scholarly journals Fully Dense Multiscale Fusion Network for Hyperspectral Image Classification

2019 ◽  
Vol 11 (22) ◽  
pp. 2718 ◽  
Author(s):  
Zhe Meng ◽  
Lingling Li ◽  
Licheng Jiao ◽  
Zhixi Feng ◽  
Xu Tang ◽  
...  
Keyword(s):  
Hyperspectral Image ◽  
Multiple Scales ◽  
Feature Learning ◽  
Classification Performance ◽  
Connectivity Pattern ◽  
Great Success ◽  
Feature Representations ◽  
Spatial Features ◽  
Discriminative Feature ◽  
Correlated Information

The convolutional neural network (CNN) can automatically extract hierarchical feature representations from raw data and has recently achieved great success in the classification of hyperspectral images (HSIs). However, most CNN based methods used in HSI classification neglect adequately utilizing the strong complementary yet correlated information from each convolutional layer and only employ the last convolutional layer features for classification. In this paper, we propose a novel fully dense multiscale fusion network (FDMFN) that takes full advantage of the hierarchical features from all the convolutional layers for HSI classification. In the proposed network, shortcut connections are introduced between any two layers in a feed-forward manner, enabling features learned by each layer to be accessed by all subsequent layers. This fully dense connectivity pattern achieves comprehensive feature reuse and enforces discriminative feature learning. In addition, various spectral-spatial features with multiple scales from all convolutional layers are fused to extract more discriminative features for HSI classification. Experimental results on three widely used hyperspectral scenes demonstrate that the proposed FDMFN can achieve better classification performance in comparison with several state-of-the-art approaches.

scholarly journals Part-Aware Mask-Guided Attention for Thorax Disease Classification

Entropy ◽  
2021 ◽  
Vol 23 (6) ◽  
pp. 653
Author(s):  
Ruihua Zhang ◽  
Fan Yang ◽  
Yan Luo ◽  
Jianyi Liu ◽  
Jinbin Li ◽  
...  
Keyword(s):  
Visual Cues ◽  
Learning Strategy ◽  
Feature Learning ◽  
Classification Performance ◽  
Disease Classification ◽  
Feature Representations ◽  
Chest X Ray ◽  
Global And Local ◽  
Aided Diagnosis ◽  
Single Organ

Thorax disease classification is a challenging task due to complex pathologies and subtle texture changes, etc. It has been extensively studied for years largely because of its wide application in computer-aided diagnosis. Most existing methods directly learn global feature representations from whole Chest X-ray (CXR) images, without considering in depth the richer visual cues lying around informative local regions. Thus, these methods often produce sub-optimal thorax disease classification performance because they ignore the very informative pathological changes around organs. In this paper, we propose a novel Part-Aware Mask-Guided Attention Network (PMGAN) that learns complementary global and local feature representations from all-organ region and multiple single-organ regions simultaneously for thorax disease classification. Specifically, multiple innovative soft attention modules are designed to progressively guide feature learning toward the global informative regions of whole CXR image. A mask-guided attention module is designed to further search for informative regions and visual cues within the all-organ or single-organ images, where attention is elegantly regularized by automatically generated organ masks and without introducing computation during the inference stage. In addition, a multi-task learning strategy is designed, which effectively maximizes the learning of complementary local and global representations. The proposed PMGAN has been evaluated on the ChestX-ray14 dataset and the experimental results demonstrate its superior thorax disease classification performance against the state-of-the-art methods.

scholarly journals Deep Discriminative Representation Learning with Attention Map for Scene Classification

2020 ◽  
Vol 12 (9) ◽  
pp. 1366 ◽  
Author(s):  
Jun Li ◽  
Daoyu Lin ◽  
Yang Wang ◽  
Guangluan Xu ◽  
Yunyan Zhang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Feature Fusion ◽  
Representation Learning ◽  
Classification Performance ◽  
Great Success ◽  
Scene Classification ◽  
Remote Sensing Images ◽  
Discriminative Ability ◽  
Feature Representations ◽  
Benchmark Datasets

In recent years, convolutional neural networks (CNNs) have shown great success in the scene classification of computer vision images. Although these CNNs can achieve excellent classification accuracy, the discriminative ability of feature representations extracted from CNNs is still limited in distinguishing more complex remote sensing images. Therefore, we propose a unified feature fusion framework based on attention mechanism in this paper, which is called Deep Discriminative Representation Learning with Attention Map (DDRL-AM). Firstly, by applying Gradient-weighted Class Activation Mapping (Grad-CAM) algorithm, attention maps associated with the predicted results are generated in order to make CNNs focus on the most salient parts of the image. Secondly, a spatial feature transformer (SFT) is designed to extract discriminative features from attention maps. Then an innovative two-channel CNN architecture is proposed by the fusion of features extracted from attention maps and the RGB (red green blue) stream. A new objective function that considers both center and cross-entropy loss are optimized to decrease the influence of inter-class dispersion and within-class variance. In order to show its effectiveness in classifying remote sensing images, the proposed DDRL-AM method is evaluated on four public benchmark datasets. The experimental results demonstrate the competitive scene classification performance of the DDRL-AM approach. Moreover, the visualization of features extracted by the proposed DDRL-AM method can prove that the discriminative ability of features has been increased.

scholarly journals A 3D-2D Multibranch Feature Fusion and Dense Attention Network for Hyperspectral Image Classification

Micromachines ◽  
2021 ◽  
Vol 12 (10) ◽  
pp. 1271
Author(s):  
Hongmin Gao ◽  
Yiyan Zhang ◽  
Yunfei Zhang ◽  
Zhonghao Chen ◽  
Chenming Li ◽  
...  
Keyword(s):  
Image Classification ◽  
Hyperspectral Image ◽  
Feature Fusion ◽  
Receptive Fields ◽  
Classification Performance ◽  
Redundant Information ◽  
Hyperspectral Image Classification ◽  
Training Process ◽  
Attention Network ◽  
Spatial Features

In recent years, hyperspectral image classification (HSI) has attracted considerable attention. Various methods based on convolution neural networks have achieved outstanding classification results. However, most of them exited the defects of underutilization of spectral-spatial features, redundant information, and convergence difficulty. To address these problems, a novel 3D-2D multibranch feature fusion and dense attention network are proposed for HSI classification. Specifically, the 3D multibranch feature fusion module integrates multiple receptive fields in spatial and spectral dimensions to obtain shallow features. Then, a 2D densely connected attention module consists of densely connected layers and spatial-channel attention block. The former is used to alleviate the gradient vanishing and enhance the feature reuse during the training process. The latter emphasizes meaningful features and suppresses the interfering information along the two principal dimensions: channel and spatial axes. The experimental results on four benchmark hyperspectral images datasets demonstrate that the model can effectively improve the classification performance with great robustness.

scholarly journals Convolutional Neural Networks with Deep Supervised Feature Learning for Remote Sensing Scene Classification

2020 ◽  
Author(s):  
Grigorios Tsagkatakis ◽  
Panagiotis Tsakalides
Keyword(s):  
Remote Sensing ◽  
Feature Learning ◽  
Ground Truth ◽  
Classification Performance ◽  
Cross Entropy ◽  
Scene Classification ◽  
Feature Representations ◽  
Benchmark Datasets ◽  
Low Dimensional ◽  
Fully Connected

State-of-the-art remote sensing scene classification methods employ different Convolutional Neural Network architectures for achieving very high classification performance. A trait shared by the majority of these methods is that the class associated with each example is ascertained by examining the activations of the last fully connected layer, and the networks are trained to minimize the cross-entropy between predictions extracted from this layer and ground-truth annotations. In this work, we extend this paradigm by introducing an additional output branch which maps the inputs to low dimensional representations, effectively extracting additional feature representations of the inputs. The proposed model imposes additional distance constrains on these representations with respect to identified class representatives, in addition to the traditional categorical cross-entropy between predictions and ground-truth. By extending the typical cross-entropy loss function with a distance learning function, our proposed approach achieves significant gains across a wide set of benchmark datasets in terms of classification, while providing additional evidence related to class membership and classification confidence.

scholarly journals Hyperspectral Image Classification Based on Two-Branch Spectral–Spatial-Feature Attention Network

2021 ◽  
Vol 13 (21) ◽  
pp. 4262
Author(s):  
Hanjie Wu ◽  
Dan Li ◽  
Yujian Wang ◽  
Xiaojun Li ◽  
Fanqiang Kong ◽  
...  
Keyword(s):  
Hyperspectral Image ◽  
Classification Performance ◽  
Hybrid Architecture ◽  
Classification Methods ◽  
Attention Network ◽  
Spatial Feature ◽  
Spatial Features ◽  
Original Dataset ◽  
Training Samples ◽  
Feature Attention

Although most of deep-learning-based hyperspectral image (HSI) classification methods achieve great performance, there still remains a challenge to utilize small-size training samples to remarkably enhance the classification accuracy. To tackle this challenge, a novel two-branch spectral–spatial-feature attention network (TSSFAN) for HSI classification is proposed in this paper. Firstly, two inputs with different spectral dimensions and spatial sizes are constructed, which can not only reduce the redundancy of the original dataset but also accurately explore the spectral and spatial features. Then, we design two parallel 3DCNN branches with attention modules, in which one focuses on extracting spectral features and adaptively learning the more discriminative spectral channels, and the other focuses on exploring spatial features and adaptively learning the more discriminative spatial structures. Next, the feature attention module is constructed to automatically adjust the weights of different features based on their contributions for classification to remarkably improve the classification performance. Finally, we design the hybrid architecture of 3D–2DCNN to acquire the final classification result, which can significantly decrease the sophistication of the network. Experimental results on three HSI datasets indicate that our presented TSSFAN method outperforms several of the most advanced classification methods.

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 Contrastive Learning for 3D Point Clouds Classification and Shape Completion

Sensors ◽  
2021 ◽  
Vol 21 (21) ◽  
pp. 7392
Author(s):  
Danish Nazir ◽  
Muhammad Zeshan Afzal ◽  
Alain Pagani ◽  
Marcus Liwicki ◽  
Didier Stricker
Keyword(s):  
Point Cloud ◽  
Feature Learning ◽  
Point Clouds ◽  
Classification Performance ◽  
Feature Representations ◽  
3D Point Clouds ◽  
Chamfer Distance ◽  
Shape Completion ◽  
Number Of Classes

In this paper, we present the idea of Self Supervised learning on the shape completion and classification of point clouds. Most 3D shape completion pipelines utilize AutoEncoders to extract features from point clouds used in downstream tasks such as classification, segmentation, detection, and other related applications. Our idea is to add contrastive learning into AutoEncoders to encourage global feature learning of the point cloud classes. It is performed by optimizing triplet loss. Furthermore, local feature representations learning of point cloud is performed by adding the Chamfer distance function. To evaluate the performance of our approach, we utilize the PointNet classifier. We also extend the number of classes for evaluation from 4 to 10 to show the generalization ability of the learned features. Based on our results, embeddings generated from the contrastive AutoEncoder enhances shape completion and classification performance from 84.2% to 84.9% of point clouds achieving the state-of-the-art results with 10 classes.

scholarly journals Double-Branch Network with Pyramidal Convolution and Iterative Attention for Hyperspectral Image Classification

2021 ◽  
Vol 13 (7) ◽  
pp. 1403
Author(s):  
Hao Shi ◽  
Guo Cao ◽  
Zixian Ge ◽  
Youqiang Zhang ◽  
Peng Fu
Keyword(s):  
Hyperspectral Image ◽  
State Of The Art ◽  
Classification Performance ◽  
Attention Mechanism ◽  
Hyperspectral Images ◽  
Hyperspectral Image Classification ◽  
Excellent Performance ◽  
Common Procedure ◽  
Spatial Features ◽  
First Choice

Deep-learning methods, especially convolutional neural networks (CNN), have become the first choice for hyperspectral image (HSI) classification to date. It is a common procedure that small cubes are cropped from hyperspectral images and then fed into CNNs. However, standard CNNs find it difficult to extract discriminative spectral–spatial features. How to obtain finer spectral–spatial features to improve the classification performance is now a hot topic of research. In this regard, the attention mechanism, which has achieved excellent performance in other computer vision, holds the exciting prospect. In this paper, we propose a double-branch network consisting of a novel convolution named pyramidal convolution (PyConv) and an iterative attention mechanism. Each branch concentrates on exploiting spectral or spatial features with different PyConvs, supplemented by the attention module for refining the feature map. Experimental results demonstrate that our model can yield competitive performance compared to other state-of-the-art models.

scholarly journals Modifications of the Multi-Layer Perceptron for Hyperspectral Image Classification

2021 ◽  
Vol 13 (17) ◽  
pp. 3547
Author(s):  
Xin He ◽  
Yushi Chen
Keyword(s):  
Spatial Information ◽  
Hyperspectral Image ◽  
Classification Performance ◽  
Multi Layer Perceptron ◽  
Feature Mapping ◽  
Spatial Feature ◽  
Spatial Features ◽  
Linear Layer ◽  
Input Sample ◽  
Good Classification Performance

Recently, many convolutional neural network (CNN)-based methods have been proposed to tackle the classification task of hyperspectral images (HSI). In fact, CNN has become the de-facto standard for HSI classification. It seems that the traditional neural networks such as multi-layer perceptron (MLP) are not competitive for HSI classification. However, in this study, we try to prove that the MLP can achieve good classification performance of HSI if it is properly designed and improved. The proposed Modified-MLP for HSI classification contains two special parts: spectral–spatial feature mapping and spectral–spatial information mixing. Specifically, for spectral–spatial feature mapping, each input sample of HSI is divided into a sequence of 3D patches with fixed length and then a linear layer is used to map the 3D patches to spectral–spatial features. For spectral–spatial information mixing, all the spectral–spatial features within a single sample are feed into the solely MLP architecture to model the spectral–spatial information across patches for following HSI classification. Furthermore, to obtain the abundant spectral–spatial information with different scales, Multiscale-MLP is proposed to aggregate neighboring patches with multiscale shapes for acquiring abundant spectral–spatial information. In addition, the Soft-MLP is proposed to further enhance the classification performance by applying soft split operation, which flexibly capture the global relations of patches at different positions in the input HSI sample. Finally, label smoothing is introduced to mitigate the overfitting problem in the Soft-MLP (Soft-MLP-L), which greatly improves the classification performance of MLP-based method. The proposed Modified-MLP, Multiscale-MLP, Soft-MLP, and Soft-MLP-L are tested on the three widely used hyperspectral datasets. The proposed Soft-MLP-L leads to the highest OA, which outperforms CNN by 5.76%, 2.55%, and 2.5% on the Salinas, Pavia, and Indian Pines datasets, respectively. The proposed Modified-MLP, Multiscale-MLP, and Soft-MLP are tested on the three widely used hyperspectral datasets (i.e., Salinas, Pavia, and Indian Pines). The obtained results reveal that the proposed models provide competitive results compared to the state-of-the-art methods, which shows that the MLP-based methods are still competitive for HSI classification.

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