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

Li, Rui; Zheng, Shunyi; Duan, Chenxi; Yang, Yang; Wang, Xiqi

doi:10.3390/rs12030582

Cited by 251 publications

(70 citation statements)

References 47 publications

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“…DBDA: The architecture of the DBDA is presented in [32], which also contains spectral and spatial dense blocks and corresponding attention blocks. And we use 7×7×B image block as input.…”

Section: B Experimental Setupmentioning

confidence: 99%

“…The DBMA [31] and DBDA [32] models utilize dense network to extract spectral and spatial features, and attention modules are employed to recalibrate extracted features. These two methods are used as comparative methods to verify the feature extraction capability of hierarchical residual network.…”

Section: B Experimental Setupmentioning

confidence: 99%

“…Then, the multi-layer network uses spectral and spatial features to extract conjoint characteristics. The double-branch multi-attention mechanism network (DBMA) and double-branch dual-attention mechanism network (DBDA) use spectral and spatial dense blocks to extract spectral and spatial features respectively, and the attention modules are utilized to set different weights for extracted features [31], [32]. Aiming to solve the problem that CNNs set the same weight for all spectral bands, the spectral attention module-based convolutional network recalibrates spectral bands so as to strengthen important bands and suppress less useful ones [33].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

HResNetAM: Hierarchical Residual Network With Attention Mechanism for Hyperspectral Image Classification

Xue

Liu

et al. 2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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This article proposes a novel hierarchical residual network with attention mechanism (HResNetAM) for hyperspectral image spectral-spatial classification to improve the performance of conventional deep learning networks. The straightforward convolutional neural network based models have limitations in exploiting the multi-scale spatial and spectral features, and this is the key factor in dealing with the high-dimensional nonlinear characteristics present in hyperspectral images. The proposed hierarchical residual network can extract multi-scale spatial and spectral features at a granular level, so the receptive fields range of this network will be increased, which can enhance the feature representation ability of the model. Besides, we utilize the attention mechanism to set adaptive weights for spatial and spectral features of different scales, and this can further improve the discriminative ability of extracted features. Furthermore, the double branch structure is also exploited to extract spectral and spatial features with corresponding convolution kernels in parallel, and the extracted spatial and spectral features of multiple scales are fused for hyperspectral image classification. Four benchmark hyperspectral datasets collected by different sensors and at different acquisition time are employed for classification experiments, and comparative results reveal that the proposed method has competitive advantages in terms of classification performance when compared with other state-ofthe-art deep learning models.

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“…DBDA: The architecture of the DBDA is presented in [32], which also contains spectral and spatial dense blocks and corresponding attention blocks. And we use 7×7×B image block as input.…”

Section: B Experimental Setupmentioning

confidence: 99%

Section: B Experimental Setupmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

HResNetAM: Hierarchical Residual Network With Attention Mechanism for Hyperspectral Image Classification

Xue

Liu

et al. 2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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show abstract

“…distancebased measures) [4], and machine learning techniques such as the support vector machine [5] and random forest [6]. However, the high dependency on hand-crafted features or mid-level semantic characteristics restricts the flexibility and adaptability of these traditional approaches [7].…”

Section: Introductionmentioning

confidence: 99%

MACU-Net for Semantic Segmentation of Fine-Resolution Remotely Sensed Images

Duan

Zheng

et al. 2022

IEEE Geosci. Remote Sensing Lett.

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Semantic segmentation of remotely sensed images plays an important role in land resource management, yield estimation, and economic assessment. U-Net, a deep encoderdecoder architecture, has been used frequently for image segmentation with high accuracy. In this Letter, we incorporate multi-scale features generated by different layers of U-Net and design a multi-scale skip connected and asymmetric-convolutionbased U-Net (MACU-Net), for segmentation using fine-resolution remotely sensed images. Our design has the following advantages: (1) The multi-scale skip connections combine and realign semantic features contained in both low-level and high-level feature maps; (2) the asymmetric convolution block strengthens the feature representation and feature extraction capability of a standard convolution layer. Experiments conducted on two remotely sensed datasets captured by different satellite sensors demonstrate that the proposed MACU-Net transcends the U-Net, U-NetPPL, U-Net 3+, amongst other benchmark approaches.

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“…Remote sensing images have been widely used in many research and application fields, such as classification [1,2], object detection [3], and urban geographical mapping [4]. However, remote sensing images are often affected by cloud cover.…”

Section: Introductionmentioning

confidence: 99%

Thick Cloud Removal of Remote Sensing Images Using Temporal Smoothness and Sparsity Regularized Tensor Optimization

Duan

Pan

2020

Remote Sensing

Self Cite

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In remote sensing images, the presence of thick cloud accompanying shadow can affect the quality of subsequent processing and limit the scenarios of application. Hence, to make good use of such images, it is indispensable to remove the thick cloud and cloud shadow as well as recover the cloud-contaminated pixels. Generally, the thick cloud and cloud shadow element are not only sparse but also smooth along the spatial horizontal and vertical direction, while the clean element is smooth along the temporal direction. Guided by the above insight, a novel thick cloud removal method for remote sensing images based on temporal smoothness and sparsity regularized tensor optimization (TSSTO) is proposed in this paper. Firstly, the sparsity norm is utilized to boost the sparsity of the cloud and cloud shadow element, and unidirectional total variation (UTV) regularizers are applied to ensure the smoothness in different directions. Then, through thresholding, the cloud mask and the cloud shadow mask can be acquired and used to guide the substitution. Finally, the reference image is selected to reconstruct details of the repairing area. A series of experiments are conducted both on simulated and real cloud-contaminated images from different sensors and with different resolutions, and the results demonstrate the potential of the proposed TSSTO method for removing cloud and cloud shadow from both qualitative and quantitative viewpoints.

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

Cited by 251 publications

References 47 publications

HResNetAM: Hierarchical Residual Network With Attention Mechanism for Hyperspectral Image Classification

HResNetAM: Hierarchical Residual Network With Attention Mechanism for Hyperspectral Image Classification

MACU-Net for Semantic Segmentation of Fine-Resolution Remotely Sensed Images

Thick Cloud Removal of Remote Sensing Images Using Temporal Smoothness and Sparsity Regularized Tensor Optimization

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