This page has only limited features, please log in for full access.

Unclaimed
Hong Huang
Key Laboratory of Optoelectronic Technology and Systems of the Education Ministry of China, Chongqing University, Chongqing 400044, China

Honors and Awards

The user has no records in this section


Career Timeline

The user has no records in this section.


Short Biography

The user biography is not available.
Following
Followers
Co Authors
The list of users this user is following is empty.
Following: 0 users

Feed

Journal article
Published: 31 July 2021 in Expert Systems with Applications
Reads 0
Downloads 0

Recently, convolutional neural networks (CNNs) are successfully applied to extract abstract features of hyperspectral image (HSI), and they obtained competitive performances in HSI classification. However, HSI has inhomogeneous pixels or inherent spectral correlation, and the classification performance of CNN on HSI data will be degraded by modeling all information with equal importance. To address the above issues, we propose an attention mechanism-based method termed multi-level feature network with spectral-spatial attention model (MFNSAM), which consists of a multi-level feature CNN (MFCNN) and a spectral-spatial attention module (SSAM). Due to rich spectral information and spatial distribution in HSI data, MFCNN is employed as multi-scale fusion architecture to bridge the gaps between multi-level features. Specifically, the MFCNN extracts diverse information by compounding the representations generated by each tunnel of multi-scale filter group. To improve the representational capacity in spatial and spectral domains, the channel-wise attention branch is exploited to suppress redundant spectral information, and the spatial-wise attention is designed to explore the contextual information for better refinement. Thus, the SSAM is formed by merging the two branches to adaptively recalibrate the nonlinear interdependence of deep spectral-spatial features. Experiments on University of Pavia, Heihe, and Kennedy Space Center hyperspectral data sets demonstrate that the proposed model provide competitive results to state-of-the-art methods.

ACS Style

Chunyu Pu; Hong Huang; Liping Yang. An attention-driven convolutional neural network-based multi-level spectral-spatial feature learning for hyperspectral image classification. Expert Systems with Applications 2021, 115663 .

AMA Style

Chunyu Pu, Hong Huang, Liping Yang. An attention-driven convolutional neural network-based multi-level spectral-spatial feature learning for hyperspectral image classification. Expert Systems with Applications. 2021; ():115663.

Chicago/Turabian Style

Chunyu Pu; Hong Huang; Liping Yang. 2021. "An attention-driven convolutional neural network-based multi-level spectral-spatial feature learning for hyperspectral image classification." Expert Systems with Applications , no. : 115663.

Journal article
Published: 07 June 2021 in IEEE Transactions on Geoscience and Remote Sensing
Reads 0
Downloads 0

Feature extraction (FE), an important preprocessing step in hyperspectral image (HSI) classification, has received growing attention in the remote sensing community. In recent years, the FE ability of deep learning (DL) methods has been widely recognized. However, most DL models focus on training networks with strong nonlinear mapping ability. They fail to explore the intrinsic manifold structure in HSI, and their performance depends on large size of the labeled training set. To address the above problems, a novel FE approach, termed manifold learning-based semisupervised neural network (MSSNet), was proposed in this article. By introducing the graph embedding (GE) framework, MSSNet develops a semisupervised graph model to explore the manifold structure in HSI with both labeled and unlabeled data. On the basis of this graph model, MSSNet constructs a combined loss function to take into account the metric of difference values and the exploration of manifold margins; thus, it reduces the difference between the predictive value and the actual value to enhance the separability of the features extracted by the network. Experiments conducted on real-world HSI datasets demonstrate that the performance of the proposed MSSNet outperforms some related state-of-the-art FE approaches.

ACS Style

Zhengying Li; Hong Huang; Zhen Zhang; Yinsong Pan. Manifold Learning-Based Semisupervised Neural Network for Hyperspectral Image Classification. IEEE Transactions on Geoscience and Remote Sensing 2021, PP, 1 -12.

AMA Style

Zhengying Li, Hong Huang, Zhen Zhang, Yinsong Pan. Manifold Learning-Based Semisupervised Neural Network for Hyperspectral Image Classification. IEEE Transactions on Geoscience and Remote Sensing. 2021; PP (99):1-12.

Chicago/Turabian Style

Zhengying Li; Hong Huang; Zhen Zhang; Yinsong Pan. 2021. "Manifold Learning-Based Semisupervised Neural Network for Hyperspectral Image Classification." IEEE Transactions on Geoscience and Remote Sensing PP, no. 99: 1-12.

Journal article
Published: 07 May 2021 in IEEE Geoscience and Remote Sensing Letters
Reads 0
Downloads 0

Scene classification of high-resolution images is an active research topic in the remote sensing community. Although convolutional neural network (CNN)-based methods have obtained good performance, large-scale changes of ground objects in complex scenes restrict the further improvement of classification accuracy. In this letter, a global-local dual-branch structure (GLDBS) is designed to explore discriminative features of the original images and the crucial areas, and the strategy of decision-level fusion is applied for performance improvement. To discover the crucial area of the original image, the energy map generated by CNNs is transformed to the binary image, and the coordinates of the maximally connected region can be obtained. Among them, two shallow CNNs, ResNet18 and ResNet34, are selected as the backbone to construct a dual-branch network, and a joint loss is designed to optimize the whole model. In the GLDBS, the two streams employ the same structure (ResNet18-ResNet34) as the backbone, while the parameters are not shared. Experimental results on the aerial image data set (AID) and NWPU-RESISC45 datasets prove that the proposed GLDBS method achieves remarkable classification performance compared with some state-of-the-art (SOTA) methods. The highest overall accuracies (OAs) on the AID and NWPU-RESISC45 datasets are 97.01% and 94.46%, respectively.

ACS Style

Kejie Xu; Hong Huang; Peifang Deng. Remote Sensing Image Scene Classification Based on Global-Local Dual-Branch Structure Model. IEEE Geoscience and Remote Sensing Letters 2021, PP, 1 -5.

AMA Style

Kejie Xu, Hong Huang, Peifang Deng. Remote Sensing Image Scene Classification Based on Global-Local Dual-Branch Structure Model. IEEE Geoscience and Remote Sensing Letters. 2021; PP (99):1-5.

Chicago/Turabian Style

Kejie Xu; Hong Huang; Peifang Deng. 2021. "Remote Sensing Image Scene Classification Based on Global-Local Dual-Branch Structure Model." IEEE Geoscience and Remote Sensing Letters PP, no. 99: 1-5.

Journal article
Published: 15 April 2021 in IEEE Transactions on Neural Networks and Learning Systems
Reads 0
Downloads 0

Scene classification of high spatial resolution (HSR) images can provide data support for many practical applications, such as land planning and utilization, and it has been a crucial research topic in the remote sensing (RS) community. Recently, deep learning methods driven by massive data show the impressive ability of feature learning in the field of HSR scene classification, especially convolutional neural networks (CNNs). Although traditional CNNs achieve good classification results, it is difficult for them to effectively capture potential context relationships. The graphs have powerful capacity to represent the relevance of data, and graph-based deep learning methods can spontaneously learn intrinsic attributes contained in RS images. Inspired by the abovementioned facts, we develop a deep feature aggregation framework driven by graph convolutional network (DFAGCN) for the HSR scene classification. First, the off-the-shelf CNN pretrained on ImageNet is employed to obtain multilayer features. Second, a graph convolutional network-based model is introduced to effectively reveal patch-to-patch correlations of convolutional feature maps, and more refined features can be harvested. Finally, a weighted concatenation method is adopted to integrate multiple features (i.e., multilayer convolutional features and fully connected features) by introducing three weighting coefficients, and then a linear classifier is employed to predict semantic classes of query images. Experimental results performed on the UCM, AID, RSSCN7, and NWPU-RESISC45 data sets demonstrate that the proposed DFAGCN framework obtains more competitive performance than some state-of-the-art methods of scene classification in terms of OAs.

ACS Style

Kejie Xu; Hong Huang; Peifang Deng; Yuan Li. Deep Feature Aggregation Framework Driven by Graph Convolutional Network for Scene Classification in Remote Sensing. IEEE Transactions on Neural Networks and Learning Systems 2021, PP, 1 -15.

AMA Style

Kejie Xu, Hong Huang, Peifang Deng, Yuan Li. Deep Feature Aggregation Framework Driven by Graph Convolutional Network for Scene Classification in Remote Sensing. IEEE Transactions on Neural Networks and Learning Systems. 2021; PP (99):1-15.

Chicago/Turabian Style

Kejie Xu; Hong Huang; Peifang Deng; Yuan Li. 2021. "Deep Feature Aggregation Framework Driven by Graph Convolutional Network for Scene Classification in Remote Sensing." IEEE Transactions on Neural Networks and Learning Systems PP, no. 99: 1-15.

Journal article
Published: 30 March 2021 in IEEE Geoscience and Remote Sensing Letters
Reads 0
Downloads 0

Recently, the convolutional neural network (CNN) has made great progress in hyperspectral image (HSI) classification because of its powerful feature extraction capability. However, the standard CNN based on grid sampling neglects the inherent relation between HSI data, which leads to poor regional edge delineation and generalization ability. Graph convolutional network (GCN) has been successfully applied to data representation in a non-Euclidean space, and it can extract discriminative embedded features by dynamically updating irregular graphs. In this letter, we propose a novel method termed attention mechanism-based dual-path convolutional network (AMDPCN), which is composed of a GCN-based global information learning model (GILM) and a CNN-based local feature extraction network (LFEN). Specifically, AMDPCN fuses the global spatial relationships explored by GILM and the local discriminant features extracted by LFEN with three different strategies: addition, multiplication, and concatenation. Furthermore, a multi-scale attention mechanism (MS-AM) is developed to mitigate the Hughes phenomenon by adaptive recalibrating the nonlinear interdependence among the features. Experiments on Kennedy Space Center and Indian Pines data sets demonstrate the advantages of the proposed AMDPCN to state-of-the-art methods.

ACS Style

Chunyu Pu; Hong Huang; Liuyang Luo. Classfication of Hyperspectral Image With Attention Mechanism-Based Dual-Path Convolutional Network. IEEE Geoscience and Remote Sensing Letters 2021, PP, 1 -5.

AMA Style

Chunyu Pu, Hong Huang, Liuyang Luo. Classfication of Hyperspectral Image With Attention Mechanism-Based Dual-Path Convolutional Network. IEEE Geoscience and Remote Sensing Letters. 2021; PP (99):1-5.

Chicago/Turabian Style

Chunyu Pu; Hong Huang; Liuyang Luo. 2021. "Classfication of Hyperspectral Image With Attention Mechanism-Based Dual-Path Convolutional Network." IEEE Geoscience and Remote Sensing Letters PP, no. 99: 1-5.

Journal article
Published: 21 December 2020 in IEEE Geoscience and Remote Sensing Letters
Reads 0
Downloads 0

Deep learning (DL) has received extensive attention from the remote sensing community in recent years due to its ability to learn deep abstract information through a hierarchical network. However, most DL methods fail to explore the local geometric structure relationship between samples within hyperspectral imagery (HSI) to improve feature extraction performance. To address this issue, a novel DL approach, termed deep manifold reconstruction neural network (DMRNet), is proposed in this letter. By introducing a graph embedding framework, DMRNet calculates a reconstruction point of each sample with corresponding neighbors and then constructs a graph model to discover the intrinsic manifold structure in HSI. On this basis, DMRNet develops a joint loss function to reduce the difference between actual and predictive values, and to explore the separability of the extracted deep features. Experimental results on real-world HSI data sets exhibit the superiority of DMRNet to some state-of-the-art methods.

ACS Style

Zhengying Li; Hong Huang; Zhen Zhang. Deep Manifold Reconstruction Neural Network for Hyperspectral Image Classification. IEEE Geoscience and Remote Sensing Letters 2020, 1 -5.

AMA Style

Zhengying Li, Hong Huang, Zhen Zhang. Deep Manifold Reconstruction Neural Network for Hyperspectral Image Classification. IEEE Geoscience and Remote Sensing Letters. 2020; (99):1-5.

Chicago/Turabian Style

Zhengying Li; Hong Huang; Zhen Zhang. 2020. "Deep Manifold Reconstruction Neural Network for Hyperspectral Image Classification." IEEE Geoscience and Remote Sensing Letters , no. 99: 1-5.

Journal article
Published: 25 August 2020 in IEEE Geoscience and Remote Sensing Letters
Reads 0
Downloads 0

Scene classification is an important research topic in the field of remote sensing (RS), and deep features from convolutional neural networks (CNNs) have shown good classification performance. However, a key issue is how to effectively combine context features for further improving classification accuracy. In this letter, an end-to-end framework termed deep neural network combined with context features (CFDNN) is proposed for scene classification. At first, the pretrained VGG-16 is transferred as feature extractor to obtain convolutional features. Then, two parallel modules, global average pooling (GAP) and long short-term memory (LSTM), are employed to extract global features and context features, respectively. Finally, a weighted concatenation method is introduced to combine the global and context features. As a result, the CFDNN method can adapt high spatial resolution (HSR) images with arbitrary size and obtain satisfactory classification accuracy. The experimental results on the aerial image data set (AID) demonstrate that the proposed CFDNN method has competitive classification performance compared with some state-of-the-art methods.

ACS Style

Peifang Deng; Hong Huang; Kejie Xu. A Deep Neural Network Combined With Context Features for Remote Sensing Scene Classification. IEEE Geoscience and Remote Sensing Letters 2020, PP, 1 -5.

AMA Style

Peifang Deng, Hong Huang, Kejie Xu. A Deep Neural Network Combined With Context Features for Remote Sensing Scene Classification. IEEE Geoscience and Remote Sensing Letters. 2020; PP (99):1-5.

Chicago/Turabian Style

Peifang Deng; Hong Huang; Kejie Xu. 2020. "A Deep Neural Network Combined With Context Features for Remote Sensing Scene Classification." IEEE Geoscience and Remote Sensing Letters PP, no. 99: 1-5.

Journal article
Published: 24 July 2020 in IEEE Geoscience and Remote Sensing Letters
Reads 0
Downloads 0

In this letter, a new semisupervised dimensionality reduction (DR) method, termed geodesic-based manifold joint hypergraphs (GMJHs), is proposed for hyperspectral image (HSI). This method first builds a geodesic-based reconstruction model to discover the nonlinear similarity between two manifold reconstruction neighborhoods. Then, it implies the probabilistic relationship between unlabeled samples and each class via the geodesic-based reconstruction distance. With the probabilistic class relationship, a supervised hypergraph and an unsupervised hypergraph are constructed to represent the multivariate manifold relationship of samples. Finally, the supervised and unsupervised hypergraphs are jointed for learning optimal projection matrix and enhancing the intraclass compactness in low-dimensional embedding space. Experiments on two HSI data sets show that the proposed GMJH algorithm performs better performance than some state-of-the-art DR methods.

ACS Style

Yule Duan; Hong Huang; Yuxiao Tang; Yuan Li; Chunyu Pu. Semisupervised Manifold Joint Hypergraphs for Dimensionality Reduction of Hyperspectral Image. IEEE Geoscience and Remote Sensing Letters 2020, 1 -5.

AMA Style

Yule Duan, Hong Huang, Yuxiao Tang, Yuan Li, Chunyu Pu. Semisupervised Manifold Joint Hypergraphs for Dimensionality Reduction of Hyperspectral Image. IEEE Geoscience and Remote Sensing Letters. 2020; (99):1-5.

Chicago/Turabian Style

Yule Duan; Hong Huang; Yuxiao Tang; Yuan Li; Chunyu Pu. 2020. "Semisupervised Manifold Joint Hypergraphs for Dimensionality Reduction of Hyperspectral Image." IEEE Geoscience and Remote Sensing Letters , no. 99: 1-5.

Journal article
Published: 17 June 2020 in Information Sciences
Reads 0
Downloads 0

Scene classification of high-spatial resolution (HSR) images has a wide range of potential applications in various fields, and it has become a research hotspot in remote sensing community. Recently, deep transfer learning-based methods have attracted tremendous attention due to powerful ability of feature extraction. In this paper, a novel architecture termed two-stream feature aggregation deep neural network (TFADNN) is developed for HSR scene classification. The TFADNN method contains two parallel parts, including the stream of discriminative features and the stream of general features. In the first stream, the fully connected layers of pre-trained CNNs are replaced by a global average pooling layer to remove the limitation on the size of input images. As for the second stream, the multi-scale nonlinear encoding based bag-of-visual-words (MNBoVW) model is proposed to process convolutional features, and the global representations can be obtained. Then, weighted fusion is adopted to integrate two-stream features. As a result, the TFADNN method can learn the discriminative features from HSR images with arbitrary sizes, and the experimental results on two challenging datasets indicate that the TFADNN method achieves satisfactory classification performance compared with some state-of-the-art methods.

ACS Style

Kejie Xu; Hong Huang; Peifang Deng; Guangyao Shi. Two-stream feature aggregation deep neural network for scene classification of remote sensing images. Information Sciences 2020, 539, 250 -268.

AMA Style

Kejie Xu, Hong Huang, Peifang Deng, Guangyao Shi. Two-stream feature aggregation deep neural network for scene classification of remote sensing images. Information Sciences. 2020; 539 ():250-268.

Chicago/Turabian Style

Kejie Xu; Hong Huang; Peifang Deng; Guangyao Shi. 2020. "Two-stream feature aggregation deep neural network for scene classification of remote sensing images." Information Sciences 539, no. : 250-268.

Journal article
Published: 12 June 2020 in Pattern Recognition
Reads 0
Downloads 0

Traditional manifold learning methods generally include a single projection stage that maps high-dimensional data into lower-dimensional space. However, these methods cannot guarantee that the projection matrix is optimal for classification, which limits their practical application. To address this issue, we propose a two-stage projection matrix optimization model termed self-adaptive manifold discriminant analysis (SAMDA). In pre-training projection stage, SAMDA obtains an initial projection matrix by constructing an interclass graph and an intraclass graph under the graph embedding (GE) framework. In weight optimization stage, a maximal manifold margin criterion is developed to further optimize the weights of projection matrix by feature similarity. A self-adaptive optimization process is introduced to increase the margins among different manifolds in low-dimensional space and extract discriminant features that are beneficial to classification. Experimental results on PaviaU, Indian Pines and Heihe data sets demonstrate that the proposed SAMDA method can achieve better classification results than some state-of-the-art methods.

ACS Style

Hong Huang; Zhengying Li; Haibo He; Yule Duan; Song Yang. Self-adaptive manifold discriminant analysis for feature extraction from hyperspectral imagery. Pattern Recognition 2020, 107, 107487 .

AMA Style

Hong Huang, Zhengying Li, Haibo He, Yule Duan, Song Yang. Self-adaptive manifold discriminant analysis for feature extraction from hyperspectral imagery. Pattern Recognition. 2020; 107 ():107487.

Chicago/Turabian Style

Hong Huang; Zhengying Li; Haibo He; Yule Duan; Song Yang. 2020. "Self-adaptive manifold discriminant analysis for feature extraction from hyperspectral imagery." Pattern Recognition 107, no. : 107487.

Journal article
Published: 29 May 2020 in IEEE Transactions on Geoscience and Remote Sensing
Reads 0
Downloads 0

Sparse representation-based graph embedding methods have been successfully applied to dimensionality reduction (DR) in recent years. However, these approaches usually become problematic in the presence of the hyperspectral image (HSI) that contains complex nonlinear manifold structure. Inspired by recent progress in manifold learning and hypergraph framework, a novel DR method named local constraint-based sparse manifold hypergraph learning (LC-SMHL) algorithm is proposed to discover the manifold-based sparse structure and the multivariate discriminant sparse relationship of HSI, simultaneously. The proposed method first designs a new sparse representation (SR) model named local constrained sparse manifold coding (LCSMC) by fusing local constraint and manifold reconstruction. Then, two manifold-based sparse hypergraphs are constructed with sparse coefficients and label information. Based on these hypergraphs, LC-SMHL learns an optimal projection for mapping data into low-dimensional space in which embedding features not only discover the manifold structure and sparse relationship of original data but also possess strong discriminant power for HSI classification. Experimental results on three real HSI data sets demonstrate that the proposed LC-SMHL method achieves better performance in comparison with some state-of-the-art DR methods.

ACS Style

Yule Duan; Hong Huang; Yuxiao Tang. Local Constraint-Based Sparse Manifold Hypergraph Learning for Dimensionality Reduction of Hyperspectral Image. IEEE Transactions on Geoscience and Remote Sensing 2020, 59, 613 -628.

AMA Style

Yule Duan, Hong Huang, Yuxiao Tang. Local Constraint-Based Sparse Manifold Hypergraph Learning for Dimensionality Reduction of Hyperspectral Image. IEEE Transactions on Geoscience and Remote Sensing. 2020; 59 (1):613-628.

Chicago/Turabian Style

Yule Duan; Hong Huang; Yuxiao Tang. 2020. "Local Constraint-Based Sparse Manifold Hypergraph Learning for Dimensionality Reduction of Hyperspectral Image." IEEE Transactions on Geoscience and Remote Sensing 59, no. 1: 613-628.

Articles
Published: 28 May 2020 in Remote Sensing Letters
Reads 0
Downloads 0

In this letter, we proposed a novel deep feature manifold embedding method to improve feature extraction ability of traditional deep learning methods. This method first obtains deep features of hyperspectral image (HSI) from a trained autoencoder. Then, an intrinsic graph and a penalty graph are constructed to discover the discriminant manifold structure of deep features. Finally, the deep features are mapped into a low-dimensional embedding space, in which samples in intraclass manifold are compacted and samples from interclass manifolds are separated. Experiments on Pavia University, Indian Pines and Urban datasets demonstrate that the proposed method effectively improves the classification performance of HSI compared with other state-of-the-art approaches.

ACS Style

Jiamin Liu; Song Yang; Hong Huang; Zhengying Li; Guangyao Shi. A deep feature manifold embedding method for hyperspectral image classification. Remote Sensing Letters 2020, 11, 620 -629.

AMA Style

Jiamin Liu, Song Yang, Hong Huang, Zhengying Li, Guangyao Shi. A deep feature manifold embedding method for hyperspectral image classification. Remote Sensing Letters. 2020; 11 (7):620-629.

Chicago/Turabian Style

Jiamin Liu; Song Yang; Hong Huang; Zhengying Li; Guangyao Shi. 2020. "A deep feature manifold embedding method for hyperspectral image classification." Remote Sensing Letters 11, no. 7: 620-629.

Journal article
Published: 27 May 2020 in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Reads 0
Downloads 0

In this paper, we designed an adaptive residual convolutional neural network (ARCNN) that takes raw hyperspectral image (HSI) cubes as input data for land cover classification. In this network, spectral and spatial feature extraction blocks are explored to learn discriminative features from abundant spectral information and spatial contexts in HSI, respectively. The proposed ARCNN is an end-to-end deep learning framework that alleviates the declining-accuracy phenomenon of deep learning models, and it also ranks the correlation and importance of each band in HSI. Furthermore, the residual blocks connect every other 3-D convolutional layer by using identity mapping, which facilitates the backpropagation of gradients. In order to address the common issue of imbalance between high dimensionality and limited availability of training samples for HSI classification, attention mechanism and feature fusion block are investigated to improve the performance of the ARCNN. Finally, some strategies, batch normalization and dropout are imposed on every convolutional layer to regularize the learning process. Therefore, the ARCNN method brings benefits to extract discriminative features, and it is easier to avoid over-fitting and achieves better performance. Experimental results on three public HSI data sets demonstrate the effectiveness of ARCNN over some state-of-the-art methods.

ACS Style

Hong Huang; Chunyu Pu; Yuan Li; Yule Duan. Adaptive Residual Convolutional Neural Network for Hyperspectral Image Classification. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2020, 13, 2520 -2531.

AMA Style

Hong Huang, Chunyu Pu, Yuan Li, Yule Duan. Adaptive Residual Convolutional Neural Network for Hyperspectral Image Classification. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 2020; 13 (99):2520-2531.

Chicago/Turabian Style

Hong Huang; Chunyu Pu; Yuan Li; Yule Duan. 2020. "Adaptive Residual Convolutional Neural Network for Hyperspectral Image Classification." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 13, no. 99: 2520-2531.

Journal article
Published: 22 May 2020 in Neural Networks
Reads 0
Downloads 0

Deep learning has received increasing attention in recent years and it has been successfully applied for feature extraction (FE) of hyperspectral images. However, most deep learning methods fail to explore the manifold structure in hyperspectral image (HSI). To tackle this issue, a novel graph-based deep learning model, termed deep locality preserving neural network (DLPNet), was proposed in this paper. Traditional deep learning methods use random initialization to initialize network parameters. Different from that, DLPNet initializes each layer of the network by exploring the manifold structure in hyperspectral data. In the stage of network optimization, it designed a deep-manifold learning joint loss function to exploit graph embedding process while measuring the difference between the predictive value and the actual value, then the proposed model can take into account the extraction of deep features and explore the manifold structure of data simultaneously. Experimental results on real-world HSI datasets indicate that the proposed DLPNet performs significantly better than some state-of-the-art methods.

ACS Style

Zhengying Li; Hong Huang; Yule Duan; Guangyao Shi. DLPNet: A deep manifold network for feature extraction of hyperspectral imagery. Neural Networks 2020, 129, 7 -18.

AMA Style

Zhengying Li, Hong Huang, Yule Duan, Guangyao Shi. DLPNet: A deep manifold network for feature extraction of hyperspectral imagery. Neural Networks. 2020; 129 ():7-18.

Chicago/Turabian Style

Zhengying Li; Hong Huang; Yule Duan; Guangyao Shi. 2020. "DLPNet: A deep manifold network for feature extraction of hyperspectral imagery." Neural Networks 129, no. : 7-18.

Journal article
Published: 20 March 2020 in IEEE Transactions on Cybernetics
Reads 0
Downloads 0

Hyperspectral image (HSI) generally contains a complex manifold structure and strong sparse correlation in its nonlinear high-dimensional data space. However, the existing manifold learning and sparse learning methods usually consider the manifold structure and sparse relationship separately rather than combining manifold and sparse properties to discover the intrinsic information in the original data. To simultaneously reveal the complex sparse relation and manifold structure of HSI, a novel feature extraction (FE) method, called local manifold-based sparse discriminant learning (LMSDL), has been proposed on the basis of manifold learning and sparse representation (SR). The LMSDL method first designs a new sparse optimization model called local manifold-based SR (LMSR) to reveal the local manifold-based sparse structure of data. Then, two geometrical sparse graphs are constructed to represent the discriminant relationship between samples and the geometrical and sparse neighbors. An objective function is constructed via geometrical sparse graphs and reconstruction points to learn a projection matrix for FE. The LMSDL effectively reveals the complex sparse relation and manifold structure in high-dimensional data, and it enhances the representation ability of extracted features for HSI classification significantly. The experimental results on the three real HSI datasets show that the proposed LMSDL algorithm possesses better performance in comparison with some state-of-the-art FE methods.

ACS Style

Yule Duan; Hong Huang; Zhengying Li; Yuxiao Tang. Local Manifold-Based Sparse Discriminant Learning for Feature Extraction of Hyperspectral Image. IEEE Transactions on Cybernetics 2020, 51, 4021 -4034.

AMA Style

Yule Duan, Hong Huang, Zhengying Li, Yuxiao Tang. Local Manifold-Based Sparse Discriminant Learning for Feature Extraction of Hyperspectral Image. IEEE Transactions on Cybernetics. 2020; 51 (8):4021-4034.

Chicago/Turabian Style

Yule Duan; Hong Huang; Zhengying Li; Yuxiao Tang. 2020. "Local Manifold-Based Sparse Discriminant Learning for Feature Extraction of Hyperspectral Image." IEEE Transactions on Cybernetics 51, no. 8: 4021-4034.

Journal article
Published: 12 February 2020 in IEEE Access
Reads 0
Downloads 0

In this paper, a MDSPF method is proposed to learn a robust observation model for representing the targets by training a CNN with a number of video sequences. The CNN architecture is composed of three shared convolutional units, two shared fully connected (Fc) units and a multiple domain Fc unit, and it is offline trained by a multi-domain learning strategy. After training, the shared convolutional units are remained as an observation model for our tracking framework. The features from the shared convolutional units can well adapt to the challenges in tracking tasks. A scale-adaptive particle filter is also proposed in our framework to improve the robustness of particle filter method. Different from most existing particle filter tackers, it can efficiently shepherd each particle towards a more precise location and scale through similarity evaluation. Extensive experiments are conducted on Object Tracking Benchmark (OTB), UAV123 and LaSOT datasets to verify the efficiency of our proposed method.

ACS Style

Yiming Tang; Yufei Liu; Hong Huang; Jiamin Liu; Wenjie Xie. A Scale-Adaptive Particle Filter Tracking Algorithm Based on Offline Trained Multi-Domain Deep Network. IEEE Access 2020, 8, 31970 -31982.

AMA Style

Yiming Tang, Yufei Liu, Hong Huang, Jiamin Liu, Wenjie Xie. A Scale-Adaptive Particle Filter Tracking Algorithm Based on Offline Trained Multi-Domain Deep Network. IEEE Access. 2020; 8 (99):31970-31982.

Chicago/Turabian Style

Yiming Tang; Yufei Liu; Hong Huang; Jiamin Liu; Wenjie Xie. 2020. "A Scale-Adaptive Particle Filter Tracking Algorithm Based on Offline Trained Multi-Domain Deep Network." IEEE Access 8, no. 99: 31970-31982.

Journal article
Published: 11 January 2020 in Neurocomputing
Reads 0
Downloads 0

Manifold learning has been successfully applied to hyperspectral image (HSI) classification by modeling different land covers as a smooth manifold embedded in a high-dimensional space. However, traditional manifold learning algorithms were proposed with the assumption of single manifold structure in HSI, while the samples in different subsets may belong to different sub-manifolds. In this paper, a novel dimensionality reduction (DR) method called multi-manifold locality graph preserving analysis (MLGPA) was proposed for feature learning of HSI data. According to the label information of HSI, MLGPA divides the samples data into different subsets, and each subset is treated as a sub-manifold. Then, it constructs a within-manifold graph and a between-manifold graph for each sub-manifold to characterize within-manifold compactness and between-manifold separability, and a discriminant projection matrix can be obtained by maximizing the between-manifold scatter and minimizing the within-manifold scatter simultaneously. Finally, low-dimensional embedding features of different sub-manifolds are fused to improve the classification performance. MLGPA can effectively reveal the multi-manifold structure and improve the classification performance of HSI. Experimental results on three real-world HSI data sets demonstrate that MLGPA is superior to some state-of-the-art methods in terms of classification accuracy.

ACS Style

Guangyao Shi; Hong Huang; Zhengying Li; Yule Duan. Multi-manifold locality graph preserving analysis for hyperspectral image classification. Neurocomputing 2020, 388, 45 -59.

AMA Style

Guangyao Shi, Hong Huang, Zhengying Li, Yule Duan. Multi-manifold locality graph preserving analysis for hyperspectral image classification. Neurocomputing. 2020; 388 ():45-59.

Chicago/Turabian Style

Guangyao Shi; Hong Huang; Zhengying Li; Yule Duan. 2020. "Multi-manifold locality graph preserving analysis for hyperspectral image classification." Neurocomputing 388, no. : 45-59.

Journal article
Published: 06 January 2020 in IEEE Access
Reads 0
Downloads 0

The traditional classification algorithms have been widely applied for hyperspectral imagery (HSI), but many methods exploit spectral or spatial information in HSI data that doesn’t make full use of the information of HSI data. To solve this problem, a new classification method, termed multiple characteristics similarity metric (MCSM), was proposed in this paper for HSI classification. In MCSM, a spatial similarity probability relationship, a sparse similarity relationship and a collaborative similarity relationship are integrated to use the multiple information of HSI. At first, a spatial similarity probability is designed by the multiple features fusion, which can reveal the spatial information of HSI. Then, it utilizes sparse representation and collaborative representation to represent the sparse and collaborative properties of HSI. Finally, the class label can be determined by combining spatial similarity probability, sparse similarity and collaborative similarity. To demonstrate the effectiveness of the proposed method, experiments have been conducted on the Indian Pines and Pavia University data sets. The experimental results show that MCSM achieves better classification performance compared with some state-of-the-art classification methods, which indicates that MCSM can make full use of the multiple information of HSI to form the complementarity of different characteristics and enhance the discriminant performance for HSI classification.

ACS Style

Jiamin Liu; Limei Zhang; Hong Huang; Chao Zheng; Song Yang. Multiple Characteristics Similarity Metric Method for Hyperspectral Image Classification. IEEE Access 2020, 8, 9501 -9512.

AMA Style

Jiamin Liu, Limei Zhang, Hong Huang, Chao Zheng, Song Yang. Multiple Characteristics Similarity Metric Method for Hyperspectral Image Classification. IEEE Access. 2020; 8 (99):9501-9512.

Chicago/Turabian Style

Jiamin Liu; Limei Zhang; Hong Huang; Chao Zheng; Song Yang. 2020. "Multiple Characteristics Similarity Metric Method for Hyperspectral Image Classification." IEEE Access 8, no. 99: 9501-9512.

Journal article
Published: 01 January 2020 in IEEE Geoscience and Remote Sensing Letters
Reads 0
Downloads 0

The scene classification of high spatial resolution (HSR) images is a challenging task in the remote sensing community. How to construct a discriminative representation of the HSR scene is a key step to improve classification performance. In this letter, we propose a novel feature extraction method termed multilayer feature fusion network (MF²Net) for scene classification. At first, the transferred VGGNet-16 model is employed as a feature extractor to acquire multilayer convolutional features. Then, several layers including pooling, transformation, and fusion layers are designed to process hierarchical features in four branches, and the prediction probability can be obtained for classification. Finally, the proposed model is optimized by fine-tuning techniques, where a novel data augmentation approach is explored to improve generalization ability. As a result, MF²Net effectively applies useful information from multilayers to improve the accuracy of scene classification. The experimental results on AID and NWPU-RESISC45 data sets exhibit that the MF²Net method obtains quite competitive classification results compared with many state-of-the-art methods.

ACS Style

Kejie Xu; Hong Huang; Yuan Li; Guangyao Shi. Multilayer Feature Fusion Network for Scene Classification in Remote Sensing. IEEE Geoscience and Remote Sensing Letters 2020, 17, 1894 -1898.

AMA Style

Kejie Xu, Hong Huang, Yuan Li, Guangyao Shi. Multilayer Feature Fusion Network for Scene Classification in Remote Sensing. IEEE Geoscience and Remote Sensing Letters. 2020; 17 (11):1894-1898.

Chicago/Turabian Style

Kejie Xu; Hong Huang; Yuan Li; Guangyao Shi. 2020. "Multilayer Feature Fusion Network for Scene Classification in Remote Sensing." IEEE Geoscience and Remote Sensing Letters 17, no. 11: 1894-1898.

Journal article
Published: 23 November 2019 in Expert Systems with Applications
Reads 0
Downloads 0

Feature extraction (FE) is an effective method for learning discriminant features from hyperspectral image (HSI). Recently, graph embedding (GE) framework has been widely applied in FE of HSI data. GE unifies many classical FE methods and explores the low-dimensional embedding of high-dimensional data by a projection matrix generated from undirected weighted graphs. However, GE is unable to adaptively optimize projection matrix due to the absence of an iterative strategy in a single mapping process. To address this issue, a unified optimization method termed manifold-based maximization margin discriminant network (M3DNet) was proposed to improve the performance of traditional FE methods. In M3DNet, an initial projection matrix is obtained from original FE method, and then a maximal manifold margin criterion (M3C) is proposed to maximize the margins among different classes, which enhances the discriminative ability of embedding features. After that, an iterative strategy is designed to optimize the projection matrix. Experiments on real-world HSI data sets indicate that the proposed M3DNet performs significantly better than some state-of-the-art methods.

ACS Style

Zhengying Li; Hong Huang; Yuan Li; Yinsong Pan. M3DNet: A manifold-based discriminant feature learning network for hyperspectral imagery. Expert Systems with Applications 2019, 144, 113089 .

AMA Style

Zhengying Li, Hong Huang, Yuan Li, Yinsong Pan. M3DNet: A manifold-based discriminant feature learning network for hyperspectral imagery. Expert Systems with Applications. 2019; 144 ():113089.

Chicago/Turabian Style

Zhengying Li; Hong Huang; Yuan Li; Yinsong Pan. 2019. "M3DNet: A manifold-based discriminant feature learning network for hyperspectral imagery." Expert Systems with Applications 144, no. : 113089.