The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XL-7/W3
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-7/W3, 1119–1122, 2015
https://doi.org/10.5194/isprsarchives-XL-7-W3-1119-2015
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-7/W3, 1119–1122, 2015
https://doi.org/10.5194/isprsarchives-XL-7-W3-1119-2015

  30 Apr 2015

30 Apr 2015

SEMANTIC SEGMENTATION AND DIFFERENCE EXTRACTION VIA TIME SERIES AERIAL VIDEO CAMERA AND ITS APPLICATION

S. N. K. Amit1, S. Saito1, S. Sasaki2,3, Y. Kiyoki2, and Y. Aoki1 S. N. K. Amit et al.
  • 1Keio University, Graduate School of Science and Technology, 3-14-1 Hiyoshi, Kohoku-ku, Yokohama, Kanagawa, 223-8522 Japan
  • 2Keio University, Graduate School of Media and Governance, 5322 Endo, Fujisawa-shi, Kanagawa, 252-0882 Japan
  • 3Chulalongkorn University, 254 Phayathai Road, Pathumwan, Bangkok 10330, Thailand

Keywords: Difference extraction, semantic segmentation, aerial images, convolution neural networks

Abstract. Google earth with high-resolution imagery basically takes months to process new images before online updates. It is a time consuming and slow process especially for post-disaster application. The objective of this research is to develop a fast and effective method of updating maps by detecting local differences occurred over different time series; where only region with differences will be updated. In our system, aerial images from Massachusetts’s road and building open datasets, Saitama district datasets are used as input images. Semantic segmentation is then applied to input images. Semantic segmentation is a pixel-wise classification of images by implementing deep neural network technique. Deep neural network technique is implemented due to being not only efficient in learning highly discriminative image features such as road, buildings etc., but also partially robust to incomplete and poorly registered target maps. Then, aerial images which contain semantic information are stored as database in 5D world map is set as ground truth images. This system is developed to visualise multimedia data in 5 dimensions; 3 dimensions as spatial dimensions, 1 dimension as temporal dimension, and 1 dimension as degenerated dimensions of semantic and colour combination dimension. Next, ground truth images chosen from database in 5D world map and a new aerial image with same spatial information but different time series are compared via difference extraction method. The map will only update where local changes had occurred. Hence, map updating will be cheaper, faster and more effective especially post-disaster application, by leaving unchanged region and only update changed region.