Change detection for high-resolution satellite images using transfer learning and deep learning network

Ah Ram Song, Jae Wan Choi, Yong Kim

Research output: Contribution to journalArticlepeer-review

5 Scopus citations

Abstract

As the number of available satellites increases and technology advances, image information outputs are becoming increasingly diverse and a large amount of data is accumulating. In this study, we propose a change detection method for high-resolution satellite images that uses transfer learning and a deep learning network to overcome the limit caused by insufficient training data via the use of pre-trained information. The deep learning network used in this study comprises convolutional layers to extract the spatial and spectral information and convolutional long-short term memory layers to analyze the time series information. To use the learned information, the two initial convolutional layers of the change detection network are designed to use learned values from 40,000 patches of the ISPRS (International Society for Photogrammertry and Remote Sensing) dataset as initial values. In addition, 2D (2-Dimensional) and 3D (3-dimensional) kernels were used to find the optimized structure for the high-resolution satellite images. The experimental results for the KOMPSAT-3A (KOrean Multi-Purpose SATllite-3A) satellite images show that this change detection method can effectively extract changed/unchanged pixels but is less sensitive to changes due to shadow and relief displacements. In addition, the change detection accuracy of two sites was improved by using 3D kernels. This is because a 3D kernel can consider not only the spatial information but also the spectral information. This study indicates that we can effectively detect changes in high-resolution satellite images using the constructed image information and deep learning network. In future work, a pre-trained change detection network will be applied to newly obtained images to extend the scope of the application.

Original languageEnglish
Pages (from-to)199-208
Number of pages10
JournalJournal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
Volume37
Issue number3
DOIs
StatePublished - 2019

Keywords

  • Change detection
  • Convolutional long short term memory layer
  • Deep learning
  • Fully convolutional layer
  • High-resolution satellite images
  • Transfer learning

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