Volume XLII-3
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3, 437-442, 2018
https://doi.org/10.5194/isprs-archives-XLII-3-437-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3, 437-442, 2018
https://doi.org/10.5194/isprs-archives-XLII-3-437-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

  30 Apr 2018

30 Apr 2018

FUSING PANCHROMATIC AND SWIR BANDS BASED ON CNN – A PRELIMINARY STUDY OVER WORLDVIEW-3 DATASETS

M. Guo1, H. Ma1, Y. Bao1, and L. Wang2 M. Guo et al.
  • 1School of Forestry, Southwest Forestry University, Kunming, China
  • 2Institutes of Big Data and Artificial Intelligence, Southwest Forestry University, Kunming, 650024, China

Keywords: Pan-sharpening, Convolutional Neural Network, Short-wave Infrared, Deep Learning, Image fusion, Remote Sensing

Abstract. The traditional fusion methods are based on the fact that the spectral ranges of the Panchromatic (PAN) and multispectral bands (MS) are almost overlapping. In this paper, we propose a new pan-sharpening method for the fusion of PAN and SWIR (short-wave infrared) bands, whose spectral coverages are not overlapping. This problem is addressed with a convolutional neural network (CNN), which is trained by WorldView-3 dataset. CNN can learn the complex relationship among bands, and thus alleviate spectral distortion. Consequently, in our network, we use the simple three-layer basic architecture with 16 × 16 kernels to conduct the experiment. Every layer use different receptive field. The first two layers compute 512 feature maps by using the 16 × 16 and 1 × 1 receptive field respectively and the third layer with a 8 × 8 receptive field. The fusion results are optimized by continuous training. As for assessment, four evaluation indexes including Entropy, CC, SAM and UIQI are selected built on subjective visual effect and quantitative evaluation. The preliminary experimental results demonstrate that the fusion algorithms can effectively enhance the spatial information. Unfortunately, the fusion image has spectral distortion, it cannot maintain the spectral information of the SWIR image.