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Kun Li - IEEE Xplore Author Profile
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Kun Li

Publications
8
Citations
121
Publications by Year
20192025
Co-Authors:
Show All Co-Authors (26)
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Kun Li

Affiliation

College of Big Data and Information Engineering
Guizhou University
Guiyang, China

Publication Topics

Biography

Kun Li received the B.S. degree in communication engineering from the National University of Defense Technology, Changsha, China, where he is currently pursuing the M.S. degree in electronic science and technology.,His current research interests include memristor characterization and mechanism.(Based on document published on 20 December 2019).
Publications
8
Citations
121
Publications by Year
20192025
Co-Authors:
Show All Co-Authors (26)
Author's Published Works

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Nonuniformity and Bad Pixel Correction Based on 3-D Network With Oversmoothing Suppression

Teliang Wang;Wei An;Zaiping Lin;Miao Li;Kun Li;Qian Yin;Wei Zhou

IEEE Transactions on Geoscience and Remote Sensing
Year: 2025 | Volume: 63 | Journal Article |
The purpose of nonuniformity and bad pixel correction is to provide a more reliable foundation for subsequent image processing and target detection. Existing correction methods generally struggle to balance the contradiction between oversmoothing and residual noise. Particularly, oversmoothing can easily filter out texture details and dim small targets. Based on the multiframe response model of th...Show More

Nonuniformity and Bad Pixel Correction Based on 3-D Network With Oversmoothing Suppression

Teliang Wang;Wei An;Zaiping Lin;Miao Li;Kun Li;Qian Yin;Wei Zhou

Year: 2025 | Volume: 63 | Journal Article |

Explicit Background Endmember Learning for Hyperspectral Anomaly Detection

Kun Li;Wei An;Yingqian Wang;Tingting Zhang;Yao Qin;Tinghong Gao

IEEE Transactions on Instrumentation and Measurement
Year: 2024 | Volume: 73 | Journal Article |
Cited by: Papers (2)
Hyperspectral anomaly detection (HAD) is challenging especially when anomalies appear in the form of subpixels. Since the spectral signatures of anomalies in mixed pixels are mixed with those of other materials in the background, the anomalies are more difficult to be separated from the background. However, most existing deep learning-based methods neglect the negative effect of mixed pixels on HA...Show More

Explicit Background Endmember Learning for Hyperspectral Anomaly Detection

Kun Li;Wei An;Yingqian Wang;Tingting Zhang;Yao Qin;Tinghong Gao

Year: 2024 | Volume: 73 | Journal Article |

Spectral Difference Guided Graph Attention Autoencoder for Hyperspectral Anomaly Detection

Kun Li;Qiang Ling;Yingqian Wang;Yaoming Cai;Yao Qin;Zaiping Lin;Wei An

IEEE Transactions on Instrumentation and Measurement
Year: 2023 | Volume: 72 | Journal Article |
Cited by: Papers (17)
Hyperspectral anomaly detection (HAD) aims at distinguishing anomalies from background in an unsupervised manner. Autoencoder (AE) and its variant-based methods have achieved promising detection performance in HAD. However, most existing methods neglect to exploit the local structure information of hyperspectral images (HSIs) that reflects the underlying relationships between each pixel and its su...Show More

Spectral Difference Guided Graph Attention Autoencoder for Hyperspectral Anomaly Detection

Kun Li;Qiang Ling;Yingqian Wang;Yaoming Cai;Yao Qin;Zaiping Lin;Wei An

Year: 2023 | Volume: 72 | Journal Article |

Spectral-Spatial Deep Support Vector Data Description for Hyperspectral Anomaly Detection

Kun Li;Qiang Ling;Yao Qin;Yingqian Wang;Yaoming Cai;Zaiping Lin;Wei An

IEEE Transactions on Geoscience and Remote Sensing
Year: 2022 | Volume: 60 | Journal Article |
Cited by: Papers (10)
Hyperspectral anomaly detection (HAD) aims to distinguish anomalies from background-by-background modeling. Deep learning has been applied to HAD and achieves promising detection results. However, there exist several issues that need to be addressed: 1) unrealistic Gaussian assumption on the latent representations may limit its application; 2) deep features are not well-suited to anomaly detection...Show More

Spectral-Spatial Deep Support Vector Data Description for Hyperspectral Anomaly Detection

Kun Li;Qiang Ling;Yao Qin;Yingqian Wang;Yaoming Cai;Zaiping Lin;Wei An

Year: 2022 | Volume: 60 | Journal Article |

Hyperspectral Detection and Unmixing of Subpixel Target Using Iterative Constrained Sparse Representation

Qiang Ling;Kun Li;Zhaoxu Li;Zaiping Lin;Jiawen Wang

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Year: 2022 | Volume: 15 | Journal Article |
Cited by: Papers (9)
With great significance in military and civilian applications, subpixel target detection is of great interest in hyperspectral remote sensing. The subpixel targets usually also need to be unmixed to identify their components. Traditionally, these subpixel targets are first detected and then unmixed to obtain their corresponding abundances. Therefore, target detection and target unmixing are indepe...Show More

Hyperspectral Detection and Unmixing of Subpixel Target Using Iterative Constrained Sparse Representation

Qiang Ling;Kun Li;Zhaoxu Li;Zaiping Lin;Jiawen Wang

Year: 2022 | Volume: 15 | Journal Article |

SRGAT: Single Image Super-Resolution With Graph Attention Network

Yanyang Yan;Wenqi Ren;Xiaobin Hu;Kun Li;Haifeng Shen;Xiaochun Cao

IEEE Transactions on Image Processing
Year: 2021 | Volume: 30 | Journal Article |
Cited by: Papers (43)
Deep neural networks have demonstrated remarkable reconstruction for single-image super-resolution (SISR). However, most existing CNN-based SISR methods directly learn the relation between low-resolution (LR) and high-resolution (HR) images, neglecting to explore the recurrence of internal patches, hence hindering the representational power of CNNs. In this paper, we propose a novel single image S...Show More

SRGAT: Single Image Super-Resolution With Graph Attention Network

Yanyang Yan;Wenqi Ren;Xiaobin Hu;Kun Li;Haifeng Shen;Xiaochun Cao

Year: 2021 | Volume: 30 | Journal Article |

Self-Supervised Deep Subspace Clustering for Hyperspectral Images With Adaptive Self-Expressive Coefficient Matrix Initialization

Kun Li;Yao Qin;Qiang Ling;Yingqian Wang;Zaiping Lin;Wei An

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Year: 2021 | Volume: 14 | Journal Article |
Cited by: Papers (33)
Deep subspace clustering network has shown its effectiveness in hyperspectral image (HSI) clustering. However, there are two major challenges that need to be addressed: 1) lack of effective supervision for feature learning; and 2) negative effect caused by the high redundancy of the global dictionary atoms. In this article, we propose an end-to-end trainable network for HSI clustering. Specificall...Show More

Self-Supervised Deep Subspace Clustering for Hyperspectral Images With Adaptive Self-Expressive Coefficient Matrix Initialization

Kun Li;Yao Qin;Qiang Ling;Yingqian Wang;Zaiping Lin;Wei An

Year: 2021 | Volume: 14 | Journal Article |

A TaOx-Based Electronic Synapse With High Precision for Neuromorphic Computing

Sen Liu;Kun Li;Yi Sun;Xi Zhu;Zhiwei Li;Bing Song;Haijun Liu;Qingjiang Li

IEEE Access
Year: 2019 | Volume: 7 | Journal Article |
Cited by: Papers (7)
Neuromorphic computing is a promising candidate for breaking the von Neumann bottleneck and developing high-efficient computing systems. Here we present a W/TaOx/Pt high-precision electronic synapse with excellent analog properties for neuromorphic computing. The device exhibits the potential of 10-bit weight precision, which is state of the art in conductance levels. Furthermore, the device shows...Show More

A TaOx-Based Electronic Synapse With High Precision for Neuromorphic Computing

Sen Liu;Kun Li;Yi Sun;Xi Zhu;Zhiwei Li;Bing Song;Haijun Liu;Qingjiang Li

Year: 2019 | Volume: 7 | Journal Article |

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