@Article{MartinsKalGelNagMac:2020:DeNeNe,
               author = "Martins, Vitor S. and Kaleita, Amy L. and Gelder, Brian K. and 
                         Nagel, Gustavo Willy and Maciel, Daniel Andrade",
          affiliation = "{Iowa State University (ISU)} and {Iowa State University (ISU)} 
                         and {Iowa State University (ISU)} and {Instituto Nacional de 
                         Pesquisas Espaciais (INPE)} and {Instituto Nacional de Pesquisas 
                         Espaciais (INPE)}",
                title = "Deep neural network for complex open-water wetland mapping using 
                         high-resolution WorldView-3 and airborne LiDAR data",
              journal = "International Journal of Applied Earth Observation and 
                         Geoinformation",
                 year = "2020",
               volume = "93",
                pages = "e102215",
                month = "Dec.",
             keywords = "Deep learning, Small wetlands, Machine learning, Optical and LiDAR 
                         data, PCA.",
             abstract = "Wetland inventory maps are essential information for the 
                         conservation and management of natural wetland areas. The 
                         classification framework is crucial for successful mapping of 
                         complex wetlands, including the model selection, input variables 
                         and training procedures. In this context, deep neural network 
                         (DNN) is a powerful technique for remote sensing image 
                         classification, but this model application for wetland mapping has 
                         not been discussed in the previous literature, especially using 
                         commercial WorldView-3 data. This study developed a new framework 
                         for wetland mapping using DNN algorithm and WorldView-3 image in 
                         the Millrace Flats Wildlife Management Area, Iowa, USA. The study 
                         area has several wetlands with a variety of shapes and sizes, and 
                         the minimum mapping unit was defined as 20 m2 (0.002 ha). A set of 
                         potential variables was derived from WorldView-3 and auxiliary 
                         LiDAR data, and a feature selection procedure using principal 
                         components analysis (PCA) was used to identify the most important 
                         variables for wetland classification. Furthermore, traditional 
                         machine learning methods (support vector machine, random forest 
                         and k-nearest neighbor) were also implemented for the comparison 
                         of results. In general, the results show that DNN achieved 
                         satisfactory results in the study area (overall accuracy = 93.33 
                         %), and we observed a high spatial overlap between reference and 
                         classified wetland polygons (Jaccard index \∼0.8). Our 
                         results confirm that PCA-based feature selection was effective in 
                         the optimization of DNN performance, and vegetation and textural 
                         indices were the most informative variables. In addition, the 
                         comparison of results indicated that DNN classification achieved 
                         relatively similar accuracies to other methods. The total 
                         classification errors vary from 0.104 to 0.111 among the methods, 
                         and the overlapped areas between reference and classified polygons 
                         range between 87.93 and 93.33 %. Finally, the findings of this 
                         study have three main implications. First, the integration of DNN 
                         model and WorldView-3 image is useful for wetland mapping at 
                         1.2-m, but DNN results did not outperform other methods in this 
                         study area. Second, the feature selection was important for model 
                         performance, and the combination of most relevant input parameters 
                         contributes to the success of all tested models. Third, the 
                         spatial resolution of WorldView-3 is appropriate to preserve the 
                         shape and extent of small wetlands, while the application of 
                         medium resolution image (30-m) has a negative impact on the 
                         accurate delineation of these areas. Since commercial satellite 
                         data are becoming more affordable for remote sensing users, this 
                         study provides a framework that can be utilized to integrate very 
                         high-resolution imagery and deep learning in the classification of 
                         complex wetland areas.",
                  doi = "10.1016/j.jag.2020.102215",
                  url = "http://dx.doi.org/10.1016/j.jag.2020.102215",
                 issn = "0303-2434",
                label = "lattes: 9511166263268121 5 MartinsKalGelNagMac:2020:DeNeNe",
             language = "en",
           targetfile = "martins_deep.pdf",
        urlaccessdate = "09 maio 2024"
}