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Dinkar Vasudevan - IEEE Xplore Author Profile

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Author details

Dinkar Vasudevan

Also published under: D. Vasudevan

Publications

11

Citations

88

Publications by Year

20072011

Co-Authors:

Satish Babu KoradaDouglas J. LeithEtienne PerronVijay G. SubramanianM. Vojnovic

Dinkar Vasudevan

Also published under: D. Vasudevan

Affiliation

Icera

Inc.

Bristol, UK

Publication Topics

Decoding,
Error Probability,
Markov Chain,
Maximum Flow,
Multiple-access Channel,
Packet Arrival,
Time Slot,
Arbitrary Patterns,
Arrival Rate,
Average Algorithm,
Average Lag,
Binary Channel

Biography

Dinkar Vasudevan received the B.Tech. and M.Tech degrees in electrical engineering from the Indian Institute of Technology, Bombay, in 2003 and the Ph.D. degree from the School of Computer and Communication Sciences, EPFL, Laussane, Switzerland, in 2009.,He is currently with Icera, Inc., Bristol, U.K. His research interests include information theory at the physical and network layers.(Based on document published on 23 December 2010).

Publications

11

Citations

88

Publications by Year

20072011

Co-Authors:

Satish Babu Korada
Douglas J. Leith
Etienne Perron
Vijay G. Subramanian
M. Vojnovic

Author's Published Works

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Showing 1-25 of 34 results

Journals (29)

Conferences (5)

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Junmin Liu(34)
Jiangshe Zhang(19)
Chunxia Zhang(10)
Shuang Xu(8)
Zixiang Zhao(7)
School of Mathematics and Statistics, Xi’an Jiaotong University, Xi’an, China(22)
School of Mathematics and Statistics, Xi'an Jiaotong University, Xi'an, China(4)
School of Mathematics and Information Science, Guangzhou University, Guangzhou, China(3)
Faculty of Science, Kunming University of Science and Technology, Kunming, China(2)
School of Computer Science and Technology and the Ministry of Education Key Laboratory for Intelligent Networks and Network Security, Xi’an Jiaotong University, Xi’an, China(2)
IEEE Transactions on Geoscience and Remote Sensing(11)
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing(4)
IEEE Geoscience and Remote Sensing Letters(3)
IEEE Transactions on Neural Networks and Learning Systems(3)
2021 IEEE International Conference on Multimedia and Expo (ICME)(2)
IEEE(34)
Media(4)
Shenzhen, China(2)
Exeter, United Kingdom(1)
Nashville, TN, USA(1)
Vancouver, BC, Canada(1)
Convolutional Neural Network(14)
Multispectral Images(12)
Neural Network(12)
Deep Neural Network(11)
Feature Maps(10)

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Results

FSGformer: Frequency Separation and Guidance Transformer for Pansharpening

Qian Liu;Xiangyu Zhao;You Qin;Lanyu Li;Junmin Liu

IEEE Transactions on Geoscience and Remote Sensing

Year: 2025 | Volume: 63 | Journal Article |

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Pansharpening is a crucial task in remote sensing image processing, aiming to generate high-resolution multispectral (HRMS) images by fusing low-resolution multispectral (LRMS) images with high-resolution panchromatic (PAN) images. However, most current deep learning methods for pansharpening rarely consider the frequency differences in PAN and MS images effectively, resulting in harmful mixing of...Show More

FSGformer: Frequency Separation and Guidance Transformer for Pansharpening

Qian Liu;Xiangyu Zhao;You Qin;Lanyu Li;Junmin Liu

IEEE Transactions on Geoscience and Remote Sensing

Year: 2025 | Volume: 63 | Journal Article |

SemiCD-VL: Visual-Language Model Guidance Makes Better Semi-Supervised Change Detector

Kaiyu Li;Xiangyong Cao;Yupeng Deng;Jiayi Song;Junmin Liu;Deyu Meng;Zhi Wang

IEEE Transactions on Geoscience and Remote Sensing

Year: 2025 | Volume: 63 | Journal Article |

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Change detection (CD) aims to identify pixels with semantic changes between images. However, annotating massive numbers of pixel-level images is labor-intensive and costly, especially for multitemporal images, which require pixel-wise comparisons by human experts. Considering the excellent performance of visual-language models (VLMs) for zero-shot, OV, etc., with prompt-based reasoning, it is prom...Show More

SemiCD-VL: Visual-Language Model Guidance Makes Better Semi-Supervised Change Detector

Kaiyu Li;Xiangyong Cao;Yupeng Deng;Jiayi Song;Junmin Liu;Deyu Meng;Zhi Wang

IEEE Transactions on Geoscience and Remote Sensing

Year: 2025 | Volume: 63 | Journal Article |

An Attention-Based Feature Processing Method for Cross-Domain Hyperspectral Image Classification

Yazhen Wang;Guojun Liu;Lixia Yang;Junmin Liu;Lili Wei

IEEE Signal Processing Letters

Year: 2025 | Volume: 32 | Journal Article |

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Cross-domain classification of hyperspectral remote sensing images is one of the hotspots of research in recent years, and its main problem is insufficient training samples. To address this issue, few-shot learning (FSL) has emerged as a promising paradigm in cross-domain classification tasks. However, a notable limitation of most existing FSL methods is that they focus only on local information a...Show More

An Attention-Based Feature Processing Method for Cross-Domain Hyperspectral Image Classification

Yazhen Wang;Guojun Liu;Lixia Yang;Junmin Liu;Lili Wei

IEEE Signal Processing Letters

Year: 2025 | Volume: 32 | Journal Article |

CACNN: Capsule Attention Convolutional Neural Networks for 3D Object Recognition

Kai Sun;Jiangshe Zhang;Shuang Xu;Zixiang Zhao;Chunxia Zhang;Junmin Liu;Junying Hu

IEEE Transactions on Neural Networks and Learning Systems

Year: 2025 | Volume: 36, Issue: 3 | Journal Article |

Cited by: Papers (1)

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Recently, view-based approaches, which recognize a 3D object through its projected 2-D images, have been extensively studied and have achieved considerable success in 3D object recognition. Nevertheless, most of them use a pooling operation to aggregate viewwise features, which usually leads to the visual information loss. To tackle this problem, we propose a novel layer called capsule attention l...Show More

CACNN: Capsule Attention Convolutional Neural Networks for 3D Object Recognition

Kai Sun;Jiangshe Zhang;Shuang Xu;Zixiang Zhao;Chunxia Zhang;Junmin Liu;Junying Hu

IEEE Transactions on Neural Networks and Learning Systems

Year: 2025 | Volume: 36, Issue: 3 | Journal Article |

A Lightweight Sparse Focus Transformer for Remote Sensing Image Change Captioning

Dongwei Sun;Yajie Bao;Junmin Liu;Xiangyong Cao

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing

Year: 2024 | Volume: 17 | Journal Article |

Cited by: Papers (2)

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Remote sensing image change captioning (RSICC) aims to automatically generate sentences that describe content differences in remote sensing bitemporal images. Recently, attention-based transformers have become a prevalent idea for capturing the features of global change. However, existing transformer-based RSICC methods face challenges, e.g., high parameters and high computational complexity cause...Show More

A Lightweight Sparse Focus Transformer for Remote Sensing Image Change Captioning

Dongwei Sun;Yajie Bao;Junmin Liu;Xiangyong Cao

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing

Year: 2024 | Volume: 17 | Journal Article |

CRS-Diff: Controllable Remote Sensing Image Generation With Diffusion Model

Datao Tang;Xiangyong Cao;Xingsong Hou;Zhongyuan Jiang;Junmin Liu;Deyu Meng

IEEE Transactions on Geoscience and Remote Sensing

Year: 2024 | Volume: 62 | Journal Article |

Cited by: Papers (4)

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The emergence of generative models has revolutionized the field of remote sensing (RS) image generation. Despite generating high-quality images, existing methods are limited in relying mainly on text control conditions, and thus do not always generate images accurately and stably. In this article, we propose CRS-Diff, a new RS generative framework specifically tailored for RS image generation, lev...Show More

CRS-Diff: Controllable Remote Sensing Image Generation With Diffusion Model

Datao Tang;Xiangyong Cao;Xingsong Hou;Zhongyuan Jiang;Junmin Liu;Deyu Meng

IEEE Transactions on Geoscience and Remote Sensing

Year: 2024 | Volume: 62 | Journal Article |

Sharpness-Aware Lookahead for Accelerating Convergence and Improving Generalization

Chengli Tan;Jiangshe Zhang;Junmin Liu;Yihong Gong

IEEE Transactions on Pattern Analysis and Machine Intelligence

Year: 2024 | Volume: 46, Issue: 12 | Journal Article |

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Lookahead is a popular stochastic optimizer that can accelerate the training process of deep neural networks. However, the solutions found by Lookahead often generalize worse than those found by its base optimizers, such as SGD and Adam. To address this issue, we propose Sharpness-Aware Lookahead (SALA), a novel optimizer that aims to identify flat minima that generalize well. SALA divides the tra...Show More

Sharpness-Aware Lookahead for Accelerating Convergence and Improving Generalization

Chengli Tan;Jiangshe Zhang;Junmin Liu;Yihong Gong

IEEE Transactions on Pattern Analysis and Machine Intelligence

Year: 2024 | Volume: 46, Issue: 12 | Journal Article |

An Emerging Incremental Fuzzy Concept-Cognitive Learning Model Based on Granular Computing and Conceptual Knowledge Clustering

Xiaoyuan Deng;Jinhai Li;Yuhua Qian;Junmin Liu

IEEE Transactions on Emerging Topics in Computational Intelligence

Year: 2024 | Volume: 8, Issue: 3 | Journal Article |

Cited by: Papers (3)

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Fuzzy granular concepts are fundamental units in developing computational intelligence approaches based on fuzzy concept-cognitive learning. However, existing models in this field merely focus on the information provided by fuzzy granular concepts induced by objects, ignoring that of those induced by attributes. Consequently, these models underutilize the information provided by fuzzy granular con...Show More

An Emerging Incremental Fuzzy Concept-Cognitive Learning Model Based on Granular Computing and Conceptual Knowledge Clustering

Xiaoyuan Deng;Jinhai Li;Yuhua Qian;Junmin Liu

IEEE Transactions on Emerging Topics in Computational Intelligence

Year: 2024 | Volume: 8, Issue: 3 | Journal Article |

Constrained Nonnegative Matrix Factorization Based on Label Propagation for Data Representation

Junmin Liu;;Jing Ma;Di Han;Yifan Huang

IEEE Transactions on Artificial Intelligence

Year: 2024 | Volume: 5, Issue: 2 | Journal Article |

Cited by: Papers (4)

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Nonnegative matrix factorization (NMF) algorithms are a series of dimensional reduction techniques widely used in data preprocessing. To improve the performance of clustering and the discrimination of the low-dimensional representation in NMF, we proposed a novel semisupervised constrained nonnegative matrix factorization based on label propagation (LpCNMF). Specifically, the proposed LpCNMF adopt...Show More

Constrained Nonnegative Matrix Factorization Based on Label Propagation for Data Representation

Junmin Liu;;Jing Ma;Di Han;Yifan Huang

IEEE Transactions on Artificial Intelligence

Year: 2024 | Volume: 5, Issue: 2 | Journal Article |

Understanding Short-Range Memory Effects in Deep Neural Networks

Chengli Tan;Jiangshe Zhang;Junmin Liu

IEEE Transactions on Neural Networks and Learning Systems

Year: 2024 | Volume: 35, Issue: 8 | Journal Article |

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Stochastic gradient descent (SGD) is of fundamental importance in deep learning. Despite its simplicity, elucidating its efficacy remains challenging. Conventionally, the success of SGD is ascribed to the stochastic gradient noise (SGN) incurred in the training process. Based on this consensus, SGD is frequently treated and analyzed as the Euler–Maruyama discretization of stochastic differential e...Show More

Understanding Short-Range Memory Effects in Deep Neural Networks

Chengli Tan;Jiangshe Zhang;Junmin Liu

IEEE Transactions on Neural Networks and Learning Systems

Year: 2024 | Volume: 35, Issue: 8 | Journal Article |

Deep Convolutional Sparse Coding Networks for Interpretable Image Fusion

Zixiang Zhao;Jiangshe Zhang;Haowen Bai;Yicheng Wang;Yukun Cui;Lilun Deng;Kai Sun;Chunxia Zhang;Junmin Liu;Shuang Xu

2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

Year: 2023 | Conference Paper |

Cited by: Papers (5)

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Image fusion is a significant problem in many fields including digital photography, computational imaging and remote sensing, to name but a few. Recently, deep learning has emerged as an important tool for image fusion. This paper presents CSCFuse, which contains three deep convolutional sparse coding (CSC) networks for three kinds of image fusion tasks (i.e., infrared and visible image fusion, mu...Show More

Deep Convolutional Sparse Coding Networks for Interpretable Image Fusion

Zixiang Zhao;Jiangshe Zhang;Haowen Bai;Yicheng Wang;Yukun Cui;Lilun Deng;Kai Sun;Chunxia Zhang;Junmin Liu;Shuang Xu

2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

Year: 2023 | Conference Paper |

Deep Tensor Attention Prior Network for Hyperspectral Image Denoising

Weilin Shen;Junmin Liu;Jinhai Li;Chao Tian

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing

Year: 2023 | Volume: 16 | Journal Article |

Cited by: Papers (1)

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Hyperspectral imaging techniques can generate continuous narrowband images with a high spectral resolution. However, owing to environmental disturbances, atmospheric effects, and hardware limitations of hyperspectral imaging sensors, captured hyperspectral images (HSIs) often contain complex noises that significantly degrades their quality and limits their utility. In this article, we design a dee...Show More

Deep Tensor Attention Prior Network for Hyperspectral Image Denoising

Weilin Shen;Junmin Liu;Jinhai Li;Chao Tian

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing

Year: 2023 | Volume: 16 | Journal Article |

MD³Net: Integrating Model-Driven and Data-Driven Approaches for Pansharpening

Yinsong Yan;Junmin Liu;Shuang Xu;Yicheng Wang;Xiangyong Cao

IEEE Transactions on Geoscience and Remote Sensing

Year: 2022 | Volume: 60 | Journal Article |

Cited by: Papers (14)

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Pansharpening is a special image fusion task of reconstructing a high-resolution multispectral (HRMS) image by integrating a panchromatic (PAN) image of high spatial resolution and a low-resolution multispectral (LRMS) image. To handle such an ill-posed multimodal fusion task, in this article, we propose a novel pansharpening method, referred to as model-driven and data-driven network (MD3Net), wh...Show More

MD³Net: Integrating Model-Driven and Data-Driven Approaches for Pansharpening

Yinsong Yan;Junmin Liu;Shuang Xu;Yicheng Wang;Xiangyong Cao

IEEE Transactions on Geoscience and Remote Sensing

Year: 2022 | Volume: 60 | Journal Article |

SRAF-Net: A Scene-Relevant Anchor-Free Object Detection Network in Remote Sensing Images

Junmin Liu;Shijie Li;Changsheng Zhou;Xiangyong Cao;Yong Gao;Bo Wang

IEEE Transactions on Geoscience and Remote Sensing

Year: 2022 | Volume: 60 | Journal Article |

Cited by: Papers (32)

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Object detection is a fundamental and important task in the analysis of remote sensing images (RSIs), and existing deep learning-based object detection models in this literature strongly rely on predefined anchor boxes and encounter redesigned difficulties related to anchors. In addition, they often ignore the scene-contextual information that objects are usually closely related to their surroundi...Show More

SRAF-Net: A Scene-Relevant Anchor-Free Object Detection Network in Remote Sensing Images

Junmin Liu;Shijie Li;Changsheng Zhou;Xiangyong Cao;Yong Gao;Bo Wang

IEEE Transactions on Geoscience and Remote Sensing

Year: 2022 | Volume: 60 | Journal Article |

Optimization Algorithm Unfolding Deep Networks of Detail Injection Model for Pansharpening

Yunqiao Feng;Junmin Liu;Kun Chen;Bo Wang;Zixiang Zhao

IEEE Geoscience and Remote Sensing Letters

Year: 2022 | Volume: 19 | Journal Article |

Cited by: Papers (6)

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Pansharpening aims at integrating a high-spatial-resolution panchromatic (PAN) image with a low-spatial-resolution multispectral (MS) image to generate a high-resolution MS (HRMS) image. It is a fundamental and significant task in the field of remotely sensed images. Classic and convolutional neural network (CNN)-based algorithms have been developed, over the last decades, for pansharpening based ...Show More

Optimization Algorithm Unfolding Deep Networks of Detail Injection Model for Pansharpening

Yunqiao Feng;Junmin Liu;Kun Chen;Bo Wang;Zixiang Zhao

IEEE Geoscience and Remote Sensing Letters

Year: 2022 | Volume: 19 | Journal Article |

Efficient and Model-Based Infrared and Visible Image Fusion via Algorithm Unrolling

Zixiang Zhao;Shuang Xu;Jiangshe Zhang;Chengyang Liang;Chunxia Zhang;Junmin Liu

IEEE Transactions on Circuits and Systems for Video Technology

Year: 2022 | Volume: 32, Issue: 3 | Journal Article |

Cited by: Papers (104)

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Infrared and visible image fusion (IVIF) expects to obtain images that retain thermal radiation information from infrared images and texture details from visible images. In this paper, a model-based convolutional neural network (CNN) model, referred to as Algorithm Unrolling Image Fusion (AUIF), is proposed to overcome the shortcomings of traditional CNN-based IVIF models. The proposed AUIF model ...Show More

Efficient and Model-Based Infrared and Visible Image Fusion via Algorithm Unrolling

Zixiang Zhao;Shuang Xu;Jiangshe Zhang;Chengyang Liang;Chunxia Zhang;Junmin Liu

IEEE Transactions on Circuits and Systems for Video Technology

Year: 2022 | Volume: 32, Issue: 3 | Journal Article |

AND: Effective Coupling of Accuracy, Novelty and Diversity in the Recommender System

Di Han;Yifan Huang;Xiaotian Jing;Junmin Liu

2021 17th International Conference on Mobility, Sensing and Networking (MSN)

Year: 2021 | Conference Paper |

Cited by: Papers (1)

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At present, most of the research on recommender system (RS) based on artificial intelligence focus on the algorithm, but the evaluation metrics being an important process to evaluate the performance of RS are usually ignored. Specifically, independent evaluation metrics cannot effectively reflect the differences between algorithms, so how to effectively couple these evaluation metrics needs furthe...Show More

AND: Effective Coupling of Accuracy, Novelty and Diversity in the Recommender System

Di Han;Yifan Huang;Xiaotian Jing;Junmin Liu

2021 17th International Conference on Mobility, Sensing and Networking (MSN)

Year: 2021 | Conference Paper |

Deep Gradient Projection Networks for Pan-sharpening

Shuang Xu;Jiangshe Zhang;Zixiang Zhao;Kai Sun;Junmin Liu;Chunxia Zhang

2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Year: 2021 | Conference Paper |

Cited by: Papers (139)

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Pan-sharpening is an important technique for remote sensing imaging systems to obtain high resolution multi-spectral images. Recently, deep learning has become the most popular tool for pan-sharpening. This paper develops a model-based deep pan-sharpening approach. Specifically, two optimization problems regularized by the deep prior are formulated, and they are separately responsible for the gene...Show More

Deep Gradient Projection Networks for Pan-sharpening

Shuang Xu;Jiangshe Zhang;Zixiang Zhao;Kai Sun;Junmin Liu;Chunxia Zhang

2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Year: 2021 | Conference Paper |

Deep Convolutional Sparse Coding Network For Pansharpening With Guidance Of Side Information

Shuang Xu;Jiangshe Zhang;Kai Sun;Zixiang Zhao;Lu Huang;Junmin Liu;Chunxia Zhang

2021 IEEE International Conference on Multimedia and Expo (ICME)

Year: 2021 | Conference Paper |

Cited by: Papers (8)

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Pansharpening is a fundamental issue in remote sensing field. This paper proposes a side information partially guided convolutional sparse coding (SCSC) model for pansharpening. The key idea is to split the low resolution multispectral image into a panchromatic image related feature map and a panchromatic image irrelated feature map, where the former one is regularized by the side information from...Show More

Deep Convolutional Sparse Coding Network For Pansharpening With Guidance Of Side Information

Shuang Xu;Jiangshe Zhang;Kai Sun;Zixiang Zhao;Lu Huang;Junmin Liu;Chunxia Zhang

2021 IEEE International Conference on Multimedia and Expo (ICME)

Year: 2021 | Conference Paper |

FGF-GAN: A Lightweight Generative Adversarial Network for Pansharpening via Fast Guided Filter

Zixiang Zhao;Jiangshe Zhan;Shuang Xu;Kai Sun;Lu Huang;Junmin Liu;Chunxia Zhang

2021 IEEE International Conference on Multimedia and Expo (ICME)

Year: 2021 | Conference Paper |

Cited by: Papers (21)

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Pansharpening is a widely used image enhancement technique for remote sensing. Its principle is to fuse the input high-resolution single-channel panchromatic (PAN) image and low-resolution multi-spectral image and to obtain a high-resolution multi-spectral (HRMS) image. The existing deep learning pansharpening method has two shortcomings. First, features of two input images need to be concatenated...Show More

FGF-GAN: A Lightweight Generative Adversarial Network for Pansharpening via Fast Guided Filter

Zixiang Zhao;Jiangshe Zhan;Shuang Xu;Kai Sun;Lu Huang;Junmin Liu;Chunxia Zhang

2021 IEEE International Conference on Multimedia and Expo (ICME)

Year: 2021 | Conference Paper |

Pansharpening Via Neighbor Embedding of Spatial Details

Junmin Liu;Changsheng Zhou;Rongrong Fei;Chunxia Zhang;Jiangshe Zhang

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing

Year: 2021 | Volume: 14 | Journal Article |

Cited by: Papers (9)

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The spatial details injection model has been considered as a general framework in the literature of pansharpening, and recently there have been significant advances in this framework based on sparse representation (SR) of spatial details. However, the SR-based methods have greater computational burden in estimating the sparse vectors and limited ability in detail edge preservation. In this article...Show More

Pansharpening Via Neighbor Embedding of Spatial Details

Junmin Liu;Changsheng Zhou;Rongrong Fei;Chunxia Zhang;Jiangshe Zhang

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing

Year: 2021 | Volume: 14 | Journal Article |

Global Context-Augmented Objection Detection in VHR Optical Remote Sensing Images

Guang Shi;Jiangshe Zhang;Junmin Liu;Chunxia Zhang;Changsheng Zhou;Shuyun Yang

IEEE Transactions on Geoscience and Remote Sensing

Year: 2021 | Volume: 59, Issue: 12 | Journal Article |

Cited by: Papers (19)

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The deep learning method, especially convolution neural networks (CNNs), has recently made ground-breaking advances on object detection in very-high-resolution (VHR) optical remote sensing images. However, as CNN is originally designed for the classification of natural images, these methods are not very suitable for object detection of remote sensing images. First, current CNN-based approaches hav...Show More

Global Context-Augmented Objection Detection in VHR Optical Remote Sensing Images

Guang Shi;Jiangshe Zhang;Junmin Liu;Chunxia Zhang;Changsheng Zhou;Shuyun Yang

IEEE Transactions on Geoscience and Remote Sensing

Year: 2021 | Volume: 59, Issue: 12 | Journal Article |

DRCNN: Dynamic Routing Convolutional Neural Network for Multi-View 3D Object Recognition

Kai Sun;Jiangshe Zhang;Junmin Liu;Ruixuan Yu;Zengjie Song

IEEE Transactions on Image Processing

Year: 2021 | Volume: 30 | Journal Article |

Cited by: Papers (35)

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3D object recognition is one of the most important tasks in 3D data processing, and has been extensively studied recently. Researchers have proposed various 3D recognition methods based on deep learning, among which a class of view-based approaches is a typical one. However, in the view-based methods, the commonly used view pooling layer to fuse multi-view features causes a loss of visual informat...Show More

DRCNN: Dynamic Routing Convolutional Neural Network for Multi-View 3D Object Recognition

Kai Sun;Jiangshe Zhang;Junmin Liu;Ruixuan Yu;Zengjie Song

IEEE Transactions on Image Processing

Year: 2021 | Volume: 30 | Journal Article |

Bayesian Transfer Learning for Object Detection in Optical Remote Sensing Images

Changsheng Zhou;Jiangshe Zhang;Junmin Liu;Chunxia Zhang;Guang Shi;Junying Hu

IEEE Transactions on Geoscience and Remote Sensing

Year: 2020 | Volume: 58, Issue: 11 | Journal Article |

Cited by: Papers (17)

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In the literature of object detection in optical remote sensing images, a popular pipeline is first modifying an off-the-shelf deep neural network, then initializing the modified network by pretrained weights on a source data set, and finally fine-tuning the network on a target data set. The procedure works well in practice but might not make full use of underlying knowledge implied by pretrained ...Show More

Bayesian Transfer Learning for Object Detection in Optical Remote Sensing Images

Changsheng Zhou;Jiangshe Zhang;Junmin Liu;Chunxia Zhang;Guang Shi;Junying Hu

IEEE Transactions on Geoscience and Remote Sensing

Year: 2020 | Volume: 58, Issue: 11 | Journal Article |

HAM-MFN: Hyperspectral and Multispectral Image Multiscale Fusion Network With RAP Loss

Shuang Xu;Ouafa Amira;Junmin Liu;Chun-Xia Zhang;Jiangshe Zhang;Guanghai Li

IEEE Transactions on Geoscience and Remote Sensing

Year: 2020 | Volume: 58, Issue: 7 | Journal Article |

Cited by: Papers (54)

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The fusion of hyperspectral image (HSI) and multispectral image (MSI) is one of the most significant topics in remote sensing image processing. Recently, deep learning (DL) has emerged as an important tool for this task. However, existing DL-based methods have two drawbacks, that is, limited ability for feature extraction and suffering from spectral distortion. To address these issues, this articl...Show More

HAM-MFN: Hyperspectral and Multispectral Image Multiscale Fusion Network With RAP Loss

Shuang Xu;Ouafa Amira;Junmin Liu;Chun-Xia Zhang;Jiangshe Zhang;Guanghai Li

IEEE Transactions on Geoscience and Remote Sensing

Year: 2020 | Volume: 58, Issue: 7 | Journal Article |

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