@InProceedings{MarettoFonsKört:2017:DeLeTe,
               author = "Maretto, Raian Vargas and Fonseca, Leila Maria Garcia and 
                         K{\"o}rting, Thales Sehn",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)} and {Instituto Nacional de 
                         Pesquisas Espaciais (INPE)}",
                title = "Deep Learning Techniques Applied to classification of Remote 
                         Sensing Images",
            booktitle = "Anais...",
                 year = "2017",
         organization = "Workshop dos Cursos de Computa{\c{c}}{\~a}o Aplicada do INPE, 
                         17. (WORCAP)",
             keywords = "Deep Learning, Remote Sensing, Machine Learning, Image 
                         classification.",
             abstract = "Remote Sensing (RS) techniques have become increasingly important 
                         in data-collection tasks and location-based services. Recent 
                         increased accessibility of new generation multispectral sensors 
                         has improved the complexity required in the analysis techniques. 
                         Produce efficient representations and understandings of the scenes 
                         has become a challenging problem. To improve knowledge 
                         representation and feature description, huge number of algorithms 
                         have been developed considering not only the local pixel 
                         information, but contextual information obtained from homogeneous 
                         regions in images (K{\"O}RTING; GARCIA FONSECA; C{\^A}MARA, 
                         2013; WA{\c{c}}LTER, 2004). However, most approaches lack on 
                         learning efficient representations of the images, extracting only 
                         shallow features that cannot easily represent the details of 
                         complex real data (LECUN; BENGIO; HINTON, 2015; ZHANG; ZHANG; 
                         KUMAR, 2016). Deep Learning (DL) techniques, which can learn 
                         representative and discriminative features from data, has become a 
                         hotspot in the Machine Learning community. They are composed of 
                         multiple levels of feature extraction layers. Each level 
                         transforms the representation of the previous level into a higher, 
                         slightly more abstract model, mapping different levels of 
                         abstractions and combining them to model and explore intrinsic 
                         correlations of the data (Lecun et al., 2015). DL algorithms have 
                         recently started to be used by the RS community, being 
                         successfully used in several tasks, from pre-processing to 
                         classification. Despite the great potential of these techniques, 
                         many questions are still unknown for its use in RS applications. 
                         The large number of bands and the way to consider the spectral 
                         curves represent a great challenge. Only few labeled samples are 
                         available, leading to difficulties to train the network. Images 
                         acquired from different sensors or in different seasons have large 
                         differences among them, leading to problems to transfer the 
                         network knowledge between different images (ZHANG; ZHANG; KUMAR, 
                         2016). The main goal of this work is to investigate the use of 
                         Deep Learning based approaches for classification of remote 
                         sensing images. We believe that designing an architecture to a 
                         Deep Neural Network considering the particularities and 
                         complexities of RS images, we can achieve good results for 
                         classification. With this approach, we expect to answer some 
                         opened questions about the use of DL in RS image analysis, filling 
                         in some gaps in the image analysis. Therefore, the main question 
                         we aim to answer is What is the best architecture to a Deep Neural 
                         Network to classify high resolution remote sensing Images?. A case 
                         study was developed in the classification of Land Cover in 
                         Brazilian Amazon, with main focus on the deforestation. To train 
                         the network and evaluate the results, PRODES deforestation data 
                         was used. It is important to emphasize that although this study is 
                         in a preliminary stage, the results are promising and reached 
                         improvements in the accuracy of the classification.",
  conference-location = "S{\~a}o Jos{\'e} dos Campos, SP",
      conference-year = "20-22 nov. 2017",
             language = "en",
           targetfile = "Maretto_deep.pdf",
        urlaccessdate = "11 maio 2024"
}