Jianjun Liu

ORCID: 0000-0003-0778-9094
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About
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Research Areas
  • Remote-Sensing Image Classification
  • Advanced Image Fusion Techniques
  • Remote Sensing and Land Use
  • Image and Signal Denoising Methods
  • Face and Expression Recognition
  • Advanced Image and Video Retrieval Techniques
  • Advanced Sensor and Control Systems
  • Image Processing Techniques and Applications
  • Advanced Measurement and Detection Methods
  • Advanced Algorithms and Applications
  • Industrial Vision Systems and Defect Detection
  • Sparse and Compressive Sensing Techniques
  • Advanced Image Processing Techniques
  • Reservoir Engineering and Simulation Methods
  • Spectroscopy and Chemometric Analyses
  • Image Retrieval and Classification Techniques
  • Safety Systems Engineering in Autonomy
  • Image Enhancement Techniques
  • Radiation Effects in Electronics
  • Software Reliability and Analysis Research
  • Evaluation Methods in Various Fields
  • Optical Systems and Laser Technology
  • Optical measurement and interference techniques
  • Advanced Decision-Making Techniques
  • Drilling and Well Engineering

Jiangnan University
2016-2025

China Railway Group (China)
2024

Qingdao Huanghai University
2024

Southeast University
2023

City University of Hong Kong
2019-2022

China University of Petroleum, Beijing
2012-2020

Tianjin University
2019

Nanjing University of Science and Technology
2011-2019

Nanjing University of Information Science and Technology
2016

PLA 306 Hospital
2015

In recent years, deep learning-based methods have been extensively utilized in remote sensing image scene classification and achieved remarkable performance. The wide geographical coverage resolution differences of images result significant within-class diversity between-class similarity, hindering the further improvement accuracy. Attention-based automatically estimate importance local regions by learning weight assignments, which effectively enhance feature extraction capability network....

10.1109/tgrs.2023.3336471 article EN IEEE Transactions on Geoscience and Remote Sensing 2023-01-01

This article focuses on hyperspectral image (HSI) super-resolution that aims to fuse a low-spatial-resolution HSI and high-spatial-resolution multispectral form (HR-HSI). Existing deep learning-based approaches are mostly supervised rely large number of labeled training samples, which is unrealistic. The commonly used model-based unsupervised flexible but handcrafted priors. Inspired by the specific properties model, we make first attempt design model-inspired network for in an manner....

10.1109/tgrs.2022.3143156 article EN IEEE Transactions on Geoscience and Remote Sensing 2022-01-01

Hyperspectral image super-resolution addresses the problem of fusing a low-resolution hyperspectral (LR-HSI) and high-resolution multispectral (HR-MSI) to produce (HR-HSI). In this paper, we propose novel fusion approach for by exploiting specific properties matrix decomposition, which consists four main steps. First, an endmember extraction algorithm is used extract initial spectral from LR-HSI. Then, with matrix, estimate spatial i.e., spatial-contextual information, degraded observations...

10.1109/tip.2020.3009830 article EN IEEE Transactions on Image Processing 2020-01-01

Hyperspectral image (HSI) fusion refers to the reconstruction of a high-resolution HSI by fusing low-resolution (LR-HSI) and multispectral (HR-MSI) over same scene. Recently, researchers have proposed many approaches handle this issue. However, most them assume that both spatial spectral degradation functions are known, which often limited or unavailable in reality. This article presents novel model-driven deep network based on matrix decomposition, considers correlations reasonably embeds...

10.1109/tgrs.2021.3112181 article EN IEEE Transactions on Geoscience and Remote Sensing 2021-09-24

Significant progress has been achieved in remote sensing image scene classification (RSISC) with the development of convolutional neural networks (CNNs) and vision transformers (ViT). However, high intra-class diversity inter-class similarity are still enormous challenges for RSISC. Metric learning can effectively improve discriminative ability deep representations by constraining distance between features. Previous metric methods only optimize feature space representation through function,...

10.1109/tgrs.2023.3262840 article EN IEEE Transactions on Geoscience and Remote Sensing 2023-01-01

Hyperspectral target detection (HTD) is an important issue in earth observation, with applications both military and civilian domains. However, conventional representation-based detectors are hindered by the reliance on unknown background dictionary, limited ability to capture nonlinear representations using linear mixing model (LMM), insufficient background-target recognition based handcrafted priors. To address these problems, this paper proposes interpretable representation network that...

10.1109/tgrs.2023.3302950 article EN IEEE Transactions on Geoscience and Remote Sensing 2023-01-01

Deep learning-based methods based on convolutional neural networks (CNNs) have demonstrated remarkable performance in hyperspectral image (HSI) classification. Most of these approaches are only 2-D CNN or 3-D CNN. It is dramatic from the literature that using just may result missing channel relationship information, and make model very complex. Moreover, existing network models do not pay enough attention to extracting spectral-spatial correlation information. To address issues, we propose a...

10.1109/jstars.2020.3016739 article EN cc-by IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2020-01-01

Hyperspectral and multispectral image fusion aims to fuse a low-spatial-resolution hyperspectral (HSI) high-spatial-resolution form HSI. Motivated by the success of model- deep learning-based approaches, we propose novel patch-aware approach for HSI unfolding subspace-based optimization model, where moderate-sized patches are used in both training test phases. The goal this is make full use information patch under subspace representation, restrict scale enhance interpretability network,...

10.1109/jstars.2022.3140211 article EN cc-by-nc-nd IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2022-01-01

In this article, we propose a novel tensorial approach, namely, generalized tensor regression, for hyperspectral image classification. First, simple and effective classifier, i.e., the ridge regression multivariate labels, is extended to its version by taking advantages of representation. Then, discrimination information different modes exploited further strengthen capacity model. Moreover, model can be simplified solved easily. Different from traditional methods, proposed utilized capture...

10.1109/tgrs.2019.2944989 article EN IEEE Transactions on Geoscience and Remote Sensing 2019-10-21

Fusion-based hyperspectral image (HSI) superresolution aims to produce a high-spatial-resolution HSI by fusing low-spatial-resolution and multispectral image.Such super-resolution process can be modeled as an inverse problem, where the prior knowledge is essential for obtaining desired solution.Motivated success of diffusion models, we propose novel spectral fusion-based super-resolution.Specifically, first investigate spectrum generation problem design model data distribution.Then, in...

10.1109/tgrs.2024.3449073 article EN IEEE Transactions on Geoscience and Remote Sensing 2024-01-01

Convolutional Neural Networks (CNNs) are widely used in various fields, and have shown good performance hyperspectral image (HSI) classification. Recently, utilizing deep networks to learn spatial-spectral features has become of great interest. However, excessively increasing the depth network may result overfitting. Moreover, HSI classification, existing models ignore strong complementary yet correlated information among different hierarchical layers. In order address these two problems, a...

10.1080/2150704x.2020.1779374 article EN Remote Sensing Letters 2020-06-25

From the non-local self-similarity (NSS)-based image denoising to convolutional-network (ConvNet)-based denoising, performance has been greatly improved. However, it is still not clear how utilize similar web images guide using ConvNet. This paper proposes a novel ConvNet for explore both internal (NSS) and external correlations when are available. Since may be taken with different viewpoints, focal lengths, contain objects, difficult directly at level Therefore, we propose an patch matching...

10.1109/tcsvt.2019.2930305 article EN IEEE Transactions on Circuits and Systems for Video Technology 2019-07-22

Fusing a low-resolution hyperspectral (LRHS) image and high-resolution multispectral (HRMS) to generate (HRHS) has grown significant attractive application in remote sensing fields. Recently, the popularization of deep learning injected more possibilities into fusion work. However, there still exists difficulty that is how make best acquired LRHS HRMS images. In this article, we present twice optimizing net with matrix decomposition fulfill task, which can be roughly divided three stages:...

10.1109/jstars.2020.3009250 article EN cc-by IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2020-01-01

10.1109/jstars.2025.3564589 article EN cc-by IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2025-01-01

This letter presents a postprocessing algorithm for kernel sparse representation (KSR)-based hyperspectral image classifier, which is based on the integration of spatial and spectral information. A pixelwise KSR first used to find coefficient vectors image. Then, sparsity concentration index (SCI) rule-guided semilocal graph regularization (SSG), called SSG+SCI, proposed determine refined that promote continuity within each class. Finally, these are obtain final classification map. Compared...

10.1109/lgrs.2013.2292831 article EN IEEE Geoscience and Remote Sensing Letters 2014-01-31

Recently, more and multi-layer perceptron (MLP) -like models have been proposed. Among them, CycleMLP is good at dense feature prediction tasks, which potentially useful for hyperspectral image (HSI) classification. However, the receptive field of tends to be cross-shaped, will lead insufficient spatial information extraction. Additionally, most HSI classification methods only use from single data. Lack diversity in features a modality limits performance. To address these issues, novel...

10.1109/jstars.2022.3216590 article EN cc-by IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2022-01-01

The main challenges of remote sensing image scene classification are extracting discriminative features and making full use the training data. current mainstream deep learning methods usually only hard labels samples, ignoring potential soft natural labels. Self-supervised can take advantage However, it is difficult to train a self-supervised network due limitations dataset computing resources. We propose knowledge distillation (SSKDNet) solve aforementioned challenges. Specifically, feature...

10.3390/rs14194813 article EN cc-by Remote Sensing 2022-09-27

This paper presents a region-based relaxed multiple kernel collaborative representation method for the spatial-spectral classification of hyperspectral images. The proposed consists three steps. In first step, multiscale achieved by extending superpixel segmentation algorithm is designed to capture information For each scale, image can be segmented into several nonoverlapping spectrally similar regions that consist some spatially adjacent pixels. second two criteria (i.e., moments) are...

10.1109/access.2017.2758168 article EN cc-by-nc-nd IEEE Access 2017-01-01

This paper presents a spatial-spectral method for hyperspectral image classification in the regularization framework of kernel sparse representation. First, two constraint terms are appended to recovery model The first one is graph-based spatially-smooth which utilized describe contextual information images. second spatial location constraint, exploited incorporate prior knowledge training pixels. Then, an efficient alternating direction multipliers developed solve corresponding minimization...

10.3390/ijgi6080258 article EN cc-by ISPRS International Journal of Geo-Information 2017-08-22

Kernel Fisher discriminant analysis (KFD) can map well-log data into a nonlinear feature space to make linear nonseparable problem of fracture identification separable one. Commonly, KFD uses one kernel. However, the prediction capacity based on kernel is limited some extent, especially for complex classification problem, such as in tight sandstone reservoirs. To alleviate this we have used multiple (MKFD) method recognize zones. MKFD multiscaled Gaussian functions instead single realize...

10.1190/int-2020-0048.1 article EN Interpretation 2020-08-12
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