Triple-Attention-Based Parallel Network for Hyperspectral Image Classification

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...

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Bibliographic Details
Published in:Remote sensing (Basel, Switzerland) Switzerland), 2021-01, Vol.13 (2), p.324
Main Authors: Qu, Lei, Zhu, Xingliang, Zheng, Jiannan, Zou, Liang
Format: Article
Language:English
Subjects:
Algorithms
Artificial neural networks
channel–spectral–spatial attention
Classification
Coders
Datasets
Feature maps
feature reuse
hyperspectral image classification
Hyperspectral imaging
Image classification
Image segmentation
Information processing
Leakage
Methods
Neural networks
parallel network
Partitioning
Remote sensing
Semantics
Spatial discrimination
Spatial resolution
Support vector machines
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Summary: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.
ISSN:2072-4292
2072-4292
DOI:10.3390/rs13020324