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Xiangrong Zhang
School of Artificial Intelligence, Xidian University, Xi'an 710071, China

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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: 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: 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: 17 May 2021 in IEEE Transactions on Geoscience and Remote Sensing
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Polarimetric synthetic aperture radar (PolSAR) image classification is one of the hottest issues in remote sensing, where studies on pixel-level information and relationship are of great significance. In this article, graph convolutional network (GCN) is employed to accomplish this pixel-level task benefiting from its excellent capability in structure exploration and information propagation between different pixels. To reduce the communication burden between various PolSAR pixels and high computational cost for the whole PolSAR image, an adaptive GCN (AdapGCN) consisting of pixel-centered subgraphs is proposed in this article. In the AdapGCN, a data-adaptive kernel and a spatial-adaptive kernel are introduced to, respectively, model data structure and spatial structure for PolSAR image. Moreover, a multiscale learning structure is integrated to further explore complicated relations between pixels. Extensive comparative evaluations validate the superiority of our new AdapGCN model for PolSAR image classification over a wide range of state-of-the-art methods on three challenging benchmarks.

ACS Style

Fang Liu; Jingya Wang; Xu Tang; Jia Liu; Xiangrong Zhang; Liang Xiao. Adaptive Graph Convolutional Network for PolSAR Image Classification. IEEE Transactions on Geoscience and Remote Sensing 2021, PP, 1 -14.

AMA Style

Fang Liu, Jingya Wang, Xu Tang, Jia Liu, Xiangrong Zhang, Liang Xiao. Adaptive Graph Convolutional Network for PolSAR Image Classification. IEEE Transactions on Geoscience and Remote Sensing. 2021; PP (99):1-14.

Chicago/Turabian Style

Fang Liu; Jingya Wang; Xu Tang; Jia Liu; Xiangrong Zhang; Liang Xiao. 2021. "Adaptive Graph Convolutional Network for PolSAR Image Classification." IEEE Transactions on Geoscience and Remote Sensing PP, no. 99: 1-14.

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.

Journal article
Published: 26 February 2021 in IEEE Transactions on Geoscience and Remote Sensing
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Data-based image classification methods, such as convolutional neural networks (CNNs), have achieved state-of-the-art performance. They usually leverage thousands of labeled samples to train the networks but ignore some prior knowledge. However, labeled samples are difficult to be obtained for synthetic aperture radar (SAR) images. Model-based methods are adept at utilizing the prior information of data, while they have to introduce some restrictions or assumptions during the realization of models. Consequently, to develop the advantages of both methods and improve their disadvantages, we propose a hybrid network by coupling the data-based with model-based methods for SAR image scene classification in this article. First, to fully use the prior information of SAR images and large amounts of unlabeled samples, we improve the G⁰-based variational Bayesian inference model (GVBI) and construct a G⁰-based convolutional variational auto-encoder (GCVAE) for unsupervised learning of the distributional characteristics of SAR images. After that, we further extend the GCVAE by combining it with CNN, resulting in a stronger hybrid network to classify SAR images with a few labeled samples. In addition, considering the abundant structural information is crucial for SAR image classification, we design a sketch fitter and two structural constraints on both pixel and sketch spaces to assist the hybrid network to improve its classification performance. Finally, we evaluate the performance of our method on real-SAR images, and the experimental results demonstrate that the proposed framework outperforms related methods on classification while reducing the manual annotation substantially.

ACS Style

Xiaoxue Qian; Fang Liu; Licheng Jiao; Xiangrong Zhang; Puhua Chen; Lingling Li; Jing Gu; Yuanhao Cui. A Hybrid Network With Structural Constraints for SAR Image Scene Classification. IEEE Transactions on Geoscience and Remote Sensing 2021, PP, 1 -17.

AMA Style

Xiaoxue Qian, Fang Liu, Licheng Jiao, Xiangrong Zhang, Puhua Chen, Lingling Li, Jing Gu, Yuanhao Cui. A Hybrid Network With Structural Constraints for SAR Image Scene Classification. IEEE Transactions on Geoscience and Remote Sensing. 2021; PP (99):1-17.

Chicago/Turabian Style

Xiaoxue Qian; Fang Liu; Licheng Jiao; Xiangrong Zhang; Puhua Chen; Lingling Li; Jing Gu; Yuanhao Cui. 2021. "A Hybrid Network With Structural Constraints for SAR Image Scene Classification." IEEE Transactions on Geoscience and Remote Sensing PP, no. 99: 1-17.

Journal article
Published: 19 February 2021 in IEEE Transactions on Geoscience and Remote Sensing
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Band selection is an important step in efficient processing of hyperspectral images (HSIs), which can be seen as the combination of powerful band search technique and effective evaluation criterion. The existing deep-learning-based methods make the network parameters sparse to search the spectral bands using threshold-based functions or regularization terms. These methods may lead to an intractable optimization problem. Furthermore, these methods need to repeatedly train deep networks for evaluating candidate band subsets. In this article, we formalize hyperspectral band selection as a reinforcement learning (RL) problem. Band search is regarded as a sequential decision-making process, where each state in the search space is a feasible band subset. To evaluate each state, a semisupervised convolutional neural network (CNN), called EvaluateNet, is constructed by adding the intraclass compactness constraint of both limited labeled and sufficient unlabeled samples. A simple stochastic band sampling method is designed to train EvaluateNet, making it possible to efficiently evaluate without any fine-tuning. In RL, new reward functions are defined by taking the EvaluateNet and the penalty of repeated selection into account. Finally, advantage actor-critic algorithms are designed to explore in the state space and select the band subset according to the expected accumulated reward. The experimental results on HSI data sets demonstrate the effectiveness and efficiency of the proposed algorithms for hyperspectral band selection.

ACS Style

Jie Feng; Di Li; Jing Gu; Xianghai Cao; Ronghua Shang; Xiangrong Zhang; Licheng Jiao. Deep Reinforcement Learning for Semisupervised Hyperspectral Band Selection. IEEE Transactions on Geoscience and Remote Sensing 2021, PP, 1 -19.

AMA Style

Jie Feng, Di Li, Jing Gu, Xianghai Cao, Ronghua Shang, Xiangrong Zhang, Licheng Jiao. Deep Reinforcement Learning for Semisupervised Hyperspectral Band Selection. IEEE Transactions on Geoscience and Remote Sensing. 2021; PP (99):1-19.

Chicago/Turabian Style

Jie Feng; Di Li; Jing Gu; Xianghai Cao; Ronghua Shang; Xiangrong Zhang; Licheng Jiao. 2021. "Deep Reinforcement Learning for Semisupervised Hyperspectral Band Selection." IEEE Transactions on Geoscience and Remote Sensing PP, no. 99: 1-19.

Journal article
Published: 21 January 2021 in IEEE Transactions on Geoscience and Remote Sensing
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With powerful feature representations, convolutional neural networks (CNNs) have produced tremendous achievements in image classification tasks and, typically, entail millions of labeled samples to train massive parameters. However, the sample labeling of synthetic aperture radar (SAR) images is extremely difficult, especially pixelwise labels, and has, sometimes, required field trips to accomplish labeling. Moreover, the inherent speckle noise may weaken the ability of networks to extract effective features from SAR images. In this article, we address these issues by labeling a few patchwise samples and propose Ridgelet-Nets with speckle reduction regularization for SAR image scene classification by combining deep learning with multiscale geometric analysis and statistical modeling of SAR images. First, we design Ridgelet-Nets with convolutional kernels constructed by ridgelet filters to reduce the training parameters and learn more discriminative features. Then, we embed speckle reduction regularization in the Ridgelet-Nets to restrain the influence of speckle noise and smooth the classification maps, in which the prior information of SAR image statistical modeling is introduced. Finally, we propose an adaptive SAR image scene classification framework based on an extended hierarchical visual semantic model, considering the differences in the structures and spatial relationships of different regions in the SAR images, particularly large-scale and complex scenes. Experimental results on real SAR images demonstrate that the proposed framework can achieve preferable classification performance using very limited labeled samples.

ACS Style

Xiaoxue Qian; Fang Liu; Licheng Jiao; Xiangrong Zhang; Yuwei Guo; Xu Liu; Yuanhao Cui. Ridgelet-Nets With Speckle Reduction Regularization for SAR Image Scene Classification. IEEE Transactions on Geoscience and Remote Sensing 2021, PP, 1 -17.

AMA Style

Xiaoxue Qian, Fang Liu, Licheng Jiao, Xiangrong Zhang, Yuwei Guo, Xu Liu, Yuanhao Cui. Ridgelet-Nets With Speckle Reduction Regularization for SAR Image Scene Classification. IEEE Transactions on Geoscience and Remote Sensing. 2021; PP (99):1-17.

Chicago/Turabian Style

Xiaoxue Qian; Fang Liu; Licheng Jiao; Xiangrong Zhang; Yuwei Guo; Xu Liu; Yuanhao Cui. 2021. "Ridgelet-Nets With Speckle Reduction Regularization for SAR Image Scene Classification." IEEE Transactions on Geoscience and Remote Sensing PP, no. 99: 1-17.

Journal article
Published: 18 January 2021 in IEEE Transactions on Geoscience and Remote Sensing
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Deep learning-based methods have demonstrated significant breakthroughs in the application of hyperspectral image (HSI) classification. However, some challenging issues still exist, such as the overfitting problem caused by the limitation of training size with high-dimensional feature and the efficiency of spectral-spatial (SS) exploitation. Therefore, to efficiently model the relative position of samples within the generative adversarial network (GAN) setting, we proposed a dual-channel SS fusion capsule generative adversarial network (DcCapsGAN) for HSI classification. Dual channels (1-D-CapsGAN and 2-D-CapsGAN) are constructed by integrating the capsule network (CapsNet) with GAN for eliminating the mode collapse and gradient disappearance problem caused by traditional GAN. Meanwhile, octave convolution and multiscale convolution are integrated into the proposed model for further reducing the parameters of the CapsNet and extracting multiscale features. To further boost the classification performance, the SS channel fusion model is constructed to composite and switch the feature information of different channels, thereby facilitating the accuracy and robustness of the whole classification performance. Three commonly used HSI data sets are utilized to investigate the performance of the proposed DcCapsGAN model, and the performance of the experiment demonstrates that the proposed model can efficiently improve the classification accuracy and performance.

ACS Style

Jianing Wang; Siying Guo; Runhu Huang; Linhao Li; Xiangrong Zhang; Licheng Jiao. Dual-Channel Capsule Generation Adversarial Network for Hyperspectral Image Classification. IEEE Transactions on Geoscience and Remote Sensing 2021, PP, 1 -16.

AMA Style

Jianing Wang, Siying Guo, Runhu Huang, Linhao Li, Xiangrong Zhang, Licheng Jiao. Dual-Channel Capsule Generation Adversarial Network for Hyperspectral Image Classification. IEEE Transactions on Geoscience and Remote Sensing. 2021; PP (99):1-16.

Chicago/Turabian Style

Jianing Wang; Siying Guo; Runhu Huang; Linhao Li; Xiangrong Zhang; Licheng Jiao. 2021. "Dual-Channel Capsule Generation Adversarial Network for Hyperspectral Image Classification." IEEE Transactions on Geoscience and Remote Sensing PP, no. 99: 1-16.

Journal article
Published: 14 January 2021 in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
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Remote sensing (RS) image scene classification is an important research topic in the RS community. Recently, due to the strong behavior of convolutional neural network (CNN) in feature representation, the growing number of CNN-based classification methods has been proposed. Although they achieve cracking performance, there is still some room for improvement. First, apart from the global information, the local features are crucial to distinguish the RS images. The existing networks are good at capturing the global features since the CNNs' hierarchical structure and the non-linear fitting capacity. However, the local features are not always emphasized. Second, to obtain satisfactory classification results, the distances of RS images from the same/different classes should be minimized/maximized. Nevertheless, these key points in pattern classification do not get the attention they deserve. To overcome the limitation mentioned above, we propose a new CNN named attention consistent network (ACNet) based on the Siamese network in this paper. First, due to the dual-branch structure of ACNet, the input data is the image pairs that are obtained by the spatial rotation. This helps our model to fully explore the global features from RS images. Second, we introduce different attention techniques to mine the objects' information from RS images comprehensively. Third, considering the influence of the spatial rotation and the similarities between RS images, we develop an attention consistent model to unify the salient regions and impact/separate the RS images from the same/different semantic categories. Finally, the classification results can be obtained using the learned features. Three popular RS scene data sets are selected to validate our ACNet. Compared with some existing networks, the proposed method can achieve better performance. The encouraging results illustrate that ACNet is effective for the RS image scene classification. The source codes of this method can be found in https://github.com/TangXu-Group/Remote-Sensing-Images-Classification/tree/main/GLCnet.

ACS Style

Xu Tang; Qiushuo Ma; Xiangrong Zhang; Fang Liu; Jingjing Ma; Licheng Jiao. Attention Consistent Network for Remote Sensing Scene Classification. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2021, 14, 2030 -2045.

AMA Style

Xu Tang, Qiushuo Ma, Xiangrong Zhang, Fang Liu, Jingjing Ma, Licheng Jiao. Attention Consistent Network for Remote Sensing Scene Classification. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 2021; 14 (99):2030-2045.

Chicago/Turabian Style

Xu Tang; Qiushuo Ma; Xiangrong Zhang; Fang Liu; Jingjing Ma; Licheng Jiao. 2021. "Attention Consistent Network for Remote Sensing Scene Classification." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 14, no. 99: 2030-2045.

Journal article
Published: 13 January 2021 in IEEE Transactions on Geoscience and Remote Sensing
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Anomaly detection of a hyperspectral image without any prior information has attracted much more attention in remote sensing image understanding and interpretation, which aims at determining whether a sample belongs to background or anomaly. Low-rank dictionary learning plays an important role in exploiting the low-rank prior of background for hyperspectral image (HSI) anomaly detection. In this article, the low-rank dictionary learning is introduced to learn a dictionary which can reconstruct the background positively, while anomaly cannot. Considering the high correlation of data especially between the adjacent bands, we resort to spectral-difference low-rank dictionary representation learning for global background modeling which can fully exploit the low-rank prior of background. Then, the residual matrix is used to distinguish anomaly. Different from the existing anomaly detection methods based on dictionary which is constructed or learned in a separated step, our proposed model can simultaneously learn the dictionary and separate anomaly by iterative learning. The experimental results on five real data sets demonstrate the superior performance of the proposed method for hyperspectral anomaly detection compared with other state-of-the-art algorithms.

ACS Style

Xiangrong Zhang; Xiaoxiao Ma; Ning Huyan; Jing Gu; Xu Tang; Licheng Jiao. Spectral-Difference Low-Rank Representation Learning for Hyperspectral Anomaly Detection. IEEE Transactions on Geoscience and Remote Sensing 2021, PP, 1 -14.

AMA Style

Xiangrong Zhang, Xiaoxiao Ma, Ning Huyan, Jing Gu, Xu Tang, Licheng Jiao. Spectral-Difference Low-Rank Representation Learning for Hyperspectral Anomaly Detection. IEEE Transactions on Geoscience and Remote Sensing. 2021; PP (99):1-14.

Chicago/Turabian Style

Xiangrong Zhang; Xiaoxiao Ma; Ning Huyan; Jing Gu; Xu Tang; Licheng Jiao. 2021. "Spectral-Difference Low-Rank Representation Learning for Hyperspectral Anomaly Detection." IEEE Transactions on Geoscience and Remote Sensing PP, no. 99: 1-14.

Journal article
Published: 04 September 2020 in IEEE Transactions on Geoscience and Remote Sensing
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Ship detection is a significant and challenging task in remote sensing. Due to the arbitrary-oriented property and large aspect ratio of ships, most of the existing detectors adopt rotation boxes to represent ships. However, manual-designed rotation anchors are needed in these detectors, which causes multiplied computational cost and inaccurate box regression. To address the abovementioned problems, an anchor-free rotation ship detector, named GRS-Det, is proposed, which mainly consists of a feature extraction network with selective concatenation module (SCM), a rotation Gaussian-Mask model, and a fully convolutional network-based detection module. First, a U-shape network with SCM is used to extract multiscale feature maps. With the help of SCM, the channel unbalance problem between different-level features in feature fusion is solved. Then, a rotation Gaussian-Mask is designed to model the ship based on its geometry characteristics, which aims at solving the mislabeling problem of rotation bounding boxes. Meanwhile, the Gaussian-Mask leverages context information to strengthen the perception of ships. Finally, multiscale feature maps are fed to the detection module for classification and regression of each pixel. Our proposed method, evaluated on ship detection benchmarks, including HRSC2016 and DOTA Ship data sets, achieves state-of-the-art results.

ACS Style

Xiangrong Zhang; Guanchun Wang; Peng Zhu; Tianyang Zhang; Chen Li; Licheng Jiao. GRS-Det: An Anchor-Free Rotation Ship Detector Based on Gaussian-Mask in Remote Sensing Images. IEEE Transactions on Geoscience and Remote Sensing 2020, 59, 3518 -3531.

AMA Style

Xiangrong Zhang, Guanchun Wang, Peng Zhu, Tianyang Zhang, Chen Li, Licheng Jiao. GRS-Det: An Anchor-Free Rotation Ship Detector Based on Gaussian-Mask in Remote Sensing Images. IEEE Transactions on Geoscience and Remote Sensing. 2020; 59 (4):3518-3531.

Chicago/Turabian Style

Xiangrong Zhang; Guanchun Wang; Peng Zhu; Tianyang Zhang; Chen Li; Licheng Jiao. 2020. "GRS-Det: An Anchor-Free Rotation Ship Detector Based on Gaussian-Mask in Remote Sensing Images." IEEE Transactions on Geoscience and Remote Sensing 59, no. 4: 3518-3531.

Journal article
Published: 06 August 2020 in IEEE Transactions on Geoscience and Remote Sensing
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Convolutional neural networks (CNNs) have demo-nstrated outstanding performance on image classification. To classify the hyperspectral images (HSIs), existing CNN-based approaches commonly adopt the architecture using single or several fixed spatial windows as inputs. This kind of architecture may lose contextual information or incorporate heterogeneous information due to the neglect of various land-cover distributions in HSIs. To deal with this problem, a novel attention multibranch CNN method based on adaptive region search (RS-AMCNN) is proposed for HSI classification. In RS-AMCNN, sizes and locations of spatial windows are searched in the nonlocal candidate region adaptively according to sample-specific distribution. These flexible spatial windows are input into several branches of RS-AMCNN. In each branch, convolutional long short-term memories (ConvLSTMs) are merged into CNN from shallow to deep layers, which not only extracts joint spatial-spectral features, but also exploits complementary information among different layers. Then, a branch attention mechanism is devised to emphasize more discriminative branches and suppress less useful ones. It forces RS-AMCNN to extract multiscale and multicontextual attention features for classification. Finally, RS-AMCNN is optimized end-to-end by combining the losses from the ramose classifiers of different branches and the main classifier. Experiments carried on several benchmark HSI data sets demonstrate that RS-AMCNN provides promising classification performance, especially in edge preservation and region uniformity.

ACS Style

Jie Feng; Xiande Wu; Ronghua Shang; Chenhong Sui; Jie Li; Licheng Jiao; Xiangrong Zhang. Attention Multibranch Convolutional Neural Network for Hyperspectral Image Classification Based on Adaptive Region Search. IEEE Transactions on Geoscience and Remote Sensing 2020, 59, 5054 -5070.

AMA Style

Jie Feng, Xiande Wu, Ronghua Shang, Chenhong Sui, Jie Li, Licheng Jiao, Xiangrong Zhang. Attention Multibranch Convolutional Neural Network for Hyperspectral Image Classification Based on Adaptive Region Search. IEEE Transactions on Geoscience and Remote Sensing. 2020; 59 (6):5054-5070.

Chicago/Turabian Style

Jie Feng; Xiande Wu; Ronghua Shang; Chenhong Sui; Jie Li; Licheng Jiao; Xiangrong Zhang. 2020. "Attention Multibranch Convolutional Neural Network for Hyperspectral Image Classification Based on Adaptive Region Search." IEEE Transactions on Geoscience and Remote Sensing 59, no. 6: 5054-5070.

Journal article
Published: 22 July 2020 in Remote Sensing
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Semantic segmentation is an important and challenging task in the aerial image community since it can extract the target level information for understanding the aerial image. As a practical application of aerial image semantic segmentation, building extraction always attracts researchers’ attention as the building is the specific land cover in the aerial images. There are two key points for building extraction from aerial images. One is learning the global and local features to fully describe the buildings with diverse shapes. The other one is mining the multi-scale information to discover the buildings with different resolutions. Taking these two key points into account, we propose a new method named global multi-scale encoder-decoder network (GMEDN) in this paper. Based on the encoder-decoder framework, GMEDN is developed with a local and global encoder and a distilling decoder. The local and global encoder aims at learning the representative features from the aerial images for describing the buildings, while the distilling decoder focuses on exploring the multi-scale information for the final segmentation masks. Combining them together, the building extraction is accomplished in an end-to-end manner. The effectiveness of our method is validated by the experiments counted on two public aerial image datasets. Compared with some existing methods, our model can achieve better performance.

ACS Style

Jingjing Ma; Linlin Wu; Xu Tang; Fang Liu; Xiangrong Zhang; Licheng Jiao. Building Extraction of Aerial Images by a Global and Multi-Scale Encoder-Decoder Network. Remote Sensing 2020, 12, 2350 .

AMA Style

Jingjing Ma, Linlin Wu, Xu Tang, Fang Liu, Xiangrong Zhang, Licheng Jiao. Building Extraction of Aerial Images by a Global and Multi-Scale Encoder-Decoder Network. Remote Sensing. 2020; 12 (15):2350.

Chicago/Turabian Style

Jingjing Ma; Linlin Wu; Xu Tang; Fang Liu; Xiangrong Zhang; Licheng Jiao. 2020. "Building Extraction of Aerial Images by a Global and Multi-Scale Encoder-Decoder Network." Remote Sensing 12, no. 15: 2350.

Journal article
Published: 17 July 2020 in IEEE Transactions on Geoscience and Remote Sensing
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The content-based remote sensing image retrieval (CBRSIR) has attracted increasing attention with the number of remote sensing (RS) images growing explosively. Benefiting from the strong capacity of the deep convolutional neural network (DCNN), the performance of CBRSIR has been improved in recent years. Although great successes have been obtained, learning the RS images' representative features and enhancing the retrieval efficiency for the large-scale CBRSIR tasks are still two challenging problems. In this article, we propose a new CBRSIR method named feature and hash (FAH) learning, which consists of a deep feature learning model (DFLM) and an adversarial hash learning model (AHLM). The DFLM aims at learning the RS images' dense features to guarantee the retrieval precision. In the DFLM, the DCNN and the proposed feature aggregation are integrated to capture the multiscale features. Then, the discrimination of the obtained features can be highlighted by the attention map in the developed attention branch. The AHLM maps the dense features onto the compact hash codes so that the retrieval efficiency can be improved. The AHLM contains a hash learning submodel and an adversarial regularization submodel. In particular, the hash learning submodel learns the real-valued hash codes that are similarity preserved by semantic supervisions. The adversarial regularization submodel regularizes the real-valued hash codes to learn the discrete uniform distribution with possible values 0 and 1. In this way, the hash codes are coding-balanced and the quantization errors are reduced. Encouraging experimental results counted on three public benchmark data sets demonstrate that our FAH can achieve competitive performance in the CBRSIR task compared with many existing hash learning methods.

ACS Style

Chao Liu; Jingjing Ma; Xu Tang; Fang Liu; Xiangrong Zhang; Licheng Jiao. Deep Hash Learning for Remote Sensing Image Retrieval. IEEE Transactions on Geoscience and Remote Sensing 2020, 59, 3420 -3443.

AMA Style

Chao Liu, Jingjing Ma, Xu Tang, Fang Liu, Xiangrong Zhang, Licheng Jiao. Deep Hash Learning for Remote Sensing Image Retrieval. IEEE Transactions on Geoscience and Remote Sensing. 2020; 59 (4):3420-3443.

Chicago/Turabian Style

Chao Liu; Jingjing Ma; Xu Tang; Fang Liu; Xiangrong Zhang; Licheng Jiao. 2020. "Deep Hash Learning for Remote Sensing Image Retrieval." IEEE Transactions on Geoscience and Remote Sensing 59, no. 4: 3420-3443.

Journal article
Published: 14 July 2020 in IEEE Transactions on Geoscience and Remote Sensing
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In recent years, with the development of deep learning (DL), the hyperspectral image (HSI) classification methods based on DL have shown superior performance. Although these DL-based methods have great successes, there is still room to improve their ability to explore spatial-spectral information. In this article, we propose a 3-D octave convolution with the spatial-spectral attention network (3DOC-SSAN) to capture discriminative spatial-spectral features for the classification of HSIs. Especially, we first extend the octave convolution model using 3-D convolution, namely, a 3-D octave convolution model (3D-OCM), in which four 3-D octave convolution blocks are combined to capture spatial-spectral features from HSIs. Not only the spatial information can be mined deeply from the high- and low-frequency aspects but also the spectral information can be taken into account by our 3D-OCM. Second, we introduce two attention models from spatial and spectral dimensions to highlight the important spatial areas and specific spectral bands that consist of significant information for the classification tasks. Finally, in order to integrate spatial and spectral information, we design an information complement model to transmit important information between spatial and spectral attention features. Through the information complement model, the beneficial parts of spatial and spectral attention features for the classification tasks can be fully utilized. Comparing with several existing popular classifiers, our proposed method can achieve competitive performance on four benchmark data sets.

ACS Style

Xu Tang; Fanbo Meng; Xiangrong Zhang; Yiu-Ming Cheung; Jingjing Ma; Fang Liu; Licheng Jiao. Hyperspectral Image Classification Based on 3-D Octave Convolution With Spatial–Spectral Attention Network. IEEE Transactions on Geoscience and Remote Sensing 2020, 59, 2430 -2447.

AMA Style

Xu Tang, Fanbo Meng, Xiangrong Zhang, Yiu-Ming Cheung, Jingjing Ma, Fang Liu, Licheng Jiao. Hyperspectral Image Classification Based on 3-D Octave Convolution With Spatial–Spectral Attention Network. IEEE Transactions on Geoscience and Remote Sensing. 2020; 59 (3):2430-2447.

Chicago/Turabian Style

Xu Tang; Fanbo Meng; Xiangrong Zhang; Yiu-Ming Cheung; Jingjing Ma; Fang Liu; Licheng Jiao. 2020. "Hyperspectral Image Classification Based on 3-D Octave Convolution With Spatial–Spectral Attention Network." IEEE Transactions on Geoscience and Remote Sensing 59, no. 3: 2430-2447.

Journal article
Published: 29 June 2020 in IEEE Transactions on Cybernetics
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Band selection has been widely utilized in hyperspectral image (HSI) classification to reduce the dimensionality of HSIs. Recently, deep-learning-based band selection has become of great interest. However, existing deep-learning-based methods usually implement band selection and classification in isolation, or evaluate selected spectral bands by training the deep network repeatedly, which may lead to the loss of discriminative bands and increased computational cost. In this article, a novel convolutional neural network (CNN) based on bandwise-independent convolution and hard thresholding (BHCNN) is proposed, which combines band selection, feature extraction, and classification into an end-to-end trainable network. In BHCNN, a band selection layer is constructed by designing bandwise 1x 1 convolutions, which perform for each spectral band of input HSIs independently. Then, hard thresholding is utilized to constrain the weights of convolution kernels with unselected spectral bands to zero. In this case, these weights are difficult to update. To optimize these weights, the straight-through estimator (STE) is devised by approximating the gradient. Furthermore, a novel coarse-to-fine loss calculated by full and selected spectral bands is defined to improve the interpretability of STE. In the subsequent layers of BHCNN, multiscale 3-D dilated convolutions are constructed to extract joint spatial-spectral features from HSIs with selected spectral bands. The experimental results on several HSI datasets demonstrate that the proposed method uses selected spectral bands to achieve more encouraging classification performance than current state-of-the-art band selection methods.

ACS Style

Jie Feng; Jiantong Chen; Qigong Sun; Ronghua Shang; Xianghai Cao; Xiangrong Zhang; Licheng Jiao. Convolutional Neural Network Based on Bandwise-Independent Convolution and Hard Thresholding for Hyperspectral Band Selection. IEEE Transactions on Cybernetics 2020, 1 -15.

AMA Style

Jie Feng, Jiantong Chen, Qigong Sun, Ronghua Shang, Xianghai Cao, Xiangrong Zhang, Licheng Jiao. Convolutional Neural Network Based on Bandwise-Independent Convolution and Hard Thresholding for Hyperspectral Band Selection. IEEE Transactions on Cybernetics. 2020; (99):1-15.

Chicago/Turabian Style

Jie Feng; Jiantong Chen; Qigong Sun; Ronghua Shang; Xianghai Cao; Xiangrong Zhang; Licheng Jiao. 2020. "Convolutional Neural Network Based on Bandwise-Independent Convolution and Hard Thresholding for Hyperspectral Band Selection." IEEE Transactions on Cybernetics , no. 99: 1-15.

Journal article
Published: 25 May 2020 in Remote Sensing
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Recently, deep learning has been highly successful in image classification. Labeling the PolSAR data, however, is time-consuming and laborious and in response semi-supervised deep learning has been increasingly investigated in PolSAR image classification. Semi-supervised deep learning methods for PolSAR image classification can be broadly divided into two categories, namely pixels-based methods and superpixels-based methods. Pixels-based semi-supervised methods are liable to be affected by speckle noises and have a relatively high computational complexity. Superpixels-based methods focus on the superpixels and ignore tiny detail-preserving represented by pixels. In this paper, a Fuzzy superpixels based Semi-supervised Similarity-constrained CNN (FS-SCNN) is proposed. To reduce the effect of speckle noises and preserve the details, FS-SCNN uses a fuzzy superpixels algorithm to segment an image into two parts, superpixels and undetermined pixels. Moreover, the fuzzy superpixels algorithm can also reduce the number of mixed superpixels and improve classification performance. To exploit unlabeled data effectively, we also propose a Similarity-constrained Convolutional Neural Network (SCNN) model to assign pseudo labels to unlabeled data. The final training set consists of the initial labeled data and these pseudo labeled data. Three PolSAR images are used to demonstrate the excellent classification performance of the FS-SCNN method with data of limited labels.

ACS Style

Yuwei Guo; Zhuangzhuang Sun; Rong Qu; Licheng Jiao; Fang Liu; Xiangrong Zhang. Fuzzy Superpixels Based Semi-Supervised Similarity-Constrained CNN for PolSAR Image Classification. Remote Sensing 2020, 12, 1694 .

AMA Style

Yuwei Guo, Zhuangzhuang Sun, Rong Qu, Licheng Jiao, Fang Liu, Xiangrong Zhang. Fuzzy Superpixels Based Semi-Supervised Similarity-Constrained CNN for PolSAR Image Classification. Remote Sensing. 2020; 12 (10):1694.

Chicago/Turabian Style

Yuwei Guo; Zhuangzhuang Sun; Rong Qu; Licheng Jiao; Fang Liu; Xiangrong Zhang. 2020. "Fuzzy Superpixels Based Semi-Supervised Similarity-Constrained CNN for PolSAR Image Classification." Remote Sensing 12, no. 10: 1694.

Journal article
Published: 21 May 2020 in IEEE Transactions on Cybernetics
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In the information age of big data, and increasingly large and complex networks, there is a growing challenge of understanding how best to restrain the spread of harmful information, for example, a computer virus. Establishing models of propagation and node immunity are important parts of this problem. In this article, a dynamic node immune model, based on the community structure and threshold (NICT), is proposed. First, a network model is established, which regards nodes carrying harmful information as new nodes in the network. The method of establishing the edge between the new node and the original node can be changed according to the needs of different networks. The propagation probability between nodes is determined by using community structure information and a similarity function between nodes. Second, an improved immune gain, based on the propagation probability of the community structure and node similarity, is proposed. The improved immune gain value is calculated for neighbors of the infected node at each time step, and the node is immunized according to the hand-coded parameter: immune threshold. This can effectively prevent invalid or insufficient immunization at each time step. Finally, an evaluation index, considering both the number of immune nodes and the number of infected nodes at each time step, is proposed. The immune effect of nodes can be evaluated more effectively. The results of network immunization experiments, on eight real networks, suggest that the proposed method can deliver better network immunization than several other well-known methods from the literature.

ACS Style

Ronghua Shang; Weitong Zhang; Licheng Jiao; Xiangrong Zhang; Rustam Stolkin. Dynamic Immunization Node Model for Complex Networks Based on Community Structure and Threshold. IEEE Transactions on Cybernetics 2020, 1 -14.

AMA Style

Ronghua Shang, Weitong Zhang, Licheng Jiao, Xiangrong Zhang, Rustam Stolkin. Dynamic Immunization Node Model for Complex Networks Based on Community Structure and Threshold. IEEE Transactions on Cybernetics. 2020; (99):1-14.

Chicago/Turabian Style

Ronghua Shang; Weitong Zhang; Licheng Jiao; Xiangrong Zhang; Rustam Stolkin. 2020. "Dynamic Immunization Node Model for Complex Networks Based on Community Structure and Threshold." IEEE Transactions on Cybernetics , no. 99: 1-14.