ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-2, 65-72, 2018
https://doi.org/10.5194/isprs-annals-IV-2-65-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
 
28 May 2018
RESIDUAL SHUFFLING CONVOLUTIONAL NEURAL NETWORKS FOR DEEP SEMANTIC IMAGE SEGMENTATION USING MULTI-MODAL DATA
K. Chen1,2, M. Weinmann3, X. Gao1, M. Yan1, S. Hinz4, B. Jutzi4, and M. Weinmann4 1Key Laboratory of Spatial Information Processing and Application System Technology, Institute of Electronics, Chinese Academy of Sciences, Beijing, P.R. China
2University of Chinese Academy of Sciences, Beijing, P.R. China
3Institute of Computer Science II, University of Bonn, Bonn, Germany
4Institute of Photogrammetry and Remote Sensing, Karlsruhe Institute of Technology, Karlsruhe, Germany
Keywords: Semantic Segmentation, Aerial Imagery, Multi-Modal Data, Deep Learning, CNN, Residual Network Abstract. In this paper, we address the deep semantic segmentation of aerial imagery based on multi-modal data. Given multi-modal data composed of true orthophotos and the corresponding Digital Surface Models (DSMs), we extract a variety of hand-crafted radiometric and geometric features which are provided separately and in different combinations as input to a modern deep learning framework. The latter is represented by a Residual Shuffling Convolutional Neural Network (RSCNN) combining the characteristics of a Residual Network with the advantages of atrous convolution and a shuffling operator to achieve a dense semantic labeling. Via performance evaluation on a benchmark dataset, we analyze the value of different feature sets for the semantic segmentation task. The derived results reveal that the use of radiometric features yields better classification results than the use of geometric features for the considered dataset. Furthermore, the consideration of data on both modalities leads to an improvement of the classification results. However, the derived results also indicate that the use of all defined features is less favorable than the use of selected features. Consequently, data representations derived via feature extraction and feature selection techniques still provide a gain if used as the basis for deep semantic segmentation.
Conference paper (PDF, 3285 KB)

Citation: Chen, K., Weinmann, M., Gao, X., Yan, M., Hinz, S., Jutzi, B., and Weinmann, M.: RESIDUAL SHUFFLING CONVOLUTIONAL NEURAL NETWORKS FOR DEEP SEMANTIC IMAGE SEGMENTATION USING MULTI-MODAL DATA, ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-2, 65-72, https://doi.org/10.5194/isprs-annals-IV-2-65-2018, 2018.

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