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Prof. Dr. Licheng Jiao
Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, International Research Center for Intelligent Perception and Computation, Joint International Research Laboratory of Intelligent Perception and Computation, School of Artificial Intelligence, Xidian University, Xi'an 710071, China

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Research Keywords & Expertise

0 Artificial Intelligence
0 Big Data
0 Pattern Recognition
0 Natural computation
0 Image intelligent perception

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Short Biography

Licheng Jiao was born in 1959. He received his B.S. degree from Shanghai Jiaotong University, Shanghai, China, in 1982, and his M.S. and Ph.D. degrees from Xi'an Jiaotong University, Xi'an, China, in 1984 and 1990, respectively. Since 1992, Dr. Jiao has been a professor at the School of Electronic Engineering of Xidian University. Currently, he is the director of the Key Lab of Intelligent Perception and Image Understanding of the Ministry of Education of China at Xidian University, Xi'an, China. Dr. Jiao is the chairman of the Awards and Recognition Committee, a vice board chairperson of the Chinese Association of Artificial Intelligence, a councilor of the Chinese Institute of Electronics, a committee member of the Chinese Committee of Neural Networks, and an expert of the Academic Degrees Committee of the State Council. His research interests include image processing, natural computation, machine learning, and intelligent information processing.

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Technical note
Published: 25 August 2021 in Remote Sensing
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Deep convolutional neural networks (DCNNs) are driving progress in object detection of high-resolution remote sensing images. Region proposal generation, as one of the key steps in object detection, has also become the focus of research. High-resolution remote sensing images usually contain various sizes of objects and complex background, small objects are easy to miss or be mis-identified in object detection. If the recall rate of region proposal of small objects and multi-scale objects can be improved, it will bring an improvement on the performance of the accuracy in object detection. Spatial attention is the ability to focus on local features in images and can improve the learning efficiency of DCNNs. This study proposes a multi-scale spatial attention region proposal network (MSA-RPN) for high-resolution optical remote sensing imagery. The MSA-RPN is an end-to-end deep learning network with a backbone network of ResNet. It deploys three novel modules to fulfill its task. First, the Scale-specific Feature Gate (SFG) focuses on features of objects by processing multi-scale features extracted from the backbone network. Second, the spatial attention-guided model (SAGM) obtains spatial information of objects from the multi-scale attention maps. Third, the Selective Strong Attention Maps Model (SSAMM) adaptively selects sliding windows according to the loss values from the system’s feedback, and sends the windowed samples to the spatial attention decoder. Finally, the candidate regions and their corresponding confidences can be obtained. We evaluate the proposed network in a public dataset LEVIR and compare with several state-of-the-art methods. The proposed MSA-RPN yields a higher recall rate of region proposal generation, especially for small targets in remote sensing images.

ACS Style

Ruchan Dong; Licheng Jiao; Yan Zhang; Jin Zhao; Weiyan Shen. A Multi-Scale Spatial Attention Region Proposal Network for High-Resolution Optical Remote Sensing Imagery. Remote Sensing 2021, 13, 3362 .

AMA Style

Ruchan Dong, Licheng Jiao, Yan Zhang, Jin Zhao, Weiyan Shen. A Multi-Scale Spatial Attention Region Proposal Network for High-Resolution Optical Remote Sensing Imagery. Remote Sensing. 2021; 13 (17):3362.

Chicago/Turabian Style

Ruchan Dong; Licheng Jiao; Yan Zhang; Jin Zhao; Weiyan Shen. 2021. "A Multi-Scale Spatial Attention Region Proposal Network for High-Resolution Optical Remote Sensing Imagery." Remote Sensing 13, no. 17: 3362.

Journal article
Published: 17 August 2021 in IEEE Transactions on Geoscience and Remote Sensing
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The remote sensing image captioning has attracted wide spread attention in remote sensing field due to its application potentiality. However, most existing approaches model limited interactions between image content and sentence and fail to exploit special characteristics of the remote sensing images. We introduce a novel recurrent attention and semantic gate (RASG) framework to facilitate the remote sensing image captioning in this article, which integrates competitive visual features and a recurrent attention mechanism to generate a better context vector for the images every time as well as enhances the representations of the current word state. Specifically, we first project each image into competitive visual features by taking the advantage of both static visual features and multiscale features. Then, a novel recurrent attention mechanism is developed to extract the high-level attentive maps from encoded features and nonvisual features, which can help the decoder recognize and focus on the effective information for understanding the complex content of the remote sensing images. Finally, the hidden states from the long short-term memory (LSTM) and other semantic references are incorporated into a semantic gate, which contributes to more comprehensive and precise semantic understanding. Comprehensive experiments on three widely used datasets, Sydney-Captions, UCM-Captions, and Remote Sensing Image Captioning Dataset, have demonstrated the superiority of the proposed RASG over a series of attentive models based on image captioning methods.

ACS Style

Yunpeng Li; Xiangrong Zhang; Jing Gu; Chen Li; Xin Wang; Xu Tang; Licheng Jiao. Recurrent Attention and Semantic Gate for Remote Sensing Image Captioning. IEEE Transactions on Geoscience and Remote Sensing 2021, PP, 1 -16.

AMA Style

Yunpeng Li, Xiangrong Zhang, Jing Gu, Chen Li, Xin Wang, Xu Tang, Licheng Jiao. Recurrent Attention and Semantic Gate for Remote Sensing Image Captioning. IEEE Transactions on Geoscience and Remote Sensing. 2021; PP (99):1-16.

Chicago/Turabian Style

Yunpeng Li; Xiangrong Zhang; Jing Gu; Chen Li; Xin Wang; Xu Tang; Licheng Jiao. 2021. "Recurrent Attention and Semantic Gate for Remote Sensing Image Captioning." IEEE Transactions on Geoscience and Remote Sensing PP, no. 99: 1-16.

Journal article
Published: 05 August 2021 in IEEE Transactions on Image Processing
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Deep convolutional neural networks attract increasing attention in image patch matching. However, most of them rely on a single similarity learning model, such as feature distance and the correlation of concatenated features. Their performances will degenerate due to the complex relation between matching patches caused by various imagery changes. To tackle this challenge, we propose a multi-relation attention learning network (MRAN) for image patch matching. Specifically, we propose to fuse multiple feature relations (MR) for matching, which can benefit from the complementary advantages between different feature relations and achieve significant improvements on matching tasks. Furthermore, we propose a relation attention learning module to learn the fused relation adaptively. With this module, meaningful feature relations are emphasized and the others are suppressed. Extensive experiments show that our MRAN achieves best matching performances, and has good generalization on multi-modal image patch matching, multi-modal remote sensing image patch matching and image retrieval tasks.

ACS Style

Dou Quan; Shuang Wang; Yi Li; Bowu Yang; Ning Huyan; Jocelyn Chanussot; Biao Hou; Licheng Jiao. Multi-Relation Attention Network for Image Patch Matching. IEEE Transactions on Image Processing 2021, 30, 7127 -7142.

AMA Style

Dou Quan, Shuang Wang, Yi Li, Bowu Yang, Ning Huyan, Jocelyn Chanussot, Biao Hou, Licheng Jiao. Multi-Relation Attention Network for Image Patch Matching. IEEE Transactions on Image Processing. 2021; 30 ():7127-7142.

Chicago/Turabian Style

Dou Quan; Shuang Wang; Yi Li; Bowu Yang; Ning Huyan; Jocelyn Chanussot; Biao Hou; Licheng Jiao. 2021. "Multi-Relation Attention Network for Image Patch Matching." IEEE Transactions on Image Processing 30, no. : 7127-7142.

Journal article
Published: 02 August 2021 in IEEE Transactions on Geoscience and Remote Sensing
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Deep learning works normally in PolSAR image classification because the complex terrain scattering characteristic results in large intraclass differences and high interclass similarity. Deep metric learning (DML) aims to make the features keep a closer intraclass and a farther interclass distance. Therefore, we introduce DML and then propose an N-cluster generative adversarial net (N-cluster GAN) framework for PolSAR image classification. However, existing DML losses mainly focus on the relationship between individual samples in feature space. Hence, we propose N-cluster loss that pays more attention to the overall structure of all samples. Meanwhile, traditional hard negative sample mining methods occupy lots of computational resources. In addition, the hard level of the negative samples will affect the model's performance. Therefore, we explore a new method based on a GAN framework to replace the sample mining. Positive N-cluster loss is added to the discriminator (D), and a negative one is added to the generator (G). In this way, D will possess better classification ability, and G can produce hard negative samples for D. Then, the hard level of the generated negative samples will change with the discrimination of D, which is appropriate for the proposed model. N-cluster loss can be directly calculated through the extracted features rather than redundant data preparation. The proposed model is verified on four PolSAR datasets from two aspects of the loss function and negative samples mining. Then, it achieves competitive performance compared with state-of-the-art algorithms.

ACS Style

Chen Yang; Biao Hou; Jocelyn Chanussot; Yue Hu; Bo Ren; Shuang Wang; Licheng Jiao. N-Cluster Loss and Hard Sample Generative Deep Metric Learning for PolSAR Image Classification. IEEE Transactions on Geoscience and Remote Sensing 2021, PP, 1 -16.

AMA Style

Chen Yang, Biao Hou, Jocelyn Chanussot, Yue Hu, Bo Ren, Shuang Wang, Licheng Jiao. N-Cluster Loss and Hard Sample Generative Deep Metric Learning for PolSAR Image Classification. IEEE Transactions on Geoscience and Remote Sensing. 2021; PP (99):1-16.

Chicago/Turabian Style

Chen Yang; Biao Hou; Jocelyn Chanussot; Yue Hu; Bo Ren; Shuang Wang; Licheng Jiao. 2021. "N-Cluster Loss and Hard Sample Generative Deep Metric Learning for PolSAR Image Classification." IEEE Transactions on Geoscience and Remote Sensing PP, no. 99: 1-16.

Journal article
Published: 21 July 2021 in IEEE Transactions on Geoscience and Remote Sensing
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Object tracking is an important research direction of space Earth observation in the field of remote sensing. Although the existing correlation filter-based and deep learning (DL)-based object tracking algorithms have achieved great success, they are still unsatisfactory for the problem of object occlusion. The occlusion caused by the complex change in background, and the deviation of the tracking lens, causes object information to go missing, which leads to the omission of detection. Traditionally, most methods for object tracking under occlusion adopt a complex network model, which redetects the occluded object. To address this issue, we propose a novel object tracking approach. First, an action decision-occlusion handling network (AD-OHNet) based on deep reinforcement learning (DRL) is built to achieve low computational complexity for object tracking under occlusion. Second, the temporal and spatial context, the object appearance model, and the motion vector are adopted to provide the occlusion information, which drives actions in reinforcement learning under complete occlusion and contributes to improving the accuracy of tracking while maintaining speed. Finally, the proposed AD-OHNet is evaluated on three remote sensing video datasets of Bogota, Hong Kong, and San Diego taken from Jilin-1 commercial remote sensing satellites. The video datasets all shared problems of low spatial resolution, background clutter, and small objects. Experimental results on the three video datasets validate the effectiveness and efficiency of the proposed tracker.

ACS Style

Yanyu Cui; Biao Hou; Qian Wu; Bo Ren; Shuang Wang; Licheng Jiao. Remote Sensing Object Tracking With Deep Reinforcement Learning Under Occlusion. IEEE Transactions on Geoscience and Remote Sensing 2021, PP, 1 -13.

AMA Style

Yanyu Cui, Biao Hou, Qian Wu, Bo Ren, Shuang Wang, Licheng Jiao. Remote Sensing Object Tracking With Deep Reinforcement Learning Under Occlusion. IEEE Transactions on Geoscience and Remote Sensing. 2021; PP (99):1-13.

Chicago/Turabian Style

Yanyu Cui; Biao Hou; Qian Wu; Bo Ren; Shuang Wang; Licheng Jiao. 2021. "Remote Sensing Object Tracking With Deep Reinforcement Learning Under Occlusion." IEEE Transactions on Geoscience and Remote Sensing PP, no. 99: 1-13.

Journal article
Published: 19 July 2021 in IEEE Transactions on Image Processing
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Recently, deep convolutional neural networks have demonstrated remarkable progresses on single image super-resolution (SR) problem. However, most of them use more deeper and wider networks to improve SR performance, which is not practical in real-world applications due to large complexity, high computation cost, and low efficiency. In addition, they cannot provide high perception quality and guarantee objective quality simultaneously. To address these limitations, we in this paper propose a novel A dversarial M ulti- p ath R esidual N etwork (AMPRN), which can largely suppress the number of network parameters and achieve a higher SR performance compared with the state-of-the-art methods. More specifically, we propose a multi-path residual block (MPRB) for multi-path residual network (MPRN) with fewer network parameters, which can extract abundant local features by fully using features from different paths generated by channel slices. These hierarchical features from all the MPRBs are then jointly aggregated by global gradual feature fusion. Following MPRN, we construct an adversarial gradient network with a gradient loss to make the gradient distribution of the generated SR images and ground truth image closer. In this way, the generated SR images of our model can provide high perception quality and objective quality. Finally, several experimental results demonstrate that our AMPRN achieves better performance in comparison with fewer parameters than the state-of-the-art methods.

ACS Style

Qianqian Wang; Quanxue Gao; Linlu Wu; Gan Sun; Licheng Jiao. Adversarial Multi-Path Residual Network for Image Super-Resolution. IEEE Transactions on Image Processing 2021, 30, 6648 -6658.

AMA Style

Qianqian Wang, Quanxue Gao, Linlu Wu, Gan Sun, Licheng Jiao. Adversarial Multi-Path Residual Network for Image Super-Resolution. IEEE Transactions on Image Processing. 2021; 30 ():6648-6658.

Chicago/Turabian Style

Qianqian Wang; Quanxue Gao; Linlu Wu; Gan Sun; Licheng Jiao. 2021. "Adversarial Multi-Path Residual Network for Image Super-Resolution." IEEE Transactions on Image Processing 30, no. : 6648-6658.

Journal article
Published: 19 July 2021 in IEEE Transactions on Geoscience and Remote Sensing
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For hyperspectral imagery (HSI) classification, most of the studies focus on how to improve the classification accuracy, while the influence of sampling strategy for classification performance attracts little attention. For now, random sampling (RS) is the most adopted strategy. That is, for a hyperspectral image, a certain number of labeled samples are randomly selected as the training set, and the remaining labeled samples are taken as the test set. However, the RS strategy will produce over optimistic results when used for performance evaluation because of the overlap between training set and test set. Though spectral-spatial classification methods benefit most from the RS strategy, the pixel-wise classification methods can also benefit from it because of the high spectral correlation between training and test samples. However, in practical applications, the RS strategy is not feasible. Because the training and test samples are often collected from different locations. In this situation, the correlation between training and test samples will decrease dramatically and the performance of HSI classification methods will be affected. In this article, a nonoverlapped sampling method is adopted to reduce the correlation between training and test samples and different classic classification methods are evaluated. Experimental results show that the classification performance of all methods drops a lot when nonoverlapped sampling strategy is adopted. After the analysis of some important factors for HSI classification, we also propose a cotraining-based classification method to relief the influence of sampling strategy and obtains much better performance compared with those classic spectral-spatial classification methods.

ACS Style

Xianghai Cao; Zuji Liu; Xiangxiang Li; Qian Xiao; Jie Feng; Licheng Jiao. Nonoverlapped Sampling for Hyperspectral Imagery: Performance Evaluation and a Cotraining-Based Classification Strategy. IEEE Transactions on Geoscience and Remote Sensing 2021, PP, 1 -14.

AMA Style

Xianghai Cao, Zuji Liu, Xiangxiang Li, Qian Xiao, Jie Feng, Licheng Jiao. Nonoverlapped Sampling for Hyperspectral Imagery: Performance Evaluation and a Cotraining-Based Classification Strategy. IEEE Transactions on Geoscience and Remote Sensing. 2021; PP (99):1-14.

Chicago/Turabian Style

Xianghai Cao; Zuji Liu; Xiangxiang Li; Qian Xiao; Jie Feng; Licheng Jiao. 2021. "Nonoverlapped Sampling for Hyperspectral Imagery: Performance Evaluation and a Cotraining-Based Classification Strategy." IEEE Transactions on Geoscience and Remote Sensing PP, no. 99: 1-14.

Journal article
Published: 16 July 2021 in IEEE Transactions on Image Processing
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Human-Object Interaction (HOI) Detection is an important task to understand how humans interact with objects. Most of the existing works treat this task as an exhaustive triplet $\left \langle{ human, verb, object }\right \rangle $ classification problem. In this paper, we decompose it and propose a novel two-stage graph model to learn the knowledge of interactiveness and interaction in one network, namely, Interactiveness Proposal Graph Network (IPGN). In the first stage, we design a fully connected graph for learning the interactiveness, which distinguishes whether a pair of human and object is interactive or not. Concretely, it generates the interactiveness features to encode high-level semantic interactiveness knowledge for each pair. The class-agnostic interactiveness is a more general and simpler objective, which can be used to provide reasonable proposals for the graph construction in the second stage. In the second stage, a sparsely connected graph is constructed with all interactive pairs selected by the first stage. Specifically, we use the interactiveness knowledge to guide the message passing. By contrast with the feature similarity, it explicitly represents the connections between the nodes. Benefiting from the valid graph reasoning, the node features are well encoded for interaction learning. Experiments show that the proposed method achieves state-of-the-art performance on both V-COCO and HICO-DET datasets.

ACS Style

Haoran Wang; Licheng Jiao; Fang Liu; Lingling Li; Xu Liu; Deyi Ji; Weihao Gan. IPGN: Interactiveness Proposal Graph Network for Human-Object Interaction Detection. IEEE Transactions on Image Processing 2021, 30, 6583 -6593.

AMA Style

Haoran Wang, Licheng Jiao, Fang Liu, Lingling Li, Xu Liu, Deyi Ji, Weihao Gan. IPGN: Interactiveness Proposal Graph Network for Human-Object Interaction Detection. IEEE Transactions on Image Processing. 2021; 30 ():6583-6593.

Chicago/Turabian Style

Haoran Wang; Licheng Jiao; Fang Liu; Lingling Li; Xu Liu; Deyi Ji; Weihao Gan. 2021. "IPGN: Interactiveness Proposal Graph Network for Human-Object Interaction Detection." IEEE Transactions on Image Processing 30, no. : 6583-6593.

Journal article
Published: 08 July 2021 in IEEE Transactions on Geoscience and Remote Sensing
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Ship detection plays a significant role in the high-resolution remote sensing (HRRS) community, but it is a challenging task due to the complex contents within HRRS images and the diverse orientation of ships. Recently, with the development of deep learning, the performance of the HRRS ship detection model has been improved greatly. Most of them employ deep networks and complicate anchor mechanism to get well ship detection results. Nevertheless, this kind of combination limits the detection efficiency. To address this problem, a new approach named accurate and real-time rotational ship detector (AR²Det) is proposed in this article to detect ships without the anchor mechanism. Based on the extracted features by the feature extraction module (FEM) and the central information of ships, AR²Det adopts two simple modules, ship detector (SDet) and center detector (CDet), to generate and improve the detection results, respectively. AR²Det is efficient due to the simple postprocessing and the lightweight network. Also, AR²Det performs satisfactorily due to the effective generation and enhancement strategy of bounding boxes. The extensive experiments are conducted on a public HRRS image ship detection dataset HRSC2016. The promising results show that our method outperforms the state-of-the-art approaches in terms of both accuracy and speed.

ACS Style

Yuqun Yang; Xu Tang; Yiu-Ming Cheung; Xiangrong Zhang; Fang Liu; Jingjing Ma; Licheng Jiao. AR²Det: An Accurate and Real-Time Rotational One-Stage Ship Detector in Remote Sensing Images. IEEE Transactions on Geoscience and Remote Sensing 2021, PP, 1 -14.

AMA Style

Yuqun Yang, Xu Tang, Yiu-Ming Cheung, Xiangrong Zhang, Fang Liu, Jingjing Ma, Licheng Jiao. AR²Det: An Accurate and Real-Time Rotational One-Stage Ship Detector in Remote Sensing Images. IEEE Transactions on Geoscience and Remote Sensing. 2021; PP (99):1-14.

Chicago/Turabian Style

Yuqun Yang; Xu Tang; Yiu-Ming Cheung; Xiangrong Zhang; Fang Liu; Jingjing Ma; Licheng Jiao. 2021. "AR²Det: An Accurate and Real-Time Rotational One-Stage Ship Detector in Remote Sensing Images." IEEE Transactions on Geoscience and Remote Sensing PP, no. 99: 1-14.

Journal article
Published: 08 July 2021 in IEEE Transactions on Cybernetics
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Attention-based deep multiple-instance learning (MIL) has been applied to many machine-learning tasks with imprecise training labels. It is also appealing in hyperspectral target detection, which only requires the label of an area containing some targets, relaxing the effort of labeling the individual pixel in the scene. This article proposes an L1 sparsity-regularized attention multiple-instance neural network (L1-attention MINN) for hyperspectral target detection with imprecise labels that enforces the discrimination of false-positive instances from positively labeled bags. The sparsity constraint applied to the attention estimated for the positive training bags strictly complies with the definition of MIL and maintains better discriminative ability. The proposed algorithm has been evaluated on both simulated and real-field hyperspectral (subpixel) target detection tasks, where advanced performance has been achieved over the state-of-the-art comparisons, showing the effectiveness of the proposed method for target detection from imprecisely labeled hyperspectral data.

ACS Style

Changzhe Jiao; Chao Chen; Shuiping Gou; Xiuxiu Wang; Bo Yang; Xiaoying Chen; Licheng Jiao. L₁ Sparsity-Regularized Attention Multiple-Instance Network for Hyperspectral Target Detection. IEEE Transactions on Cybernetics 2021, PP, 1 -14.

AMA Style

Changzhe Jiao, Chao Chen, Shuiping Gou, Xiuxiu Wang, Bo Yang, Xiaoying Chen, Licheng Jiao. L₁ Sparsity-Regularized Attention Multiple-Instance Network for Hyperspectral Target Detection. IEEE Transactions on Cybernetics. 2021; PP (99):1-14.

Chicago/Turabian Style

Changzhe Jiao; Chao Chen; Shuiping Gou; Xiuxiu Wang; Bo Yang; Xiaoying Chen; Licheng Jiao. 2021. "L₁ Sparsity-Regularized Attention Multiple-Instance Network for Hyperspectral Target Detection." IEEE Transactions on Cybernetics PP, no. 99: 1-14.

Journal article
Published: 01 July 2021 in Remote Sensing
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Ship detection is a challenging task for synthetic aperture radar (SAR) images. Ships have arbitrary directionality and multiple scales in SAR images. Furthermore, there is a lot of clutter near the ships. Traditional detection algorithms are not robust to these situations and easily cause redundancy in the detection area. With the continuous improvement in resolution, the traditional algorithms cannot achieve high-precision ship detection in SAR images. An increasing number of deep learning algorithms have been applied to SAR ship detection. In this study, a new ship detection network, known as the instance segmentation assisted ship detection network (ISASDNet), is presented. ISASDNet is a two-stage detection network with two branches. A branch is called an object branch and can extract object-level information to obtain positioning bounding boxes and classification results. Another branch called the pixel branch can be utilized for instance segmentation. In the pixel branch, the designed global relational inference layer maps the features to interaction space to learn the relationship between ship and background. The global reasoning module (GRM) based on global relational inference layers can better extract the instance segmentation results of ships. A mask assisted ship detection module (MASDM) is behind the two branches. The MASDM can improve detection results by interacting with the outputs of the two branches. In addition, a strategy is designed to extract the mask of SAR ships, which enables ISASDNet to perform object detection training and instance segmentation training at the same time. Experiments carried out two different datasets demonstrated the superiority of ISASDNet over other networks.

ACS Style

Zitong Wu; Biao Hou; Bo Ren; Zhongle Ren; Shuang Wang; Licheng Jiao. A Deep Detection Network Based on Interaction of Instance Segmentation and Object Detection for SAR Images. Remote Sensing 2021, 13, 2582 .

AMA Style

Zitong Wu, Biao Hou, Bo Ren, Zhongle Ren, Shuang Wang, Licheng Jiao. A Deep Detection Network Based on Interaction of Instance Segmentation and Object Detection for SAR Images. Remote Sensing. 2021; 13 (13):2582.

Chicago/Turabian Style

Zitong Wu; Biao Hou; Bo Ren; Zhongle Ren; Shuang Wang; Licheng Jiao. 2021. "A Deep Detection Network Based on Interaction of Instance Segmentation and Object Detection for SAR Images." Remote Sensing 13, no. 13: 2582.

Journal article
Published: 24 June 2021 in Remote Sensing
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Object detection in remote sensing images has been widely used in military and civilian fields and is a challenging task due to the complex background, large-scale variation, and dense arrangement in arbitrary orientations of objects. In addition, existing object detection methods rely on the increasingly deeper network, which increases a lot of computational overhead and parameters, and is unfavorable to deployment on the edge devices. In this paper, we proposed a lightweight keypoint-based oriented object detector for remote sensing images. First, we propose a semantic transfer block (STB) when merging shallow and deep features, which reduces noise and restores the semantic information. Then, the proposed adaptive Gaussian kernel (AGK) is adapted to objects of different scales, and further improves detection performance. Finally, we propose the distillation loss associated with object detection to obtain a lightweight student network. Experiments on the HRSC2016 and UCAS-AOD datasets show that the proposed method adapts to different scale objects, obtains accurate bounding boxes, and reduces the influence of complex backgrounds. The comparison with mainstream methods proves that our method has comparable performance under lightweight.

ACS Style

Yangyang Li; Heting Mao; Ruijiao Liu; Xuan Pei; Licheng Jiao; Ronghua Shang. A Lightweight Keypoint-Based Oriented Object Detection of Remote Sensing Images. Remote Sensing 2021, 13, 2459 .

AMA Style

Yangyang Li, Heting Mao, Ruijiao Liu, Xuan Pei, Licheng Jiao, Ronghua Shang. A Lightweight Keypoint-Based Oriented Object Detection of Remote Sensing Images. Remote Sensing. 2021; 13 (13):2459.

Chicago/Turabian Style

Yangyang Li; Heting Mao; Ruijiao Liu; Xuan Pei; Licheng Jiao; Ronghua Shang. 2021. "A Lightweight Keypoint-Based Oriented Object Detection of Remote Sensing Images." Remote Sensing 13, no. 13: 2459.

Journal article
Published: 16 June 2021 in Remote Sensing
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Hyperspectral image unmixing is an important task for remote sensing image processing. It aims at decomposing the mixed pixel of the image to identify a set of constituent materials called endmembers and to obtain their proportions named abundances. Recently, number of algorithms based on sparse nonnegative matrix factorization (NMF) have been widely used in hyperspectral unmixing with good performance. However, these sparse NMF algorithms only consider the correlation characteristics of abundance and usually just take the Euclidean structure of data into account, which can make the extracted endmembers become inaccurate. Therefore, with the aim of addressing this problem, we present a sparse NMF algorithm based on endmember independence and spatial weighted abundance in this paper. Firstly, it is assumed that the extracted endmembers should be independent from each other. Thus, by utilizing the autocorrelation matrix of endmembers, the constraint based on endmember independence is to be constructed in the model. In addition, two spatial weights for abundance by neighborhood pixels and correlation coefficient are proposed to make the estimated abundance smoother so as to further explore the underlying structure of hyperspectral data. The proposed algorithm not only considers the relevant characteristics of endmembers and abundances simultaneously, but also makes full use of the spatial-spectral information in the image, achieving a more desired unmixing performance. The experiment results on several data sets further verify the effectiveness of the proposed algorithm.

ACS Style

Jingyan Zhang; Xiangrong Zhang; Licheng Jiao. Sparse Nonnegative Matrix Factorization for Hyperspectral Unmixing Based on Endmember Independence and Spatial Weighted Abundance. Remote Sensing 2021, 13, 2348 .

AMA Style

Jingyan Zhang, Xiangrong Zhang, Licheng Jiao. Sparse Nonnegative Matrix Factorization for Hyperspectral Unmixing Based on Endmember Independence and Spatial Weighted Abundance. Remote Sensing. 2021; 13 (12):2348.

Chicago/Turabian Style

Jingyan Zhang; Xiangrong Zhang; Licheng Jiao. 2021. "Sparse Nonnegative Matrix Factorization for Hyperspectral Unmixing Based on Endmember Independence and Spatial Weighted Abundance." Remote Sensing 13, no. 12: 2348.

Journal article
Published: 15 June 2021 in IEEE Transactions on Geoscience and Remote Sensing
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Hyperspectral image (HSI) classification is one of the most important tasks in hyperspectral data analysis. Convolutional neural networks (CNN) have been introduced to HSI classification and achieved good performance. In this article, an effective and efficient CNN-based spectral partitioning residual network (SPRN) is proposed for HSI classification. The SPRN splits the input spectral bands into several nonoverlapping continuous subbands and uses cascaded parallel improved residual blocks to extract spectral-spatial features from these subbands, respectively. Finally, the features are fused and fed into a classifier. By equivalently using grouped convolutions, the spectral partition and feature extraction are embedded into an end-to-end network. Experimental results show that the proposed SPRN achieves state-of-the-art performance, meanwhile, with relatively fewer parameters and computational costs. Usually, the CNN takes a patch that contains continuous spatial information as the input and results in a class label of the center pixel. The large size of the input patch includes more spatial information, whereas also introduces interfering pixels that may lead to a degradation of classification accuracies. For that reason, we propose a novel spatial attention module named homogeneous pixel detection module (HPDM). The module alleviates the degradation of performance as the input patch size increases by capturing the homogeneous pixels in the input patch. The module can be integrated into any CNN-based HSI classification framework.

ACS Style

Xiangrong Zhang; Shouwang Shang; Xu Tang; Jie Feng; Licheng Jiao. Spectral Partitioning Residual Network With Spatial Attention Mechanism for Hyperspectral Image Classification. IEEE Transactions on Geoscience and Remote Sensing 2021, PP, 1 -14.

AMA Style

Xiangrong Zhang, Shouwang Shang, Xu Tang, Jie Feng, Licheng Jiao. Spectral Partitioning Residual Network With Spatial Attention Mechanism for Hyperspectral Image Classification. IEEE Transactions on Geoscience and Remote Sensing. 2021; PP (99):1-14.

Chicago/Turabian Style

Xiangrong Zhang; Shouwang Shang; Xu Tang; Jie Feng; Licheng Jiao. 2021. "Spectral Partitioning Residual Network With Spatial Attention Mechanism for Hyperspectral Image Classification." IEEE Transactions on Geoscience and Remote Sensing PP, no. 99: 1-14.

Journal article
Published: 07 June 2021 in Remote Sensing
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SAR image registration is a crucial problem in SAR image processing since the registration results with high precision are conducive to improving the quality of other problems, such as change detection of SAR images. Recently, for most DL-based SAR image registration methods, the problem of SAR image registration has been regarded as a binary classification problem with matching and non-matching categories to construct the training model, where a fixed scale is generally set to capture pair image blocks corresponding to key points to generate the training set, whereas it is known that image blocks with different scales contain different information, which affects the performance of registration. Moreover, the number of key points is not enough to generate a mass of class-balance training samples. Hence, we proposed a new method of SAR image registration that meanwhile utilizes the information of multiple scales to construct the matching models. Specifically, considering that the number of training samples is small, deep forest was employed to train multiple matching models. Moreover, a multi-scale fusion strategy is proposed to integrate the multiple predictions and obtain the best pair matching points between the reference image and the sensed image. Finally, experimental results on four datasets illustrate that the proposed method is better than the compared state-of-the-art methods, and the analyses for different scales also indicate that the fusion of multiple scales is more effective and more robust for SAR image registration than one single fixed scale.

ACS Style

Shasha Mao; Jinyuan Yang; Shuiping Gou; Licheng Jiao; Tao Xiong; Lin Xiong. Multi-Scale Fused SAR Image Registration Based on Deep Forest. Remote Sensing 2021, 13, 2227 .

AMA Style

Shasha Mao, Jinyuan Yang, Shuiping Gou, Licheng Jiao, Tao Xiong, Lin Xiong. Multi-Scale Fused SAR Image Registration Based on Deep Forest. Remote Sensing. 2021; 13 (11):2227.

Chicago/Turabian Style

Shasha Mao; Jinyuan Yang; Shuiping Gou; Licheng Jiao; Tao Xiong; Lin Xiong. 2021. "Multi-Scale Fused SAR Image Registration Based on Deep Forest." Remote Sensing 13, no. 11: 2227.

Journal article
Published: 04 June 2021 in Pattern Recognition
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Traditional pattern recognition problems are usually accomplished through two successive stages of representation and classification, the generalization ability and stability are difficult to guarantee for small samples and category imbalance. For tackling these problems, an unlabeled data-driven representation learning classification (RLC) fused model is constructed by integrating representation learning and classification into one model, rather than simple putting the two stages together. The RLC fused model mainly focuses on interactive iteratively optimizing representation learning and classification in a model, guiding and reinforcing each other. Under the framework of RLC, a deep nonnegative matrix factorization (NMF) is adopted for representation learning by complementing the advantages of NMF and deep learning, and avoiding complex network structure and parameter modulation. The framework is called deep NMF-RLC fusion model, which can achieve good performance for binary classification even the simplest linear regression classifier is used. The model explores useful information embedded in unlabeled data, and is suitable for small training samples and unbalanced classification. The performance of the proposed framework is verified on genetic-based tumor recognition, which contains all three stages of early diagnosis, tumor type recognition and postoperative metastasis. Experiments show that, compared with the published state-of-the-art methods and results, there are significant improvements in classification accuracy, specificity and sensitivity.

ACS Style

Xiaohui Yang; Wenming Wu; Licheng Jiao; Changzhe Jiao; Zhicheng Jiao. A deep fusion framework for unlabeled data-driven tumor recognition. Pattern Recognition 2021, 119, 108066 .

AMA Style

Xiaohui Yang, Wenming Wu, Licheng Jiao, Changzhe Jiao, Zhicheng Jiao. A deep fusion framework for unlabeled data-driven tumor recognition. Pattern Recognition. 2021; 119 ():108066.

Chicago/Turabian Style

Xiaohui Yang; Wenming Wu; Licheng Jiao; Changzhe Jiao; Zhicheng Jiao. 2021. "A deep fusion framework for unlabeled data-driven tumor recognition." Pattern Recognition 119, no. : 108066.

Journal article
Published: 25 May 2021 in IEEE Transactions on Neural Networks and Learning Systems
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Due to the complementary properties of different types of sensors, change detection between heterogeneous images receives increasing attention from researchers. However, change detection cannot be handled by directly comparing two heterogeneous images since they demonstrate different image appearances and statistics. In this article, we propose a deep pyramid feature learning network (DPFL-Net) for change detection, especially between heterogeneous images. DPFL-Net can learn a series of hierarchical features in an unsupervised fashion, containing both spatial details and multiscale contextual information. The learned pyramid features from two input images make unchanged pixels matched exactly and changed ones dissimilar and after transformed into the same space for each scale successively. We further propose fusion blocks to aggregate multiscale difference images (DIs), generating an enhanced DI with strong separability. Based on the enhanced DI, unchanged areas are predicted and used to train DPFL-Net in the next iteration. In this article, pyramid features and unchanged areas are updated alternately, leading to an unsupervised change detection method. In the feature transformation process, local consistency is introduced to constrain the learned pyramid features, modeling the correlations between the neighboring pixels and reducing the false alarms. Experimental results demonstrate that the proposed approach achieves superior or at least comparable results to the existing state-of-the-art change detection methods in both homogeneous and heterogeneous cases.

ACS Style

Meijuan Yang; Licheng Jiao; Fang Liu; Biao Hou; Shuyuan Yang; Meng Jian. DPFL-Nets: Deep Pyramid Feature Learning Networks for Multiscale Change Detection. IEEE Transactions on Neural Networks and Learning Systems 2021, PP, 1 -15.

AMA Style

Meijuan Yang, Licheng Jiao, Fang Liu, Biao Hou, Shuyuan Yang, Meng Jian. DPFL-Nets: Deep Pyramid Feature Learning Networks for Multiscale Change Detection. IEEE Transactions on Neural Networks and Learning Systems. 2021; PP (99):1-15.

Chicago/Turabian Style

Meijuan Yang; Licheng Jiao; Fang Liu; Biao Hou; Shuyuan Yang; Meng Jian. 2021. "DPFL-Nets: Deep Pyramid Feature Learning Networks for Multiscale Change Detection." IEEE Transactions on Neural Networks and Learning Systems PP, no. 99: 1-15.

Journal article
Published: 17 May 2021 in IEEE Transactions on Geoscience and Remote Sensing
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Synthetic aperture radar (SAR) is widely used in the field of modern remote sensing due to its high resolution for a comparatively small antenna. However, there are still some difficulties in the processing of SAR images. In particular, accurate segmentation of small targets and image corners remains an important challenge, as these can easily be lost during conventional image smoothing and denoising methods. To address this, we propose an SAR image segmentation algorithm based on constrained smoothing and hierarchical label correction (CSHLC). First, a Canny algorithm is used to extract the edges of SAR images, and the Gaussian smoothing is performed on SAR images under edge constraints to achieve noise reduction so that the edges of small and big targets are well preserved. Second, a preliminary K-means clustering is conducted on the smoothing results, and then, a Markov random field (MRF) model is used on the clustering results (``original label'' results), iteratively calculating a maximum likelihood set of pixel labels. Finally, through two label correction methods, pixel group counting comparison (PGCC) and gray similarity comparison (GSC), the labels of the MRF output are further checked and corrected to obtain final segmentation results. Compared with seven state-of-the-art algorithms, simulation results on both simulated SAR images and real SAR images show that the proposed CSHLC delivers higher accuracy while better retaining corners and small targets.

ACS Style

Ronghua Shang; Mengmeng Liu; Junkai Lin; Jie Feng; Yangyang Li; Rustam Stolkin; Licheng Jiao. SAR Image Segmentation Based on Constrained Smoothing and Hierarchical Label Correction. IEEE Transactions on Geoscience and Remote Sensing 2021, PP, 1 -16.

AMA Style

Ronghua Shang, Mengmeng Liu, Junkai Lin, Jie Feng, Yangyang Li, Rustam Stolkin, Licheng Jiao. SAR Image Segmentation Based on Constrained Smoothing and Hierarchical Label Correction. IEEE Transactions on Geoscience and Remote Sensing. 2021; PP (99):1-16.

Chicago/Turabian Style

Ronghua Shang; Mengmeng Liu; Junkai Lin; Jie Feng; Yangyang Li; Rustam Stolkin; Licheng Jiao. 2021. "SAR Image Segmentation Based on Constrained Smoothing and Hierarchical Label Correction." IEEE Transactions on Geoscience and Remote Sensing PP, no. 99: 1-16.

Journal article
Published: 17 May 2021 in IEEE Transactions on Geoscience and Remote Sensing
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Convolutional neural network (CNN)-based research has been successfully applied in remote sensing image classification due to its powerful feature representation ability. However, these high-capacity networks bring heavy inference costs and are easily overparameterized, especially for the deep CNNs pretrained on natural image datasets. Network pruning is regarded as a prevalent approach for compressing networks, but most existing research ignores model interpretability while formulating pruning criterion. To address these issues, a network pruning method for remote sensing image classification based on interpretable CNNs is proposed. More specifically, an original interpretable CNN with a predefined pruning ratio is trained at first. The filters, namely channels in the high convolutional layer, are able to learn specific semantic meanings in proportion to the predefined pruning ratio. The filters without interpretability are supposed to be removed. As for other convolutional layers, a sensitivity function is designed to assess the risk of pruning channels for each layer, and furthermore, the pruning ratio for each layer is corrected adaptively. The pruning method based on the proposed sensitivity function is effective and requires little computational costs to search abandoned channels without damaging classification performance. To demonstrate the effectiveness, the proposed method is implemented on different scales of modern CNN models, including VGG-VD and AlexNet. The experimental results, obtained on the UC Merced dataset and NWPU-RESISC45 dataset, prove that our method significantly reduces the inference costs and improves the interpretability of networks.

ACS Style

Xianpeng Guo; Biao Hou; Bo Ren; Zhongle Ren; Licheng Jiao. Network Pruning for Remote Sensing Images Classification Based on Interpretable CNNs. IEEE Transactions on Geoscience and Remote Sensing 2021, PP, 1 -15.

AMA Style

Xianpeng Guo, Biao Hou, Bo Ren, Zhongle Ren, Licheng Jiao. Network Pruning for Remote Sensing Images Classification Based on Interpretable CNNs. IEEE Transactions on Geoscience and Remote Sensing. 2021; PP (99):1-15.

Chicago/Turabian Style

Xianpeng Guo; Biao Hou; Bo Ren; Zhongle Ren; Licheng Jiao. 2021. "Network Pruning for Remote Sensing Images Classification Based on Interpretable CNNs." IEEE Transactions on Geoscience and Remote Sensing PP, no. 99: 1-15.

Journal article
Published: 12 May 2021 in IEEE Geoscience and Remote Sensing Letters
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Hyperspectral anomaly detection is a popular topic in remote sensing image intelligent interpretation. To detect anomaly, many methods for background representation have been proposed. However, the prior information of background and anomaly is not fully explored in these methods. To tackle this issue, we combine low-rank dictionary learning (LRDL) with total variation (TV) constraint for hyperspectral anomaly detection. To be specific, the LRDL is introduced for background representation to explore the low-rank priori of background. Considering the smooth structural characteristic of background in spatial, we introduce the TV constraint on coefficients matrix for better background representation learning. Then the residual part is used to discriminate anomaly. The experiments on three real data sets demonstrate the effectiveness of the proposed method compared with state-of-the-art algorithms in hyperspectral anomaly detection.

ACS Style

Xiaoxiao Ma; Xiangrong Zhang; Ning Huyan; Jing Gu; Xu Tang; Licheng Jiao. Background Representation Learning With Structural Constraint for Hyperspectral Anomaly Detection. IEEE Geoscience and Remote Sensing Letters 2021, PP, 1 -5.

AMA Style

Xiaoxiao Ma, Xiangrong Zhang, Ning Huyan, Jing Gu, Xu Tang, Licheng Jiao. Background Representation Learning With Structural Constraint for Hyperspectral Anomaly Detection. IEEE Geoscience and Remote Sensing Letters. 2021; PP (99):1-5.

Chicago/Turabian Style

Xiaoxiao Ma; Xiangrong Zhang; Ning Huyan; Jing Gu; Xu Tang; Licheng Jiao. 2021. "Background Representation Learning With Structural Constraint for Hyperspectral Anomaly Detection." IEEE Geoscience and Remote Sensing Letters PP, no. 99: 1-5.