehn and Almeida, Jurandy",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and 
                         {Universidade Federal de S{\~a}o Paulo (UNIFESP)} and 
                         {Universidade Federal de S{\~a}o Paulo (UNIFESP)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)} and {Instituto Nacional de 
                         Pesquisas Espaciais (INPE)} and {Universidade Federal de S{\~a}o 
                         Carlos (UFSCar)}",
                title = "A High-Spatial Resolution Dataset and Few-shot Deep Learning 
                         Benchmark for Image Classification",
            booktitle = "Proceedings...",
                 year = "2022",
         organization = "Conference on Graphics, Patterns and Images, 35. (SIBGRAPI)",
             keywords = "Dataset. Few-shot. Deep Learning. Cerrado. Remote Sensing.",
             abstract = "This paper presents a high-spatial-resolution dataset with remote 
                         sensing images of the Brazilian Cerrado for land use and land 
                         cover classification. The Biome Cerrado Dataset (Cerra- Data) is a 
                         large database created from 150 scenes of the CBERS- 4A satellite. 
                         Images were created by merging the near-infrared, green, and blue 
                         bands. Moreover, pan-sharpening was performed between all the 
                         scenes and their respective panchromatic bands, resulting in a 
                         final spatial resolution of two meters. A total of 2.5 million 
                         tiles of 256x256 pixels were derived from these scenes. From this 
                         total, 50 thousand tiles were labeled. We also conducted a 
                         few-shot learning experiment considering a training set with only 
                         100 samples, 11 deep neural networks (DNNs), and two traditional 
                         machine learning (ML) algorithms, i.e., support vector machine 
                         (SVM) and random forest (RF). Results show that the DNN 
                         DenseNet-161 was the best model but its performance can be 
                         improved if it is used only as a feature extractor, leaving the 
                         classification task for the traditional ML algorithms. However, by 
                         decreasing the size of the training set, smarter approaches are 
                         needed. The labeled subset of CerraData as well as the source code 
                         we developed to support this study are available on-line: 
                         https://github.com/ai4luc/CerraData-code-data.",
  conference-location = "Natal, RN",
      conference-year = "24-27 Oct. 2022",
                  doi = "10.1109/SIBGRAPI55357.2022.9991746",
                  url = "http://dx.doi.org/10.1109/SIBGRAPI55357.2022.9991746",
             language = "en",
                  ibi = "8JMKD3MGPEW34M/47JU8TS",
                  url = "http://urlib.net/ibi/8JMKD3MGPEW34M/47JU8TS",
           targetfile = "miranda_400826.pdf",
        urlaccessdate = "2024, May 02"
}