Double-Branch Multi-Attention Mechanism Network for Hyperspectral Image Classification

Recently, Hyperspectral Image (HSI) classification has gradually been getting attention from more and more researchers. HSI has abundant spectral and spatial information; thus, how to fuse these two types of information is still a problem worth studying. In this paper, to extract spectral and spatial feature, we propose a Double-Branch Multi-Attention mechanism network (DBMA) for HSI classification. This network has two branches to extract spectral and spatial feature respectively which can reduce the interference between the two types of feature. Furthermore, with respect to the different characteristics of these two branches, two types of attention mechanism are applied in the two branches respectively, which ensures to extract more discriminative spectral and spatial feature. The extracted features are then fused for classification. A lot of experiment results on three hyperspectral datasets shows that the proposed method performs better than the state-of-the-art method.

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Classification of Hyperspectral Image Based on Double-Branch Dual-Attention Mechanism Network

10.20944/preprints201912.0059.v1 ◽

2019 ◽

Author(s):

Li Rui ◽

Zheng Shunyi ◽

Duan Chenxi ◽

Yang Yang ◽

Wang Xiqi

Keyword(s):

Spatial Information ◽

Hyperspectral Image ◽

State Of The Art ◽

Empirical Studies ◽

The State ◽

Attention Mechanism ◽

Spatial Features ◽

Better Than

In recent years, more and more researchers have gradually paid attention to Hyperspectral Image (HSI) classification. It is significant to implement researches on how to use HSI's sufficient spectral and spatial information to its fullest potential. To capture spectral and spatial features, we propose a Double-Branch Dual-Attention mechanism network (DBDA) for HSI classification in this paper, Two branches aer designed to extract spectral and spatial features separately to reduce the interferences between these two kinds of features. What is more, because distinguishing characteristics exist in the two branches, two types of attention mechanisms are applied in two branches above separately, ensuring to exploit spectral and spatial features more discriminatively. Finally, the extracted features are fused for classification. A series of empirical studies have been conducted on four hyperspectral datasets, and the results show that the proposed method performs better than the state-of-the-art method.

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Evaluating the Performance of the state-of-the-art HybridSN Deep Learning Algorithm for Airborne Hyperspectral Image Classification

IOP Conference Series Earth and Environmental Science ◽

10.1088/1755-1315/767/1/012019 ◽

2021 ◽

Vol 767 (1) ◽

pp. 012019

Author(s):

M A A M Abidin ◽

H Z M Shafri ◽

M M A Al-Habshi ◽

N S N Shaharum

Keyword(s):

Deep Learning ◽

Image Classification ◽

Hyperspectral Image ◽

Learning Algorithm ◽

State Of The Art ◽

The State ◽

Hyperspectral Image Classification ◽

Deep Learning Algorithm

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Effective subspace detection based on the measurement of both the spectral and spatial information for hyperspectral image classification

International Journal of Remote Sensing ◽

10.1080/01431161.2020.1763502 ◽

2020 ◽

Vol 41 (19) ◽

pp. 7541-7564 ◽

Cited By ~ 3

Author(s):

Sadia Zaman Mishu ◽

Boshir Ahmed ◽

Md. Ali Hossain ◽

Md. Palash Uddin

Keyword(s):

Image Classification ◽

Spatial Information ◽

Hyperspectral Image ◽

Hyperspectral Image Classification ◽

Subspace Detection

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Deep Spectral Spatial Inverted Residual Network for Hyperspectral Image Classification

Remote Sensing ◽

10.3390/rs13214472 ◽

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.

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