scholarly journals Triple-Attention-Based Parallel Network for Hyperspectral Image Classification

2021 ◽  
Vol 13 (2) ◽  
pp. 324
Author(s):  
Lei Qu ◽  
Xingliang Zhu ◽  
Jiannan Zheng ◽  
Liang Zou
Keyword(s):  
Image Classification ◽  
High Performance ◽  
Hyperspectral Image ◽  
Information Leakage ◽  
Data Partitioning ◽  
Hyperspectral Image Classification ◽  
Feature Maps ◽  
Parallel Network ◽  
High Level ◽  
Partitioning Strategy

Convolutional neural networks have been highly successful in hyperspectral image classification owing to their unique feature expression ability. However, the traditional data partitioning strategy in tandem with patch-wise classification may lead to information leakage and result in overoptimistic experimental insights. In this paper, we propose a novel data partitioning scheme and a triple-attention parallel network (TAP-Net) to enhance the performance of HSI classification without information leakage. The dataset partitioning strategy is simple yet effective to avoid overfitting, and allows fair comparison of various algorithms, particularly in the case of limited annotated data. In contrast to classical encoder–decoder models, the proposed TAP-Net utilizes parallel subnetworks with the same spatial resolution and repeatedly reuses high-level feature maps of preceding subnetworks to refine the segmentation map. In addition, a channel–spectral–spatial-attention module is proposed to optimize the information transmission between different subnetworks. Experiments were conducted on three benchmark hyperspectral datasets, and the results demonstrate that the proposed method outperforms state-of-the-art methods with the overall accuracy of 90.31%, 91.64%, and 81.35% and the average accuracy of 93.18%, 87.45%, and 78.85% over Salinas Valley, Pavia University and Indian Pines dataset, respectively. It illustrates that the proposed TAP-Net is able to effectively exploit the spatial–spectral information to ensure high performance.

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 Spatial Residual Blocks Combined Parallel Network for Hyperspectral Image Classification

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 74513-74524
Author(s):  
Buyi Zhang ◽  
Chunmei Qing ◽  
Xiangmin Xu ◽  
Jinchang Ren
Keyword(s):  
Image Classification ◽  
Hyperspectral Image ◽  
Hyperspectral Image Classification ◽  
Parallel Network

scholarly journals Hyperspectral Image Classification with Feature-Oriented Adversarial Active Learning

2020 ◽  
Vol 12 (23) ◽  
pp. 3879
Author(s):  
Guangxing Wang ◽  
Peng Ren
Keyword(s):  
Deep Learning ◽  
Active Learning ◽  
Image Classification ◽  
Hyperspectral Image ◽  
Hyperspectral Images ◽  
Hyperspectral Image Classification ◽  
The Real ◽  
Learning Classifier ◽  
Feature Variability ◽  
High Level

Deep learning classifiers exhibit remarkable performance for hyperspectral image classification given sufficient labeled samples but show deficiency in the situation of learning with limited labeled samples. Active learning endows deep learning classifiers with the ability to alleviate this deficiency. However, existing active deep learning methods tend to underestimate the feature variability of hyperspectral images when querying informative unlabeled samples subject to certain acquisition heuristics. A major reason for this bias is that the acquisition heuristics are normally derived based on the output of a deep learning classifier, in which representational power is bounded by the number of labeled training samples at hand. To address this limitation, we developed a feature-oriented adversarial active learning (FAAL) strategy, which exploits the high-level features from one intermediate layer of a deep learning classifier for establishing an acquisition heuristic based on a generative adversarial network (GAN). Specifically, we developed a feature generator for generating fake high-level features and a feature discriminator for discriminating between the real high-level features and the fake ones. Trained with both the real and the fake high-level features, the feature discriminator comprehensively captures the feature variability of hyperspectral images and yields a powerful and generalized discriminative capability. We leverage the well-trained feature discriminator as the acquisition heuristic to measure the informativeness of unlabeled samples. Experimental results validate the effectiveness of both (i) the full FAAL framework and (ii) the adversarially learned acquisition heuristic, for the task of classifying hyperspectral images with limited labeled samples.

scholarly journals A High-Performance Spectral-Spatial Residual Network for Hyperspectral Image Classification with Small Training Data

2020 ◽  
Vol 12 (19) ◽  
pp. 3137
Author(s):  
Wijayanti Nurul Khotimah ◽  
Mohammed Bennamoun ◽  
Farid Boussaid ◽  
Ferdous Sohel ◽  
David Edwards
Keyword(s):  
Data Structure ◽  
Image Classification ◽  
Spatial Data ◽  
High Performance ◽  
Hyperspectral Image ◽  
Spectral Characteristics ◽  
Training Data ◽  
Hyperspectral Image Classification ◽  
Residual Network ◽  
Training Time

In this paper, we propose a high performance Two-Stream spectral-spatial Residual Network (TSRN) for hyperspectral image classification. The first spectral residual network (sRN) stream is used to extract spectral characteristics, and the second spatial residual network (saRN) stream is concurrently used to extract spatial features. The sRN uses 1D convolutional layers to fit the spectral data structure, while the saRN uses 2D convolutional layers to match the hyperspectral spatial data structure. Furthermore, each convolutional layer is preceded by a Batch Normalization (BN) layer that works as a regularizer to speed up the training process and to improve the accuracy. We conducted experiments on three well-known hyperspectral datasets, and we compare our results with five contemporary methods across various sizes of training samples. The experimental results show that the proposed architecture can be trained with small size datasets and outperforms the state-of-the-art methods in terms of the Overall Accuracy, Average Accuracy, Kappa Value, and training time.

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