scholarly journals Pixel-Wise PolSAR Image Classification via a Novel Complex-Valued Deep Fully Convolutional Network

2019 ◽  
Vol 11 (22) ◽  
pp. 2653 ◽  
Author(s):  
Yice Cao ◽  
Yan Wu ◽  
Peng Zhang ◽  
Wenkai Liang ◽  
Ming Li
Keyword(s):  
Image Classification ◽  
Spatial Information ◽  
Speckle Noise ◽  
Classification Performance ◽  
Feature Maps ◽  
Convolutional Network ◽  
Polarimetric Synthetic Aperture Radar ◽  
Initialization Scheme ◽  
Novel Complex ◽  
Complex Valued

Although complex-valued (CV) neural networks have shown better classification results compared to their real-valued (RV) counterparts for polarimetric synthetic aperture radar (PolSAR) classification, the extension of pixel-level RV networks to the complex domain has not yet thoroughly examined. This paper presents a novel complex-valued deep fully convolutional neural network (CV-FCN) designed for PolSAR image classification. Specifically, CV-FCN uses PolSAR CV data that includes the phase information and uses the deep FCN architecture that performs pixel-level labeling. The CV-FCN architecture is trained in an end-to-end scheme to extract discriminative polarimetric features, and then the entire PolSAR image is classified by the trained CV-FCN. Technically, for the particularity of PolSAR data, a dedicated complex-valued weight initialization scheme is proposed to initialize CV-FCN. It considers the distribution of polarization data to conduct CV-FCN training from scratch in an efficient and fast manner. CV-FCN employs a complex downsampling-then-upsampling scheme to extract dense features. To enrich discriminative information, multi-level CV features that retain more polarization information are extracted via the complex downsampling scheme. Then, a complex upsampling scheme is proposed to predict dense CV labeling. It employs the complex max-unpooling layers to greatly capture more spatial information for better robustness to speckle noise. The complex max-unpooling layers upsample the real and the imaginary parts of complex feature maps based on the max locations maps retained from the complex downsampling scheme. In addition, to achieve faster convergence and obtain more precise classification results, a novel average cross-entropy loss function is derived for CV-FCN optimization. Experiments on real PolSAR datasets demonstrate that CV-FCN achieves better classification performance than other state-of-art methods.

scholarly journals Semi-Supervised PolSAR Image Classification Based on Self-Training and Superpixels

2019 ◽  
Vol 11 (16) ◽  
pp. 1933 ◽  
Author(s):  
Yangyang Li ◽  
Ruoting Xing ◽  
Licheng Jiao ◽  
Yanqiao Chen ◽  
Yingte Chai ◽  
...  
Keyword(s):  
Image Classification ◽  
State Of The Art ◽  
Sample Selection ◽  
Spatial Relations ◽  
Speckle Noise ◽  
Classification Performance ◽  
Selection Strategy ◽  
Training Set ◽  
Polarimetric Synthetic Aperture Radar ◽  
Unlabeled Sample

Polarimetric synthetic aperture radar (PolSAR) image classification is a recent technology with great practical value in the field of remote sensing. However, due to the time-consuming and labor-intensive data collection, there are few labeled datasets available. Furthermore, most available state-of-the-art classification methods heavily suffer from the speckle noise. To solve these problems, in this paper, a novel semi-supervised algorithm based on self-training and superpixels is proposed. First, the Pauli-RGB image is over-segmented into superpixels to obtain a large number of homogeneous areas. Then, features that can mitigate the effects of the speckle noise are obtained using spatial weighting in the same superpixel. Next, the training set is expanded iteratively utilizing a semi-supervised unlabeled sample selection strategy that elaborately makes use of spatial relations provided by superpixels. In addition, a stacked sparse auto-encoder is self-trained using the expanded training set to obtain classification results. Experiments on two typical PolSAR datasets verified its capability of suppressing the speckle noise and showed excellent classification performance with limited labeled data.

scholarly journals Deep Spectral Spatial Inverted Residual Network for Hyperspectral Image Classification

2021 ◽  
Vol 13 (21) ◽  
pp. 4472
Author(s):  
Tianyu Zhang ◽  
Cuiping Shi ◽  
Diling Liao ◽  
Liguo Wang
Keyword(s):  
Image Classification ◽  
Data Augmentation ◽  
Hyperspectral Image ◽  
Classification Performance ◽  
Hyperspectral Data ◽  
Hyperspectral Images ◽  
Hyperspectral Image Classification ◽  
Feature Maps ◽  
Spatial Feature ◽  
Global Context

Convolutional neural networks (CNNs) have been widely used in hyperspectral image classification in recent years. The training of CNNs relies on a large amount of labeled sample data. However, the number of labeled samples of hyperspectral data is relatively small. Moreover, for hyperspectral images, fully extracting spectral and spatial feature information is the key to achieve high classification performance. To solve the above issues, a deep spectral spatial inverted residuals network (DSSIRNet) is proposed. In this network, a data block random erasing strategy is introduced to alleviate the problem of limited labeled samples by data augmentation of small spatial blocks. In addition, a deep inverted residuals (DIR) module for spectral spatial feature extraction is proposed, which locks the effective features of each layer while avoiding network degradation. Furthermore, a global 3D attention module is proposed, which can realize the fine extraction of spectral and spatial global context information under the condition of the same number of input and output feature maps. Experiments are carried out on four commonly used hyperspectral datasets. A large number of experimental results show that compared with some state-of-the-art classification methods, the proposed method can provide higher classification accuracy for hyperspectral images.

scholarly journals Building Extraction from Very High Resolution Aerial Imagery Using Joint Attention Deep Neural Network

2019 ◽  
Vol 11 (24) ◽  
pp. 2970 ◽  
Author(s):  
Ziran Ye ◽  
Yongyong Fu ◽  
Muye Gan ◽  
Jinsong Deng ◽  
Alexis Comber ◽  
...  
Keyword(s):  
Neural Network ◽  
High Resolution ◽  
Spatial Information ◽  
Remote Sensing Data ◽  
Aerial Imagery ◽  
Building Extraction ◽  
Feature Maps ◽  
Convolutional Network ◽  
Wide Range ◽  
Very High

Automated methods to extract buildings from very high resolution (VHR) remote sensing data have many applications in a wide range of fields. Many convolutional neural network (CNN) based methods have been proposed and have achieved significant advances in the building extraction task. In order to refine predictions, a lot of recent approaches fuse features from earlier layers of CNNs to introduce abundant spatial information, which is known as skip connection. However, this strategy of reusing earlier features directly without processing could reduce the performance of the network. To address this problem, we propose a novel fully convolutional network (FCN) that adopts attention based re-weighting to extract buildings from aerial imagery. Specifically, we consider the semantic gap between features from different stages and leverage the attention mechanism to bridge the gap prior to the fusion of features. The inferred attention weights along spatial and channel-wise dimensions make the low level feature maps adaptive to high level feature maps in a target-oriented manner. Experimental results on three publicly available aerial imagery datasets show that the proposed model (RFA-UNet) achieves comparable and improved performance compared to other state-of-the-art models for building extraction.

scholarly journals Variational Generative Adversarial Network with Crossed Spatial and Spectral Interactions for Hyperspectral Image Classification

2021 ◽  
Vol 13 (16) ◽  
pp. 3131
Author(s):  
Zhongwei Li ◽  
Xue Zhu ◽  
Ziqi Xin ◽  
Fangming Guo ◽  
Xingshuai Cui ◽  
...  
Keyword(s):  
Image Classification ◽  
Spatial Information ◽  
Hyperspectral Image ◽  
Classification Performance ◽  
Generative Adversarial Networks ◽  
Hyperspectral Image Classification ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Spatial Features ◽  
Virtual Samples

Variational Autoencoders (VAEs) and Generative Adversarial Networks (GANs) have been widely used in hyperspectral image classification (HSIC) tasks. However, the generated HSI virtual samples by VAEs are often ambiguous, and GANs are prone to the mode collapse, which lead the poor generalization abilities ultimately. Moreover, most of these models only consider the extraction of spectral or spatial features. They fail to combine the two branches interactively and ignore the correlation between them. Consequently, the variational generative adversarial network with crossed spatial and spectral interactions (CSSVGAN) was proposed in this paper, which includes a dual-branch variational Encoder to map spectral and spatial information to different latent spaces, a crossed interactive Generator to improve the quality of generated virtual samples, and a Discriminator stuck with a classifier to enhance the classification performance. Combining these three subnetworks, the proposed CSSVGAN achieves excellent classification by ensuring the diversity and interacting spectral and spatial features in a crossed manner. The superior experimental results on three datasets verify the effectiveness of this method.

scholarly journals PolSAR Image Classification Using a Superpixel-Based Composite Kernel and Elastic Net

2021 ◽  
Vol 13 (3) ◽  
pp. 380
Author(s):  
Yice Cao ◽  
Yan Wu ◽  
Ming Li ◽  
Wenkai Liang ◽  
Peng Zhang
Keyword(s):  
Image Classification ◽  
Feature Fusion ◽  
Classification Performance ◽  
Elastic Net ◽  
Synthetic Aperture ◽  
Polarimetric Synthetic Aperture Radar ◽  
Training Samples ◽  
Composite Kernel ◽  
Limited Training Samples ◽  
State Of Art

The presence of speckles and the absence of discriminative features make it difficult for the pixel-level polarimetric synthetic aperture radar (PolSAR) image classification to achieve more accurate and coherent interpretation results, especially in the case of limited available training samples. To this end, this paper presents a composite kernel-based elastic net classifier (CK-ENC) for better PolSAR image classification. First, based on superpixel segmentation of different scales, three types of features are extracted to consider more discriminative information, thereby effectively suppressing the interference of speckles and achieving better target contour preservation. Then, a composite kernel (CK) is constructed to map these features and effectively implement feature fusion under the kernel framework. The CK exploits the correlation and diversity between different features to improve the representation and discrimination capabilities of features. Finally, an ENC integrated with CK (CK-ENC) is proposed to achieve better PolSAR image classification performance with limited training samples. Experimental results on airborne and spaceborne PolSAR datasets demonstrate that the proposed CK-ENC can achieve better visual coherence and yield higher classification accuracies than other state-of-art methods, especially in the case of limited training samples.

scholarly journals Efficient Attention Mechanism for Dynamic Convolution in Lightweight Neural Network

2021 ◽  
Vol 11 (7) ◽  
pp. 3111
Author(s):  
Enjie Ding ◽  
Yuhao Cheng ◽  
Chengcheng Xiao ◽  
Zhongyu Liu ◽  
Wanli Yu
Keyword(s):  
Neural Networks ◽  
Spatial Information ◽  
Attention Mechanism ◽  
Feature Representation ◽  
Feature Maps ◽  
Convolutional Network ◽  
Dynamic Neural Networks ◽  
Reduction Methods ◽  
Channel Reduction ◽  
Channel Information

Light-weight convolutional neural networks (CNNs) suffer limited feature representation capabilities due to low computational budgets, resulting in degradation in performance. To make CNNs more efficient, dynamic neural networks (DyNet) have been proposed to increase the complexity of the model by using the Squeeze-and-Excitation (SE) module to adaptively obtain the importance of each convolution kernel through the attention mechanism. However, the attention mechanism in the SE network (SENet) selects all channel information for calculations, which brings essential challenges: (a) interference caused by the internal redundant information; and (b) increasing number of network calculations. To address the above problems, this work proposes a dynamic convolutional network (termed as EAM-DyNet) to reduce the number of channels in feature maps by extracting only the useful spatial information. EAM-DyNet first uses the random channel reduction and channel grouping reduction methods to remove the redundancy in the information. As the downsampling of information can lead to the loss of useful information, it then applies an adaptive average pooling method to maintain the information integrity. Extensive experimental results on the baseline demonstrate that EAM-DyNet outperformed the existing approaches, thus it can achieve higher accuracy of the network test and less network parameters.

scholarly journals SUPRAVENTRICULAR TACHYCARDIA CLASSIFICATION USING ATTENTION-BASED RESIDUAL NETWORKS

2021 ◽  
pp. 2140004
Author(s):  
JIAYU ZHANG ◽  
LI QIAN ◽  
XINGYU HOU ◽  
HONGLEI ZHU ◽  
XIAOMEI WU
Keyword(s):  
Spatial Information ◽  
Classification Performance ◽  
Classification Model ◽  
Atrioventricular Nodal Reentrant Tachycardia ◽  
Patient Classification ◽  
Reentrant Tachycardia ◽  
Feature Maps ◽  
Atrioventricular Reentrant Tachycardia ◽  
Ecg Signals ◽  
Electrocardiogram Ecg

Atrioventricular nodal reentrant tachycardia (AVNRT) and atrioventricular reentrant tachycardia (AVRT) are two common arrhythmias with high similarity. Automatic electrocardiogram (ECG) detection using machine learning and neural networks has replaced manual detection, but few studies distinguishing AVNRT from AVRT have been reported. This study proposed a classification algorithm using bottleneck attention module (BAM)-based deep residual network (ResNet) through two-lead ECG records. Specifically, ResNet possessed sufficient network depth to extract abundant features, and BAM was introduced to optimize weight assignment of feature maps by fusing together channel and spatial information. Seven types of ECG signals from four public databases were used to pretrain the proposed classification model, which was then fine-tuned using the experimental dataset. The AVNRT and AVRT detection precisions were 98.95% and 87.47%, sensitivities were 87.52% and 98.58%, and the [Formula: see text]1-scores were 92.82% and 92.68%, respectively. These findings showed that our proposed classification model achieved excellent inter-patient classification performance and can assist doctors in the diagnosis of AVNRT and AVRT.

scholarly journals A Novel Deep Fully Convolutional Network for PolSAR Image Classification

2018 ◽  
Vol 10 (12) ◽  
pp. 1984 ◽  
Author(s):  
Yangyang Li ◽  
Yanqiao Chen ◽  
Guangyuan Liu ◽  
Licheng Jiao
Keyword(s):  
Image Classification ◽  
Sparse Coding ◽  
State Of The Art ◽  
Sliding Window ◽  
Synthetic Aperture ◽  
Prediction Problem ◽  
Convolutional Network ◽  
Polarimetric Synthetic Aperture Radar ◽  
Fully Convolutional Network ◽  
Image Integrity

Polarimetric synthetic aperture radar (PolSAR) image classification has become more and more popular in recent years. As we all know, PolSAR image classification is actually a dense prediction problem. Fortunately, the recently proposed fully convolutional network (FCN) model can be used to solve the dense prediction problem, which means that FCN has great potential in PolSAR image classification. However, there are some problems to be solved in PolSAR image classification by FCN. Therefore, we propose sliding window fully convolutional network and sparse coding (SFCN-SC) for PolSAR image classification. The merit of our method is twofold: (1) Compared with convolutional neural network (CNN), SFCN-SC can avoid repeated calculation and memory occupation; (2) Sparse coding is used to reduce the computation burden and memory occupation, and meanwhile the image integrity can be maintained in the maximum extent. We use three PolSAR images to test the performance of SFCN-SC. Compared with several state-of-the-art methods, SFCN-SC achieves promising results in PolSAR image classification.

scholarly journals Motif-Matching Based Subgraph-Level Attentional Convolutional Network for Graph Classification

2020 ◽  
Vol 34 (04) ◽  
pp. 5387-5394
Author(s):  
Hao Peng ◽  
Jianxin Li ◽  
Qiran Gong ◽  
Yuanxin Ning ◽  
Senzhang Wang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Social Network ◽  
Spatial Information ◽  
Structural Information ◽  
Feature Learning ◽  
Drug Analysis ◽  
Classification Performance ◽  
Learning Approaches ◽  
Graph Classification ◽  
Convolutional Network

Graph classification is critically important to many real-world applications that are associated with graph data such as chemical drug analysis and social network mining. Traditional methods usually require feature engineering to extract the graph features that can help discriminate the graphs of different classes. Although recently deep learning based graph embedding approaches are proposed to automatically learn graph features, they mostly use a few vertex arrangements extracted from the graph for feature learning, which may lose some structural information. In this work, we present a novel motif-based attentional graph convolution neural network for graph classification, which can learn more discriminative and richer graph features. Specifically, a motif-matching guided subgraph normalization method is developed to better preserve the spatial information. A novel subgraph-level self-attention network is also proposed to capture the different impacts or weights of different subgraphs. Experimental results on both bioinformatics and social network datasets show that the proposed models significantly improve graph classification performance over both traditional graph kernel methods and recent deep learning approaches.

scholarly journals Panchromatic Image Super-Resolution Via Self Attention-Augmented Wasserstein Generative Adversarial Network

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 2158
Author(s):  
Juan Du ◽  
Kuanhong Cheng ◽  
Yue Yu ◽  
Dabao Wang ◽  
Huixin Zhou
Keyword(s):  
Large Scale ◽  
Spatial Information ◽  
Super Resolution ◽  
Attention Mechanism ◽  
Feature Representation ◽  
Similarity Function ◽  
Feature Maps ◽  
Generative Adversarial Network ◽  
Convolutional Network ◽  
Adversarial Network

Panchromatic (PAN) images contain abundant spatial information that is useful for earth observation, but always suffer from low-resolution ( LR) due to the sensor limitation and large-scale view field. The current super-resolution (SR) methods based on traditional attention mechanism have shown remarkable advantages but remain imperfect to reconstruct the edge details of SR images. To address this problem, an improved SR model which involves the self-attention augmented Wasserstein generative adversarial network ( SAA-WGAN) is designed to dig out the reference information among multiple features for detail enhancement. We use an encoder-decoder network followed by a fully convolutional network (FCN) as the backbone to extract multi-scale features and reconstruct the High-resolution (HR) results. To exploit the relevance between multi-layer feature maps, we first integrate a convolutional block attention module (CBAM) into each skip-connection of the encoder-decoder subnet, generating weighted maps to enhance both channel-wise and spatial-wise feature representation automatically. Besides, considering that the HR results and LR inputs are highly similar in structure, yet cannot be fully reflected in traditional attention mechanism, we, therefore, designed a self augmented attention (SAA) module, where the attention weights are produced dynamically via a similarity function between hidden features; this design allows the network to flexibly adjust the fraction relevance among multi-layer features and keep the long-range inter information, which is helpful to preserve details. In addition, the pixel-wise loss is combined with perceptual and gradient loss to achieve comprehensive supervision. Experiments on benchmark datasets demonstrate that the proposed method outperforms other SR methods in terms of both objective evaluation and visual effect.

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