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@InProceedings{NogueiraMiraSant:2015:ImSpFe,
               author = "Nogueira, Keiller and Miranda, Waner O. and Santos, Jefersson A. 
                         dos",
          affiliation = "{Universidade Federal de Minas Gerais} and {Universidade Federal 
                         de Minas Gerais} and {Universidade Federal de Minas Gerais}",
                title = "Improving Spatial Feature Representation from Aerial Scenes by 
                         Using Convolutional Networks",
            booktitle = "Proceedings...",
                 year = "2015",
               editor = "Papa, Jo{\~a}o Paulo and Sander, Pedro Vieira and Marroquim, 
                         Ricardo Guerra and Farrell, Ryan",
         organization = "Conference on Graphics, Patterns and Images, 28. (SIBGRAPI)",
            publisher = "IEEE Computer Society",
              address = "Los Alamitos",
             keywords = "Deep Learning, Remote Sensing, Feature Learning, Image 
                         Classification, Machine Learning, High-resolution Images.",
             abstract = "The performance of image classification is highly dependent on the 
                         quality of extracted features. Concerning high resolution remote 
                         image images, encoding the spatial features in an efficient and 
                         robust fashion is the key to generating discriminatory models to 
                         classify them. Even though many visual descriptors have been 
                         proposed or successfully used to encode spatial features of remote 
                         sensing images, some applications, using this sort of images, 
                         demand more specific description techniques. Deep Learning, an 
                         emergent machine learning approach based on neural networks, is 
                         capable of learning specific features and classifiers at the same 
                         time and adjust at each step, in real time, to better fit the need 
                         of each problem. For several task, such image classification, it 
                         has achieved very good results, mainly boosted by the feature 
                         learning performed which allows the method to extract specific and 
                         adaptable visual features depending on the data. In this paper, we 
                         propose a novel network capable of learning specific spatial 
                         features from remote sensing images, with any pre-processing step 
                         or descriptor evaluation, and classify them. Specifically, 
                         automatic feature learning task aims at discovering hierarchical 
                         structures from the raw data, leading to a more representative 
                         information. This task not only poses interesting challenges for 
                         existing vision and recognition algorithms, but also brings huge 
                         opportunities for urban planning, crop and forest management and 
                         climate modelling. The propose convolutional neural network has 
                         six layers: three convolutional, two fully-connected and one 
                         classifier layer. So, the five first layers are responsible to 
                         extract visual features while the last one is responsible to 
                         classify the images. We conducted a systematic evaluation of the 
                         proposed method using two datasets: (i) the popular aerial image 
                         dataset UCMerced Land-use and, (ii) a multispectral 
                         high-resolution scenes of the Brazilian Coffee Scenes. The 
                         experiments show that the proposed method outperforms 
                         state-of-the-art algorithms in terms of overall accuracy.",
  conference-location = "Salvador",
      conference-year = "Aug. 26-29, 2015",
             language = "en",
           targetfile = "sibgrapi2015.pdf",
        urlaccessdate = "2021, Dec. 01"
}


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