The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLIII-B2-2020
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B2-2020, 521–525, 2020
https://doi.org/10.5194/isprs-archives-XLIII-B2-2020-521-2020
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B2-2020, 521–525, 2020
https://doi.org/10.5194/isprs-archives-XLIII-B2-2020-521-2020

  12 Aug 2020

12 Aug 2020

DENSE MATCHING COMPARISON BETWEEN CLASSICAL AND DEEP LEARNING BASED ALGORITHMS FOR REMOTE SENSING DATA

Y. Xia, P. d’Angelo, J. Tian, and P. Reinartz Y. Xia et al.
  • German Aerospace Center (DLR), Remote Sensing Technology Institute, 82234 Wessling, Germany

Keywords: Dense Matching, CNN, GA-Net, SGM, Census, Disparity

Abstract. Deep learning and convolutional neural networks (CNN) have obtained a great success in image processing, by means of its powerful feature extraction ability to learn specific tasks. Many deep learning based algorithms have been developed for dense image matching, which is a hot topic in the community of computer vision. These methods are tested for close-range or street-view stereo data, however, not well studied with remote sensing datasets, including aerial and satellite data. As more high-quality datasets are collected by recent airborne and spaceborne sensors, it is necessary to compare the performance of these algorithms to classical dense matching algorithms on remote sensing data. In this paper, Guided Aggregation Net (GA-Net), which belongs to the most competitive algorithms on KITTI 2015 benchmark (street-view dataset), is tested and compared with Semi-Global Matching (SGM) on satellite and airborne data. GA-Net is an end-to-end neural network, which starts from an stereo pair and directly outputs a disparity map indicating the scene’s depth information. It is based on a differentiable approximation of SGM embedded into a neural network, performing well for ill-posed regions, such as textureless areas, slanted surfaces, etc. The results demonstrate that GA-Net is capable of producing a smoother disparity map with less errors, particularly for across track data acquired at different dates.