предыдущий|следующий

Neural network generation of adaptive filter and new applications in remote sensing image processing

  • role： First author第一作者
  • Affiliation：

    Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China

  • Email：tangping@aircas.ac.cn
  • Introduction：唐娉，研究方向为遥感图像处理、遥感图像智能处理和大数据技术、非线性关系建模和表达。E-mail： tangping@aircas.ac.cn
TANG Ping1,  
  • Affiliation：

    Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China

    School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100049, China

LIU Xuan12,  
  • Affiliation：

    Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China

    School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100049, China

JIN Xing12,  
  • role： Corresponding author通信作者
  • Affiliation：

    Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China

  • Email：zhangzheng@aircas.ac.cn
  • Introduction：张正，研究方向为遥感数据挖掘、高性能遥感数据处理系统的理论和应用。E-mail： zhangzheng@aircas.ac.cn
ZHANG Zheng1*

реферат

Image adaptive filtering is a nonlinear image transformation, which has a wide range of applications. Traditional image adaptive filters are designed by experts, such as bilateral filter and shape adaptive filter. They can determine the shape, size, and weight of the filter based on the local structure and content of the image. They are commonly used to suppress noise while preserving the structural characteristics of the image. Convolutional Neural Networks (CNNs) are an effective tool for feature extraction and nonlinear expression. They can be used to learn and construct image adaptive filters. And this paper explores the application of nonlinear image adaptive filters generated by convolutional neural networks in image interpolation and image fusion.This paper introduces the generation network of an image adaptive filter, including its model structure and objective function. The common network structure usually employs an encoder-decoder architecture, which is mainly composed of three parts: feature extraction, feature recovery, and filter (convolution kernel) estimation. Then, the paper presents two different application scenarios of image adaptive filters: image interpolation and image fusion. The adaptive filter for images enables transformation between different phases during image interpolation and transformation between different bands during image fusion. In these two scenarios, the image adaptive filters are learned by the filter generation network based on the specific application scenario and then applied. In image interpolation applications, the image adaptive filter is used as a nonlinear transformation between two temporal images. The interpolated image is looked at as the mean of adaptive filtering of the previous temporal image and adaptive filtering of the latter temporal image. In image fusion applications, the image adaptive filter is used as a nonlinear fitting method to regress multispectral bands to the panchromatic band. It then extracts spatial details from the difference of the panchromatic band and the simulated panchromatic band, and finally adds spatial details to all the multispectral bands.We conducted experiments in two application scenarios. The first involved nonlinear transformation for image interpolation with different phases simultaneously. The second utilized an image adaptive filter as a nonlinear fitting method for multi-spectral band regression panchromatic band in image fusion. In image interpolation applications, the experimental results show that the interpolated results are consistent with the reference image in spatial and spectral characteristics, and the RMSE of the interpolated image with the reference image is relatively small. The experimental results for image fusion applications indicate that the low-resolution panchromatic band obtained through adaptive filter fitting of the multi-spectral band is more accurate than the traditional component replacement method. The fusion result obtained by nonlinear image adaptive filters has neither obvious spectral distortion nor obvious spatial distortion.From the application of nonlinear image adaptive filters generated by convolutional neural networks in image interpolation and image fusion, we have a glimpse of its application potential of image adaptive filter in constructing image nonlinear transformation. The filter generation network can generate adaptive filters for particular application scenarios, resulting in more accurate and visually pleasing images.

ключеви́че слова́

remote sensing;image adaptive filtering;filter generation network;dynamic filter network;image interpolation;image fusion

References

  1. 1.
    Aiazzi B, Alparone L, Baronti S, Garzelli A and Selva M. 2003. An MTF-based spectral distortion minimizing model for pan-sharpening of very high resolution multispectral images of urban areas//Proceedings of the 2003 2nd GRSS/ISPRS Joint Workshop on Remote Sensing and Data Fusion over Urban Areas. Berlin: IEEE: 90-94
  2. 2.
    Alvarez-Vanhard E, Houet T, Mony C, Lecoq L and Corpetti T. 2020. Can UAVs fill the gap between in situ surveys and satellites for habitat mapping?. Remote Sensing of Environment, 243: 111780
  3. 3.
    Chavez P S Jr and Kwarteng A Y. 1989. Extracting spectral contrast in Landsat Thematic Mapper image data using selective principal component analysis. Photogrammetric Engineering and Remote Sensing, 55(3): 339-348
  4. 4.
    Chavez P S, Sides S C and Anderson J A. 1991. Comparison of three different methods to merge multiresolution and multispectral data: LANDSAT TM and SPOT panchromatic. Photogrammetric Engineering and Remote Sensing, 57(3): 265-303
  5. 5.
    Dai J F, Qi H Z, Xiong Y W, Li Y, Zhang G D, Hu H and Wei Y C. 2017. Deformable convolutional networks. arXiv: 1703.06211
  6. 6.
    Dong C, Loy C C, He K M and Tang X O. 2016. Image super-resolution using deep convolutional networks. IEEE Transactions on Pattern Analysis and Machine Intelligence, 38(2): 295-307
  7. 7.
    Foi A and Katkovnik V. 2006. From local polynomial approximation to pointwise shape-adaptive transforms: an evolutionary nonparametric regression perspective// Proceedings of the 2006 International TICSP Workshop on Spectral Methods and Multirate Signal Processing, SMMSP2006, Florence, Italy. TICSP Series： 51-58
  8. 8.
    Garzelli A, Nencini F and Capobianco L. 2008. Optimal MMSE pan sharpening of very high resolution multispectral images. IEEE Transactions on Geoscience and Remote Sensing, 46(1): 228-236
  9. 9.
    Goodfellow I, Bengio Y and Courvillen A. 2016. Deep Learning. Cambridge: MIT Press
  10. 10.
    Jin X, Tang P, Houet T, Corpetti T, Alvarez-Vanhard E G and Zhang Z. 2021. Sequence image interpolation via separable convolution network. Remote Sensing, 13(2): 296
  11. 11.
    Kingma D P and Ba J. 2017. Adam: a method for stochastic optimization. arXiv: 1412.6980
  12. 12.
    Lee J and Lee C. 2010. Fast and efficient panchromatic sharpening. IEEE Transactions on Geoscience and Remote Sensing, 48(1): 155-163
  13. 13.
    Li C J, Liu L Y, Wang J H, Zhao C J and Wang R C. 2004. Comparison of two methods of the fusion of remote sensing images with fidelity of spectral information//2004 IEEE International Geoscience and Remote Sensing Symposium. Anchorage: IEEE: 2561-2564
  14. 14.
    Liu X, Tang P, Jin X and Zhang Z. 2022. From regression based on dynamic filter network to pansharpening by pixel-dependent spatial-detail injection. Remote Sensing, 14(5): 1242
  15. 15.
    Long J, Shelhamer E and Darrell T. 2015. Fully convolutional networks for semantic segmentation//Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition. Boston: IEEE: 3431-3440
  16. 16.
    Maurer T. 2013. How to pan-sharpen images using the Gram-Schmidt pan-sharpen method—A recipe. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XL-1/W1: 239-244
  17. 17.
    Meng X C, Xiong Y M, Shao F, Shen H F, Sun W W, Yang G, Yuan Q Q, Fu R D and Zhang H Y. 2021. A large-scale benchmark data set for evaluating pansharpening performance: overview and implementation. IEEE Geoscience and Remote Sensing Magazine, 9(1): 18-52
  18. 18.
    Su H, Jampani V, Sun D Q, Gallo O, Learned-Miller E and Kautz J. 2019. Pixel-adaptive convolutional neural networks//2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Long Beach: IEEE: 11158-11167
  19. 19.
    Tomasi C and Manduchi R. 1998. Bilateral filtering for gray and color images//Sixth International Conference on Computer Vision. Bombay: IEEE: 839-846
  20. 20.
    Tu T M, Su S C, Shyu H C and Huang P S. 2001. A new look at IHS-like image fusion methods. Information Fusion, 2(3): 177-186
  21. 21.
    Wang Z J, Ziou D, Armenakis C, Li D and Li Q Q. 2005. A comparative analysis of image fusion methods. IEEE Transactions on Geoscience and Remote Sensing, 43(6): 1391-1402
  22. 22.
    Wu J L, Li D, Yang Y, Bajaj C and Ji X Y. 2019. Dynamic sampling convolutional neural networks. arXiv: 1803.07624
  23. 23.
    Xue T F, Wu J J, Bouman K L and Freeman B T. 2016. Visual dynamics: probabilistic future frame synthesis via cross convolutional networks//Proceedings of the 30th International Conference on Neural Information Processing Systems. Barcelona: Curran Associates Inc.: 91-99
  24. 24.
    Yang J, Fu X, Hu Y, Huang Y, Ding X, Paisley J. 2017. PanNet: a deep network architecture for pan-sharpening//Proceedings of the 2017 IEEE International Conference on Computer Vision (ICCV). Venice: IEEE: 1753-1761

Читать полностью

The above content is generated by Large Model Translation. The translated content is for reference only. We do not assume any commercial or legal responsibilty for any consequences arising from the use of our website