2019
DOI: 10.3390/rs11050597
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Fully Convolutional Networks and Geographic Object-Based Image Analysis for the Classification of VHR Imagery

Abstract: Land cover Classified maps obtained from deep learning methods such as Convolutional neural networks (CNNs) and fully convolutional networks (FCNs) usually have high classification accuracy but with the detailed structures of objects lost or smoothed. In this work, we develop a methodology based on fully convolutional networks (FCN) that is trained in an end-to-end fashion using aerial RGB images only as input. Skip connections are introduced into the FCN architecture to recover high spatial details from the l… Show more

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Cited by 53 publications
(45 citation statements)
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“…They also employed ancillary data and image texture rasters. In a recent work, Mboga et al [46] applied a fully convolutional neural network to OBIA-derived segmentation to produce landcover maps in an urban setting.…”
Section: Segmentation and Classificationmentioning
confidence: 99%
“…They also employed ancillary data and image texture rasters. In a recent work, Mboga et al [46] applied a fully convolutional neural network to OBIA-derived segmentation to produce landcover maps in an urban setting.…”
Section: Segmentation and Classificationmentioning
confidence: 99%
“…For example, conditional random field (CRF) models are used as a postprocessing step to improve the classification by imposing a smoothness prior in low-level vision, so that neighboring pixels are more likely to be allocated the same class label [36]. An object-based classification method, which is adequate for describing boundaries of geo-objects by image segmentation, is combined with abstracted deep features to increase the classification accuracy [31,37]. In addition, a variety of methods are used in an attempt to add additional data to the FCN, such as digital surface models (DSM), vegetation indices (VI), and edge information [38][39][40][41].…”
Section: Fusion Of Multilevel Deep Featuresmentioning
confidence: 99%
“…Recently, deep neural networks (DNNs) are utilized for various kind of problems, which have shown immense performance in different applications, including image recognition [16,40,41]. Deep networks have changed the trend by replacing hand-engineered features to the learning strategy.…”
Section: B Deep Featuresmentioning
confidence: 99%