LI Yanshan, FAN Leidong, XIE Weixin. TGSIFT: Robust SIFT Descriptor Based on Tensor Gradient for Hyperspectral Images[J]. Chinese Journal of Electronics, 2020, 29(5): 916-925. 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[J]. Chinese Journal of Electronics, 2020, 29(5): 916-925. 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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