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      Soil Texture Classification with 1D Convolutional Neural Networks based on Hyperspectral Data

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          Abstract

          Soil texture is important for many environmental processes. In this paper, we study the classification of soil texture based on hyperspectral data. We develop and implement three 1-dimensional (1D) convolutional neural networks (CNN): the LucasCNN, the LucasResNet which contains an identity block as residual network, and the LucasCoordConv with an additional coordinates layer. Furthermore, we modify two existing 1D CNN approaches for the presented classification task. The code of all five CNN approaches is available on GitHub (Riese, 2019). We evaluate the performance of the CNN approaches and compare them to a random forest classifier. Thereby, we rely on the freely available LUCAS topsoil dataset. The CNN approach with the least depth turns out to be the best performing classifier. The LucasCoordConv achieves the best performance regarding the average accuracy. In future work, we can further enhance the introduced LucasCNN, LucasResNet and LucasCoordConv and include additional variables of the rich LUCAS dataset.

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          Most cited references19

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          Classification of hyperspectral remote sensing images with support vector machines

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            Deep Learning in Remote Sensing: A Comprehensive Review and List of Resources

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              Deep Convolutional Neural Networks for Hyperspectral Image Classification

              Recently, convolutional neural networks have demonstrated excellent performance on various visual tasks, including the classification of common two-dimensional images. In this paper, deep convolutional neural networks are employed to classify hyperspectral images directly in spectral domain. More specifically, the architecture of the proposed classifier contains five layers with weights which are the input layer, the convolutional layer, the max pooling layer, the full connection layer, and the output layer. These five layers are implemented on each spectral signature to discriminate against others. Experimental results based on several hyperspectral image data sets demonstrate that the proposed method can achieve better classification performance than some traditional methods, such as support vector machines and the conventional deep learning-based methods.
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                Author and article information

                Journal
                15 January 2019
                Article
                1901.04846
                e0125097-04ad-4fee-b0df-ffc96c915471

                http://arxiv.org/licenses/nonexclusive-distrib/1.0/

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                Custom metadata
                Submitted to an ISPRS conference
                cs.CV cs.LG physics.geo-ph stat.ML

                Computer vision & Pattern recognition,Geophysics,Machine learning,Artificial intelligence

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