A mosaic method for multi-temporal data registration by using convolutional neural networks for forestry remote sensing applications
- 3 Downloads
Image registration is one of the most important processes for the generation of remote sensing image mosaics. This paper focuses on the special problems related to remote sensing data registration, and multi-temporal data mosaic applications in the domain of forestry. It proposes an image registration method based on hierarchical convolutional features, and applies it to improve the efficiency of large scale forestry image mosaic generation. This method uses a deep learning architecture to adaptively obtain image features from deep convolutional neural networks. The features derived from different images at different depth are sent to a correlation filter to compute the similarity between them; then the locations of the feature points are computed precisely. Based on this method, we study forestry image registration and the mosaic framework. We apply our approach to remote sensing images under different weather and seasonal conditions, and compare the results with those generated using the traditional SIFT image mosaic method. The experimental result shows that our method can detect and match the image feature points with significant spectral difference, and effectively extract feature points to generate accurate image registration and mosaic results. This demonstrates the effectiveness and robustness of the proposed approach.
KeywordsData mosaic Deep convolutional neural networks Image registration Hierarchical convolutional features Forestry remote sensing
This work was supported by National Natural Science Foundation of China (No. 61731022), the National Key Programme of Research and Development, Ministry of Science and Technology (No. 2017YFD0600900), and National Natural Science Foundation of China (No. 31872240). The authors would express their great appreciation to the contribution and support.
- 3.Li M, Liew SC, Kwoh LK (2003) Producing cloud free and cloud-shadow free mosaic from cloudy IKONOS images. In: Geoscience and remote sensing symposium, Toulouse, France, 21–25 July 2003Google Scholar
- 5.Qin X, Hong X, Wang H (2016) Improved SURF feature matching algorithm for remote sensing images. J Chin Mini Micro Comput Syst 37:327–331Google Scholar
- 6.Xu Z, Tian Q, Zhang L (2006) Discussion on image mosaic method. Microcomput Inf 30:255–256Google Scholar
- 7.Harris C, Stephens MA (1988) A combined corner and edge detector. In: Proceedings of the 4th Alvey vision conference, Manchester, UK, 31 Aug–2 Sept 1988Google Scholar
- 11.Zhao Y (2012) Automatic stitching algorithm for multiple images. Comput Eng Appl 48:152–157Google Scholar
- 12.Ma C, Huang J, Yang X (2015) Hierarchical convolutional features for visual tracking. In: IEEE international conference on computer vision, Chile, 13–16 Dec 2015Google Scholar
- 14.Boddeti V, Kanade T, Kumar B (2013) Correlation filters for object alignment. In: IEEE conference on computer vision and pattern recognition, Portland, Oregon, 25–27 June 2013Google Scholar
- 20.Cheng Y, Zhou Y, Lin HT (2008) Automatic registration and mosaic of remote sensing images based on SIFT features. Remote Sens Technol Appl 6:721–728Google Scholar
- 21.Simonyan K, Zisserman A (2015) Very deep convolutional networks for large-scale image recognition. In: International conference on learning representations, San Diego, the USA, 7–9 May 2015Google Scholar