SEMANTIC SEGMENTATION of AERIAL IMAGERY VIA MULTI-SCALE SHUFFLING CONVOLUTIONAL NEURAL NETWORKS with DEEP SUPERVISION

K. Chen, M. Weinmann, X. Sun, M. Yan, S. Hinz, B. Jutzi, M. Weinmann

Research output: Contribution to journalConference articlepeer-review

16 Scopus citations

Abstract

In this paper, we address the semantic segmentation of aerial imagery based on the use of multi-modal data given in the form of true orthophotos and the corresponding Digital Surface Models (DSMs). We present the Deeply-supervised Shuffling Convolutional Neural Network (DSCNN) representing a multi-scale extension of the Shuffling Convolutional Neural Network (SCNN) with deep supervision. Thereby, we take the advantage of the SCNN involving the shuffling operator to effectively upsample feature maps and then fuse multiscale features derived from the intermediate layers of the SCNN, which results in the Multi-scale Shuffling Convolutional Neural Network (MSCNN). Based on the MSCNN, we derive the DSCNN by introducing additional losses into the intermediate layers of the MSCNN. In addition, we investigate the impact of using different sets of hand-crafted radiometric and geometric features derived from the true orthophotos and the DSMs on the semantic segmentation task. For performance evaluation, we use a commonly used benchmark dataset. The achieved results reveal that both multi-scale fusion and deep supervision contribute to an improvement in performance. Furthermore, the use of a diversity of hand-crafted radiometric and geometric features as input for the DSCNN does not provide the best numerical results, but smoother and improved detections for several objects.

Original languageEnglish
Pages (from-to)29-36
Number of pages8
JournalISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Volume4
Issue number1
DOIs
StatePublished - 23 Sep 2018
Externally publishedYes
Event2018 ISPRS Technical Commission I Midterm Symposium on Innovative Sensing - From Sensors to Methods and Applications - Karlsruhe, Germany
Duration: 10 Oct 201812 Oct 2018

Keywords

  • Aerial Imagery
  • CNN
  • Deep Supervision
  • Multi-Modal Data
  • Multi-Scale
  • Semantic Segmentation

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