=Paper=
{{Paper
|id=Vol-3207/paper1
|storemode=property
|title=A Center-Masked Convolutional Transformer for Hyperspectral Image Classification
|pdfUrl=https://ceur-ws.org/Vol-3207/paper1.pdf
|volume=Vol-3207
|authors=Yifan Wang,Shuguo Jiang,Meng Xu,Shuyu Zhang,Sen Jia
|dblpUrl=https://dblp.org/rec/conf/cdceo/WangJXZJ22
}}
==A Center-Masked Convolutional Transformer for Hyperspectral Image Classification==
https://ceur-ws.org/Vol-3207/paper1.pdf
A Center-Masked Convolutional Transformer for
Hyperspectral Image Classification
Yifan Wang1 , Shuguo Jiang1 , Meng Xu1 , Shuyu Zhang1 and Sen Jia1,*
1
College of Computer Science and Software Engineering, Shenzhen University, China
Abstract
Hyperspectral images (HSIs) have a wide field of view and rich spectral information, where each pixel represents a small area
of the earth’s surface. The pixel-level classification task of HSI has become one of the research hotspots in hyperspectral
image processing and analysis. More and more deep learning methods have been proposed in recent years, among which
convolutional neural network (CNN) is the most influential. However, it is difficult for CNN-based models to obtain the
global receptive field in HSI classification task. Besides, most of the self-supervised training methods are based on sample
reconstruction, and it is not easy to achieve effective use of unlabeled samples. In this paper, we propose a novel convolutional
embedding module, combined with the Transformer blocks, which successfully improves the context-awareness while
retaining the local feature extraction capability. Moreover, a new self-supervised task is designed to make more efficient use
of unlabeled data. Our proposed pre-training task only masks the central token and reconstructs the central pixel from a
learnable vector. It allows the model to capture the patterns between the central object and surrounding objects without
labels.
Keywords
Deep learning, Masked autoencoder, Transformer, Hyperspectral image classification.
1. Introduction [6], to classify the ground objects through spectral in-
formation. However, the imaging distance of HSI is far
Hyperspectral images are generally composed of dozens away, and there are many interference factors in this pro-
to hundreds of bands and have the characteristics of low cess, so that the spectral curve of different surface objects
spatial resolution and high spectral resolution. The spec- is not always easy to distinguish. This creates difficul-
tral information provides the possibility to distinguish the ties for these methods to achieve good performance in
corresponding land covers, which has spawned various complex scenes. In recent years, deep learning methods
research fields. Among them, pixel-level hyperspectral have gradually become popular, in which CNN-based
image classification is the most concerned one in the methods are dominant. Hu et al. [7] made a preliminary
community. Its main task is to assign a class label to attempt that several 1-D convolutional layers are stacked
each pixel, somewhat like semantic segmentation in the to extract local spectral information, and many classical
computer vision (CV) field. Different from RGB image, data augmentation methods in CV have been introduced.
hyperspectral image is high-dimensional data. In order to Roy et al. [8] combined 3D-CNN and 2D-CNN to achieve
avoid the curse of dimensionality, principal component hierarchical feature learning. In addition, other neural
analysis (PCA) [1] and independent component analysis networks have also achieved good performance. Zhou
[2] are widely used for redundancy elimination. et al. [9] designed a two-branch Long Short-Term Mem-
So far, many hyperspectral image classification meth- ory network (LSTM) to extract spectral information and
ods have been proposed, but deep learning methods have spatial information respectively. He et al. [10] proposed
taken the lead. According to the different techniques a pure multilayer perceptron (MLP) network, proving
used, it can be divided into traditional methods and deep that the MLP network still has potential. Hong et al.
learning-based methods. In early research, people mostly [11] designed a mini-batch graph neural network. It
selected a single pixel and all its spectral information is worth mentioning that the recently prevalent Trans-
as the training sample and rely on the traditional clas- former model has also been introduced. Hu et al. [12]
sifiers, such as logistic regression [3], decision tree [4], used 1-D convolution as an embedding layer combined
random forest [5], and support vector machine (SVM) with Transformer Block. Hong et al. [13] analyzed the dif-
ference between Transformer and other classical neural
CDCEO 2022: 2nd Workshop on Complex Data Challenges in Earth networks in detail and proposed a ViT-based Spectral-
Observation, July 25, 2022, Vienna, Austria
Former for spectral information learning. Zhong et al.
*
Corresponding author.
$ 2070276050@email.szu.edu.cn (Y. Wang); shuguoj@foxmail.com [14] proposed a spatial–spectral Transformer network
(S. Jiang); m.xu@szu.edu.cn (M. Xu); shuyu-zhang@szu.edu.cn and a model structure search framework. Dang et al. [15]
(S. Zhang); senjia@szu.edu.cn (S. Jia) combined spectral-spatial attention module with densely
© 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License
Attribution 4.0 International (CC BY 4.0). connected Transformer blocks. Besides, self-attention
CEUR
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network is also used to address adversarial attacks that Table 1
may be encountered in hyperspectral classification tasks The Detail Description of BG3DCE Module
[16].
However, limited by the size of the receptive field, Layer Kernel Size Output Shape Groups Param
it is difficult for CNN models to capture the global re- Input - (8, 13, 13, 10) - -
lationship. Meanwhile, deep learning models are data- Conv3D-1 (3, 3, 3) (32, 11, 11, 8) 8 896
driven which means that more labeled data leads to better BatchNorm3d-1 - - - 64
model performance. But, obtaining such a large number Conv3D-2 (3, 3, 3) (32, 9, 9, 6) 8 3,488
of labeled samples in practical applications is expensive. BatchNorm3d-2 - - - 64
How to effectively use unlabeled data has become an Reshape - (192, 9, 9) -
urgent need. The self-supervised pre-training method in
Conv2D (1,1) (128, 9, 9) 8 3,200
HSI classification task is still stuck in the autoencoder-
Reshape - (81, 128) - -
based sample reconstruction [17]. This article proposes
a band-grouping-based 3D convolutional Transformer
(BG3DCT) and a new self-supervised task for model pre-
training. The main contributions are listed as follows: 2.1.1. BG3DCE Module
• A novel band-grouping-based 3D convolutional Considering the differences between RGB images and
Transformer is designed for HSI classification. HSIs, the ViT network is not well compatible with hyper-
We replace the commonly used linear embedding spectral image. When the training sample is limited, the
module with a well-designed 3D convolutional linear embedding module cannot sufficiently character-
embedding module, combined with the spectral ize the spatial-spectral features. Meanwhile, CNN-based
segmentation strategy, to achieve efficient spatial- module is more adaptable to this situation, while also
spectral feature embedding in each sub-band. being able to capture the local texture information. So
we design a band-grouping-based 3D convolutional em-
• According to the characteristics of hyperspectral
bedding module for HSI embedding. Firstly, we perform
data, a new pre-training task is proposed. In the
PCA processing on the input samples and employ a spec-
process of masking and reconstructing the cen-
tral partition strategy to divide the spectra into several
ter pixel, the model’s ability to capture the rela-
sub-bands of equal length. Because the spectral curves of
tionship between the center pixel and surround-
objects often have local differences, the extraction of 3D-
ing pixels is improved. Compared to the overall
CNN on sub-band is more efficient than full-band. Then,
sample reconstruction task, center-masked pre-
paralleled 3D convolution extraction are performed on
training task is more efficient for the representa-
each sub-band twice, and the 3D batch normalization
tion of center area in the pre-training stage.
operation is followed to unify the feature deviations gen-
• A series of comparative experiments and ablation erated from each sub-band. Finally, we concatenate the
experiments demonstrate the effectiveness of our features to maintain the relative positional relationship
proposed pre-training method and BG3DCT net- between sub-bands and a lightweight 2D-CNN is used for
work. In particular, our proposed pre-training feature fusion and compression. In particular, paralleled
method can alleviate the instability of results 3D convolution operation have a simple implementation,
caused by random sampling in the limited train- and the convolution function includes a grouped con-
ing samples scenario. volution option. The detailed description of BG3DCE
module is listed in table 1.
The rest of this paper is organized as follows. Our
proposed method will be introduced in detail in section
II. The descriptions of the comparative experiments and 2.1.2. Transformer Encoder
result analysis will be provided in section III, and section
The context-aware ability of CNN often needs to make
IV presents the conclusions. the model go deeper, but HSI data is limited, so it is dif-
ficult for us to stack modules as simply as the model in
CV task. In contrast, The multi-head attention module
2. Methodology can make up for the shortcomings of CNN here and ef-
fectively model the relationship between ground objects.
2.1. BG3DCT Network
So, the combination of CNN and Transformer is comple-
The BG3DCT network has three parts, the band- mentary and powerful. A standard Transformer mainly
grouping-based 3D convolutional embedding (BG3DCE) comprises position encoding, multi-head attention, and
module, Transformer encoder, and MLP head. The spe- feedforward layers. Since the convolution features con-
cific design is as follows. tains position information, the positional encoding is not
Figure 1: Architecture of the proposed band-grouping-based 3D convolutional Transformer for HSI classification.
Figure 2: Architecture of the proposed center-mask pre-training task.
used here, and the embedded spatial-spectral features hood areas of HSI samples serve for the central pixel. So,
are directly input into the Transformer blocks. Finally, our masking target can select the essential part in the
we add an average pooling layer to achieve the global training sample, namely the center pixel. Therefore, we
representation and get the classification results through replace the token in the middle of the sequence E with
a MLP layer. a learnable vector. Then, the masked sequence is input
into the decoder, and the pixel-level reconstruction is per-
2.2. Center-mask Pre-training Task formed by a MLP head to obtain the reconstruction result
𝑣ˆ𝑐 of the center pixel. The target of the center-masked
Today, most hyperspectral image classification meth- pre-training task is to reconstruct the centre pixel as effi-
ods are patch-based. The model’s input not only is the ciently as possible so that the encoder can better learn the
spectral curve of the centre pixel but also contains its relationship between the centre pixel and the neighbour
neighbour region, which is generally a square area and pixels without labels. The reconstruction target can be
makes the input more distinctive. Inspired by the form formulated as:
of the training sample, we propose the center-masked
pre-training task, which is similar to MAE [18] but our 𝑇 (𝑣𝑐 , 𝑣ˆ𝑐 ) = min |𝑣𝑐 − 𝑣ˆ𝑐 |2 (1)
method is easier to implement.
The flowchart of our proposed pre-training task is where 𝑇 is the similarity function. In the deep learning
shown in Fig. 2. The Encoder is a BG3DCT network framework, function 𝑇 is equivalent to the mean squared
but removes the average pooling layer and MLP layer. error (MSE) loss function.
The decoder consists of two layers of standard Trans-
former encoders, which are only used in the pre-training
stage. Given an input sample X and center pixel vector
3. Experiment
𝑣𝑐 , the latent representation of the input sample is E (em- To fully evaluate our proposed pre-training method and
bedded by the BG3DCE module). Unlike self-supervised BG3DCT network, we conduct comparative and ablation
pre-training in the CV field, RGB images cannot directly experiments on two public datasets, Salinas and Yellow
find areas that need to be focused on, but the neighbor- River Estuary (YRE). The detailed information and the
Table 2 Table 3
Number of Training and Testing Samples on The Salinas Number of Training and Testing Samples on The YRE Dataset
Dataset
Class Class Name Training Testing
Class Class Name Training Testing 1 Building 10 523
1 Brocoli green weeds 1 5 2004 2 River 10 5366
2 Brocoli green weeds 2 5 3721 3 Salt Marsh 10 4985
3 Fallow 5 1971 4 Shallow Sea 10 17540
5 Deep Sea 10 18667
4 Fallow rough plow 5 1389
6 Intertidal Saltwater Marsh 10 2333
5 Fallow smooth 5 2673 7 Tidal Flat 10 1782
6 Stubble 5 3954 8 Pond 10 1777
7 Celery 5 3574 9 Sorghum 10 636
8 Grapes untrained 5 11266 10 Corn 10 1499
9 Soil vinyard develop 5 6198 11 Lotus Root 10 2709
10 Corn senesced green weeds 5 3273 12 Aquaculture 10 8009
11 Lettuce romaine 4wk 5 1063 13 Rice 10 5498
12 Lettuce romaine 5wk 5 1922 14 Tamarix Chinensis 10 1210
15 Freshwater Herbaceous Marsh 10 1407
13 Lettuce romaine 6wk 5 911
16 Suaeda Salsa 10 864
14 Lettuce romaine 7wk 5 1065
17 Spartina Alterniflora 10 570
15 Vinyard untrained 5 7263 18 Reed 10 1960
16 Vinyard vertical trellis 5 1802 19 Floodplain 10 337
Total 80 54129 20 Locus 10 65
Total 200 77737
partition of the training set and testing set are shown
in the table 2 and table 3, respectively. We use three 180 bands after removing noise bands. The surface ob-
metrics to evaluate the classification results, overall accu- jects are mainly wetland vegetation, there are 20 kinds
racy (OA), classwise average accuracy (AA), and kappa of objects, and the total number of labeled samples is
coefficient (𝜅). All the experiments are conducted on a 77,937.
computer with an Intel Xeon Platinum 8260 CPU, 64-GB
RAM and an NVIDIA Tesla P100-16GB GPU. The model 3.2. Comparative Experiment
structure and parameter settings of the comparison meth-
ods comply with open source codes or corresponding pa- To demonstrate the superiority of our proposed method,
pers. For our proposed model, the patchsize is set to 13, we select five state-of-the-art methods on two public
the spectral dimension is 80 after PCA, and the number datasets, Salinas and YRE, including four CNN-based
of sub-bands is 10. The embedding size of each token is methods and one classical Transformer network. They
set to 128. The learning rate is set to 0.001, and Adam are CNNHSI [19], FC3D [20], HybridSN [21], TwoCNN
is adopt as the gradient descent optimizer. Meanwhile, [22], and Vision Transformer (ViT) [23]. Among them,
all the experiments are repeated ten times to smooth out several methods based on 2D-CNN are distinguished in
errors caused by random sampling. The setting of the the size of the convolution kernel and the structure de-
center-masked pre-training is the same. sign. CNNHSI stacks several 2-D Convolution layers with
1×1 kernel size. TwoCNN is a dual-branch CNN with a
2D-CNN and a 1D-CNN to extract spatial information
3.1. Datasets Description and spectral information, respectively. FC3D is a pure 3D-
3.1.1. Salinas Dataset CNN network, and HybridSN uses 3D convolution and
2D convolution successively for hierarchical feature ex-
The salinas dataset, collected by the AVIRS sensor in the traction. ViT divides the input samples into equal-sized
Salinas Valley, USA, has an image size of 512 × 217 and a patches, obtains the embedded tokens through linear
spatial resolution of 3.7 meters. After noise band removal, embedding module, and then inputs them into the Trans-
204 bands are remained. There are 16 kinds of ground former encoder.
objects in the dataset, with 56,975 samples that can be The results of the comparative experiments are shown
used for pixel-level classification. in table 4 and table 5. Our method has obtained obvious
advantages and achieved the best or second-best results
3.1.2. YRE Dataset in each class, reflecting our approach’s superiority and
robustness. Under the setting of training with limited
YRE dataset is a large scene dataset captured by the
samples, CNNHSI achieves excellent classification results
Gaofen-5 satellite in the yellow river estuary region of
due to its lightweight network structure. Limited by the
Shandong Province, China. Its size is 1400 × 1400, and
large model size, HybridSN, FC3D, and TwoCNN fail to
the spatial resolution of each pixel is 30 meters, leaving
Table 4 Table 6
Classification Accuracy (%) and Kappa Measure for The Salinas Ablation Study Results Toward The Center-Masked Pre-
Dataset training Pretask on The salinas and YRE Datasets
Class ViT HybridSN FC3D CNNHSI TwoCNN Ours YRE Salinas
1 97.83 99.93 99.95 93.64 93.44 99.98 Case
OA AA 𝜅 OA AA 𝜅
2 97.21 98.31 92.92 79.30 89.86 99.92 BG3DCT w/ pretrain 89.01 84.11 87.30 89.99 95.40 88.87
3 82.83 97.04 97.84 84.62 90.63 99.99 BG3DCT w/o pretrain 86.24 82.19 84.15 88.60 85.42 86.46
4 92.91 93.18 96.66 99.40 97.45 99.11
5 85.29 98.08 86.54 77.31 95.23 97.59
6 98.58 98.46 94.08 98.45 99.74 99.95
7 98.01 99.76 99.78 99.13 100.00 100.00
8 69.92 59.81 53.82 65.56 74.59 63.87 is slightly lower than that of the Salinas dataset. It is
9
10
97.38
82.71
96.84
93.33
95.46
90.32
94.70
46.59
99.67
95.17
99.77
95.08 worth mentioning that TwoCNN achieves good classifi-
11 90.61 97.65 89.21 90.40 95.71 99.96 cation results, which may benefit from its spectral feature
12 98.10 94.88 91.28 97.85 99.20 99.43
13 90.71 84.63 87.82 99.19 99.28 97.08 extraction branch.
14 96.15 98.97 92.99 92.94 96.39 99.31
15 62.93 71.60 65.73 40.12 66.80 83.49
16 94.19 79.97 88.53 82.69 96.38 99.08 3.3. Ablation Study
OA (%) 84.68 85.24 81.65 76.41 87.99 89.66
AA (%)
𝜅
89.71
83.01
91.40
83.70
88.93
79.78
83.87
73.74
93.10
86.65
95.85
88.54
In this section, we only conduct ablation experiments
on our proposed pre-training task, considering that the
experimental results compared with the ViT model can
Table 5 intuitively demonstrate the effectiveness of our proposed
Classification Accuracy (%) and Kappa Measure for The YRE BG3DCT module. The results are shown in table 6 that
Dataset the OA of the model finetuned on pre-trained model
Class ViT HybridSN FC3D CNNHSI TwoCNN Ours
outperforms the model without pre-training by 2.77%
1 49.78 82.35 78.84 90.66 70.48 84.23 and 1.33%, on the Salinas and YRE datasets, respectively.
2
3
95.45 100.00
55.45 61.34
99.93
72.20
98.83
74.55
99.96
85.93
97.36
78.15
This undoubtedly proves the superiority and robustness
4 78.22 71.04 72.78 73.08 90.44 92.36 of our pre-training task.
5 85.89 76.74 90.49 84.31 97.59 99.55
6 79.48 81.77 83.37 81.59 82.65 85.85
7 56.38 51.53 57.30 59.95 63.63 60.30
8 73.16 73.94 73.68 78.55 60.17 73.23 4. Conclusion
9 75.10 85.90 86.11 86.57 82.70 90.24
10
11
57.06
63.65
70.82
82.55
62.15
83.38
88.20
88.73
72.22
75.71
88.49
90.84
In this article, we creatively propose a band-grouping-
12 72.82 76.36 79.69 77.62 73.76 76.96 based convolutional embedding module to extract spatial-
13
14
71.27
67.11
87.47
75.32
84.49
76.50
92.63
87.81
87.79
88.63
89.78
79.28
spectral information in each sub-bands. The Transformer
15 59.32 64.65 74.65 82.00 96.32 71.77 module is used to model the global relationship between
16
17
74.27
81.29
93.02
93.98
89.89
89.47
92.66
95.08
92.20
93.72
95.08
94.40
surface objects. Additionally, for effective use of unla-
18 44.74 58.11 62.74 65.86 67.59 70.65 beled data, we design a new unsupervised pre-training
19
20
60.53
87.69
82.89
91.28
68.25
71.79
88.72
85.84
68.84
64.00
71.60
92.15
task for hyperspectral classification. Through the mask
OA (%) 75.58 76.14 80.84 84.61 87.45 89.01 and reconstruction process of the token generated from
AA (%) 69.43 78.05 77.88 83.26 80.72 84.11 the central area, the model can initialize the backbone
𝜅 71.87 72.96 78.09 82.30 85.48 87.29
network without labeled data and provide a more stable
model performance. To fully evaluate our proposed meth-
ods, we conducted a series of comparative experiments
obtain superior classification results. The performance and ablation experiments on two public datasets, Salinas
of the ViT model is not stable. When the distribution of and YRE. The experimental results prove the effective-
ground objects in the dataset is more complex, the linear ness and superiority of our method.
embedding module drags down the model performance.
Therefore, this proves the necessity of a well-designed
embedding layer for the Transformer network in HSI 5. Acknowledgment
classification. In addition, all the methods cannot dis-
criminate the Vinyard untrained class well, which may This work was supported in part by the National Natu-
be caused by the large variability of this land cover. It is a ral Science Foundation of China under Grant 41971300,
problem we need to solve in the future. The classification and Grant 61901278; in part by the Key Project of
results of each method on the YRE dataset are similar Department of Education of Guangdong Province un-
to the Salinas dataset. The YRE dataset is a large scene der Grant 2020ZDZX3045; in part by the Guangdong
dataset so that the classification task is more complicated. Basic and Applied Basic Research Foundation under
Hence, the classification performance of each method
Grant 2022A1515011290; in part by the Natural Sci- [12] X. Hu, W. Yang, H. Wen, Y. Liu, Y. Peng, A
ence Foundation of Guangdong Province under Grant lightweight 1-d convolution augmented trans-
2021A1515011413; in part by Shenzhen Scientific Re- former with metric learning for hyperspectral im-
search and Development Funding Program under Grant age classification, Sensors 21 (2021) 1751.
20200803152531004. [13] D. Hong, Z. Han, J. Yao, L. Gao, B. Zhang, A. Plaza,
J. Chanussot, Spectralformer: Rethinking hy-
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