Geoinformatics Unit

Yong CHEN

  

Current Position

Yong Chen is an intern of Geoinformatics Unit at RIKEN Center for Advanced Intelligence Project (AIP), Japan.
His research is focused on remote sensing image processing and sparse optimization modeling.

Biography

2018 Oct - Present    Intern, RIKEN AIP, Japan
2015 Sep - Present    Ph.D. student at the School of Mathematical Sciences, University of Electronic Science and Technology of China, China (Successive postgraduate and doctoral program)

Journal Papers

  1. Y. Chen, L. Huang, L. Zhu, N. Yokoya, and X. Jia, " Fine-grained classification of hyperspectral imagery based on deep learning ," Remote Sensing (accepted for publication), 2019.
  2. Y. Chen, W. He, N. Yokoya, and T.-Z. Huang, " Non-local tensor ring decomposition for hyperspectral image denoising ," IEEE Trans. Geosci. Remote Sens. (accepted for publication), 2019.
    Quick Abstract

    Abstract: Hyperspectral image (HSI) denoising is a fundamental problem in remote sensing and image processing. Recently, non-local low-rank tensor approximation based denoising methods have attracted much attention, due to the advantage of fully exploiting the non-local self-similarity and global spectral correlation. Existing non-local low-rank tensor approximation methods were mainly based on two common Tucker or CP decomposition and achieved the state-of-the-art results, but they suffer some troubles and are not the best approximation for a tensor. For example, the number of parameters of Tucker decomposition increases exponentially follow its dimension, and CP decomposition cannot better preserve the intrinsic correlation of HSI. In this paper, we propose a non-local tensor ring (TR) approximation for HSI denoising by utilizing TR decomposition to simultaneously explore non-local self-similarity and global spectral low-rank characteristic. TR decomposition approximates a high-order tensor as a sequence of cyclically contracted three-order tensors, which has a strong ability to explore these two intrinsic priors and improve the HSI denoising result. Moreover, we develop an efficient proximal alternating minimization algorithm to efficiently optimize the proposed TR decomposition model. Extensive experiments on three simulated datasets under several noise levels and two real datasets testify that the proposed TR model performs better HSI denoising results than several state-of-the-art methods in term of quantitative and visual performance evaluations.

  3. Y. Chen, W. He, N. Yokoya, and T.-Z. Huang, " Blind cloud and cloud shadow removal of multitemporal images based on total variation regularized low-rank sparsity decomposition ," ISPRS Journal of Photogrammetry and Remote Sensing (accepted for publication), 2019.
    Quick Abstract

    Abstract: Cloud and cloud shadow (cloud/shadow) removal from multitemporal satellite images is a challenging task and has elicited much attention for subsequent information extraction. Regarding cloud/shadow areas as missing information, low-rank matrix/tensor completion based methods are popular to recover information undergoing cloud/shadow degradation. However, existing methods required to determine the cloud/shadow locations in advance and failed to completely use the latent information in cloud/shadow areas. In this study, we propose a blind cloud/shadow removal method for time-series remote sensing images by unifying cloud/shadow detection and removal together. First, we decompose the degraded image into low-rank clean image (surface-reflected) component and sparse (cloud/shadow) component, which can simultaneously and completely use the underlying characteristics of these two components. Meanwhile, the spatial-spectral total variation regularization is introduced to promote the spatial-spectral continuity of the cloud/shadow component. Second, the cloud/shadow locations are detected from the sparse component using a threshold method. Finally, we adopt the cloud/shadow detection results to guide the information compensation from the original observed images to better preserve the information in cloud/shadow-free locations. The problem of the proposed model is efficiently addressed using the alternating direction method of multipliers. Both simulated and real datasets are performed to demonstrate the effectiveness of our method for cloud/shadow detection and removal when compared with other state-of-the-art methods.

  4. Y. Chen, W. He, N. Yokoya, and T.-Z. Huang, " Hyperspectral image restoration using weighted group sparsity regularized low-rank tensor decomposition ," IEEE Transactions on Cybernetics (accepted for publication), 2019.
    Quick Abstract

    Abstract: Mixed noise (such as Gaussian, impulse, stripe, and deadline noises) contamination is a common phenomenon in hyperspectral imagery (HSI), greatly degrading visual quality and affecting subsequent processing accuracy. By encoding sparse prior to the spatial or spectral difference images, total variation (TV) regularization is an efficient tool for removing the noises. However, the previous TV term cannot maintain the shared group sparsity pattern of the spatial difference images of different spectral bands. To address this issue, this study proposes a group sparsity regularization of the spatial difference images for HSI restoration. Instead of using L1 or L2-norm (sparsity) on the difference image itself, we introduce a weighted L2,1-norm to constrain the spatial difference image cube, efficiently exploring the shared group sparse pattern. Moreover, we employ the well-known low-rank Tucker decomposition to capture the global spatial-spectral correlation from three HSI dimensions. To summarize, a weighted group sparsity regularized low-rank tensor decomposition (LRTDGS) method is presented for HSI restoration. An efficient augmented Lagrange multiplier algorithm is employed to solve the LRTDGS model. The superiority of this method for HSI restoration is demonstrated by a series of experimental results from both simulated and real data, as compared to other state-of-the-art TV regularized low-rank matrix/tensor decomposition methods.