Hyperspectral Image Classification With Pre-Activation Residual Attention Network

Gao, Hongmin; Yang, Yuwei; Yao, Dan; Li, Chenming

doi:10.1109/access.2019.2957163

Cited by 30 publications

(11 citation statements)

References 48 publications

(52 reference statements)

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“…In addition, for fair comparison, all experiments are performed in the same environment, and the same hyperparameters are set. Compare the proposed RCTC model with Alex-Net [24], Res-Net [21], Dense-Net [29], PRAN [8], AML [27] and SAGP [31]. Tables 2, 3 and 7 show the results of comparative experiments with different methods.…”

Section: Real-time Transmission Image Preprocessingmentioning

confidence: 99%

Residual-capsule networks with threshold convolution for segmentation of wheat plantation rows in UAV images

Cai

Wei

Song

et al. 2021

Multimed Tools Appl

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The early growth process of wheat is vulnerable to various factors, and poor growth leads to vacancies in the planting row. Therefore, the wheat images captured by unmanned aerial vehicles (UAV) are essential for monitoring the growth of wheat and preventing diseases and insect pests. This paper uses wheat images captured by UAV as a dataset, and propose a novel residual-capsule network with threshold convolution (RCTC) for segmentation of wheat plantation rows. The network is achieved by replacing the AveragePooling of the improved ResNet34 with Capsule. Since the capsule network represents the features by vectors, it can explain the direction of features and the relative positions between features. Therefore, deeper feature information can be extracted. In addition, to reduce redundant features and enhance effective features, a new threshold convolution is also proposed. Experiments on the wheat field dataset show that our proposed algorithm can effectively segment the wheat plantation rows images collected by UAV, and is superior to some existing well-known algorithms, and can provide scientific support and reference for the decision-making process of smart agriculture.

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Section: Real-time Transmission Image Preprocessingmentioning

confidence: 99%

Residual-capsule networks with threshold convolution for segmentation of wheat plantation rows in UAV images

Cai

Wei

Song

et al. 2021

Multimed Tools Appl

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“…For spatial domain, spatial features are regarded as a complementary to spectral ones, where the inner-spatial dependency were exploited to support spatial attention. Gao et al [53] added the attention mechanism into the pre-activation residual block. Sun et al [54] make a attention module that can be embedded anywhere in the spectral module and spatial module for HSIC.…”

Section: A Attention Mechanismmentioning

confidence: 99%

Siamese Spectral Attention With Channel Consistency for Hyperspectral Image Classification

Wang¹,

Yang²,

Liu³

et al. 2021

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

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Abundant spectral features are the precious wealth of hyperspectral images (HSI). Nevertheless, well-designed spectral feature is still a challenge that affects the performance of the classifier, especially with insufficient number of training samples. To make up the poor discriminability of spectral feature, doublebranch methods are proposed by fusing parallel spectral and spatial branches. However, this structure does nothing to improve the quality of spectral feature, which is regarded as the most valuable information for hyperspectral image information. In this paper, we propose a siamese spectral attention network with channel consistency (SSACC) to focus on obtaining discriminative spectral features, thus improving the generalization ability of the classifier. Two kinds of HSI cubes with different patch sizes are generated as the input of SSACC. The two cubes are divided into top and bottom branches and then be fed into the siamese network to obtain the refined spectral features. Then, self-attention is conducted to interacting with each channel for the spectral features enhancement. Meanwhile, two attention maps are obtained to display the spectral structures of each branch. A channel consistency regularization is performed on the two attention maps by enforcing the two branches to possess similar spectral patterns when identifying the same centric pixel. Extensive experiments conducted on the three HSI datasets verify the superiority of the obtained spectral feature. Furthermore, the proposed method applying convolution only on the spectral domain outperforms the state-of-the-art double-branch methods which integrate the spectral and spatial features simultaneously. Index Terms-spectral siamese; channel consistency; doublebranch; hyperspectral image classification.

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“…In general, the purpose of the attention mechanism is to strengthen important information and weaken unimportant information by certain methods. Inspired by the principle of SENet [36], we used the bottom-up top-down structure to construct the attention mechanism based on CNN. Our proposed channel-wise and spatial-wise attention modules have the same architecture, but the implementation methods of up-sampling are different.…”

Section: Proposed Attention Mechanismmentioning

confidence: 99%

“…This attention module, which only focuses on spatial information, cannot globally optimize feature maps processed by convolutional layers. The authors of [36] adopted the squeeze-and-excitation network (SENet) [37] to adaptively recalibrate channel feature responses by explicitly modelling interdependencies between channels. In addition, [38] also constructed a spatial-spectral squeeze-and-excitation (SSSE) module based on SENet.…”

Section: Introductionmentioning

confidence: 99%

Residual Group Channel and Space Attention Network for Hyperspectral Image Classification

Cui

Gan

et al. 2020

Remote Sensing

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Recently, deep learning methods based on three-dimensional (3-D) convolution have been widely used in the hyperspectral image (HSI) classification tasks and shown good classification performance. However, affected by the irregular distribution of various classes in HSI datasets, most previous 3-D convolutional neural network (CNN)-based models require more training samples to obtain better classification accuracies. In addition, as the network deepens, which leads to the spatial resolution of feature maps gradually decreasing, much useful information may be lost during the training process. Therefore, how to ensure efficient network training is key to the HSI classification tasks. To address the issue mentioned above, in this paper, we proposed a 3-DCNN-based residual group channel and space attention network (RGCSA) for HSI classification. Firstly, the proposed bottom-up top-down attention structure with the residual connection can improve network training efficiency by optimizing channel-wise and spatial-wise features throughout the whole training process. Secondly, the proposed residual group channel-wise attention module can reduce the possibility of losing useful information, and the novel spatial-wise attention module can extract context information to strengthen the spatial features. Furthermore, our proposed RGCSA network only needs few training samples to achieve higher classification accuracies than previous 3-D-CNN-based networks. The experimental results on three commonly used HSI datasets demonstrate the superiority of our proposed network based on the attention mechanism and the effectiveness of the proposed channel-wise and spatial-wise attention modules for HSI classification. The code and configurations are released at Github.com.

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Hyperspectral Image Classification With Pre-Activation Residual Attention Network

Cited by 30 publications

References 48 publications

Residual-capsule networks with threshold convolution for segmentation of wheat plantation rows in UAV images

Residual-capsule networks with threshold convolution for segmentation of wheat plantation rows in UAV images

Siamese Spectral Attention With Channel Consistency for Hyperspectral Image Classification

Residual Group Channel and Space Attention Network for Hyperspectral Image Classification

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