LI Yanshan, FAN Leidong, XIE Weixin, “TGSIFT: Robust SIFT Descriptor Based on Tensor Gradient for Hyperspectral Images,” Chinese Journal of Electronics, vol. 29, no. 5, pp. 916-925, 2020, doi: 10.1049/cje.2020.08.007
Citation: LI Yanshan, FAN Leidong, XIE Weixin, “TGSIFT: Robust SIFT Descriptor Based on Tensor Gradient for Hyperspectral Images,” Chinese Journal of Electronics, vol. 29, no. 5, pp. 916-925, 2020, doi: 10.1049/cje.2020.08.007

TGSIFT: Robust SIFT Descriptor Based on Tensor Gradient for Hyperspectral Images

doi: 10.1049/cje.2020.08.007
Funds:  This work is supported in part by the National Natural Science Foundation of China (No.61771319, No.61871154, No.61401286), Natural Science Foundation of Guangdong Province (No.2017A030313343, No.2019A1515011307), Shenzhen Science and Technology Project (No.JCYJ20180507182259896), and the other project (No.WDZC20195500201).
  • Received Date: 2017-11-03
  • Rev Recd Date: 2018-04-23
  • Publish Date: 2020-09-10
  • This paper proposes a robust sparse descriptor based on tensor theory by using the spatial and spectral information synthetically, namely the Tensor gradient SIFT (TGSIFT), for Hyperspectral image (HSI). TGSIFT integrates both spatial and spectral information and considers the natural vector feature of HSIs. Based on the HSI Gaussian scale space, a new tensor model for HSI is proposed which takes the vectorial nature of HSI into consideration and preserves all the necessary structural information distributed over all the bands. The TGSIFT descriptor is formed based on the model proposed. Experimental results of HSI matching show that the TGSIFT descriptor achieves better matching performance than other SIFT descriptors under different transformations, including illumination change, sensor noise, image rotation, viewpoint change, and scale change.
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  • A. Datta, S. Ghosh and A. Ghosh, "Unsupervised band extraction for hyperspectral images using clustering and kernel principal component analysis", International Journal of Remote Sensing, Vol.38, No.3, pp.850-873, 2017.
    P. Lasch and I. Noda, "Two-dimensional correlation spectroscopy for multimodal analysis of FT-IR, Raman, and MALDI-TOF MS hyperspectral images with hamster brain tissue", Analytical Chemistry, Vol.89, No.9, pp.5008-5016, 2017.
    K. G. Taskin, H. Kaya and L. Bruzzone, "Feature selection based on high dimensional model representation for hyperspectral images", IEEE Transactions on Image Processing, Vol.26, No.6, pp.2918-2928, 2017.
    B. Du, M. Zhang, L. Zhang, et al., "PLTD:Patch-based lowrank tensor decomposition for hyperspectral images", IEEE Transactions on Multimedia, Vol.19, No.1, pp.67-79, 2017.
    W. Di, L. Zhang, D. Zhang, et al., "Studies on hyperspectral face recognition in visible spectrum with feature band selection", IEEE Transactions on Systems, Man, and Cybernetics-Part A:Systems and Humans, Vol.40, No.6, pp.1354-1361, 2010.
    A. Mukherjee, M. Velez-Reyes and B. Roysam, "Interest points for hyperspectral image data", IEEE Transactions on Geoscience and Remote Sensing, Vol.47, No.3, pp.748-760, 2009.
    P. Khuwuthyakorn, A. Robles-Kelly and J. Zhou, "Affine invariant hyperspectral image descriptors based upon harmonic analysis", Machine Vision Beyond Visible Spectrum, Springer, Berlin, Heidelberg, pp.179-199, 2011.
    D. G. Lowe, "Object recognition from local scale-invariant features", The Proceedings of the Seventh IEEE International Conference on Computer Vision, Kerkyra, Greece, pp.2002-1150, 1999.
    D. G. Lowe, "Distinctive image features from scale-invariant keypoints", International Journal of Computer Vision, Vol.60, No.2, pp.91-110, 2004.
    K. Mikolajczyk and C. Schmid, "A performance evaluation of local descriptors", IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol.27, No.10, pp.1615-1630, 2005.
    S. Belongie, J. Malik and J. Puzicha, "Shape matching and object recognition using shape contexts", IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol.24, No.4, pp.509-522, 2002.
    L. P. Dorado-Munoz, M. Velez-Reyes, A. Mukherjee, et al., "A vector SIFT detector for interest point detection in hyperspectral imagery", IEEE Transactions on Geoscience and Remote Sensing, Vol.50, No.11, pp.4521-4533.
    Y. Li, W. Liu, X. Li, et al., "GA-SIFT:A new scale invariant feature transform for multispectral image using geometric algebra", Information Sciences, Vol.281, No.10, pp.559-572, 2014.
    Y. Xu, K. Hu, Y. Tian, et al., "Classification of hyperspectral imagery using SIFT for spectral matching", Congress on Image and Signal Processing, Sanya, Hainan, China, pp.704-708, 2008.
    J. Tan, J. Zhang and Y. Zhang, "Target detection for polarized hyperspectral images based on tensor decomposition", IEEE Geoscience and Remote Sensing Letters, Vol.14, No.5, pp.674-678, 2017.
    S. Di Zenzo, "A note on the gradient of a multi-image", Computer Vision, Graphics, and Image Processing, Vol.33, No.1, pp.116-125, 1986.
    L. Zhang, L. Zhang, D. Tao, et al., "Tensor discriminative locality alignment for hyperspectral image spectral-spatial feature extraction", IEEE Transactions on Geoscience & Remote Sensing, Vol.51, No.1, pp.242-256, 2013.
    L. Zhang, L. Zhang, D. Tao, et al. "A multifeature tensor for remote-sensing target recognition", Geoscience & Remote Sensing Letters, Vol.8, No.2, pp.374-378, 2011.
    L. Zhang, L. Zhang, D. Tao, et al., "Compression of hyperspectral remote sensing images by tensor approach", Neurocomputing, Vol.147, No.1, pp.358-363, 2015.
    N. Renard and S. Bourennane, "Improvement of target detection methods by multiway filtering", IEEE Transactions on Geoscience and Remote Sensing, Vol.46, No.8, pp.2407-2417, 2008.
    J. Chen, S. Shan, C. He, et al., "WLD:A robust local image descriptor", IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol.32, No.9, pp.1705-1720, 2010.
    L. Shen and S. Zheng, "Hyperspectral face recognition using 3D Gabor wavelets", 21st International Conference on Pattern Recognition (ICPR), Tsukuba, Japan, pp.1574-1577, 2013.
    M. Uzair, A. Mahmood and A. S. Mian, "Hyperspectral face recognition using 3D-DCT and partial least squares", British Machine Vision Conference (BMVC), Bristol, UK, pp.1-10, 2013.
    J. Liang, J. Zhou and Y. Gao, "3D local derivative pattern for hyperspectral face recognition", 11th International Conference and Workshops on Automatic Face and Gesture Recognition (FG), Ljubljana, Slovenia, pp.1-6, 2015.
    P. Scovanner, S. Ali and M. Shah, "A 3-dimensional sift descriptor and its application to action recognition", 2007 Proceedings of the 15th international conference on Multimedia, Augsburg, Germany, pp.357-360, 2007.
    Y. Li and W. Shi, "A new framework of hyperspectral image classification based on spatial spectral interest point", IEEE International Conference on Signal Processing, Beijing, China, pp.733-736, 2017.
    Y. Li, Q. Huang, W. Xie, et al. "A novel visual codebook model based on fuzzy geometry for large-scale image classification", Pattern Recognition, Vol.48, No.10, pp.3125-3134, 2015.
    M. Al Ghamdi, L. Zhang and Y. Gotoh, "Spatio-temporal SIFT and its application to human action classification", European Conference on Computer Vision, Florence, Italy, pp.301-310, 2012.
    D. Ni, Y. P. Chui, Y. Qu, et al., "Reconstruction of volumetric ultrasound panorama based on improved 3D SIFT", Computerized Medical Imaging and Graphics, Vol.33, No.7, pp.559-566, 2009.
    S. Edelman, N. Intrator and T. Poggio, "Complex cells and object recognition", NIPS Conference, Denver, Colorado, USA, pp.423-429, 1997.
    M. Heikkilä, M. Pietikäinen and C. Schmid, "Description of interest regions with local binary patterns", Pattern Recognition, Vol.42, No.3, pp.425-436, 2009.
    K. Yan and R. Sukthankar, "PCA-SIFT:A more distinctive representation for local image descriptors", CVPR, Vol.2, No.2, pp.506-513, 2004.
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