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A hyperspectral image classification algorithm based on atrous convolution
EURASIP Journal on Wireless Communications and Networking volume 2019, Article number: 270 (2019)
Abstract
Hyperspectral images not only have high spectral dimension, but the spatial size of datasets containing such kind of images is also small. Aiming at this problem, we design the NGAPC (nongridding multilevel concatenated Atrous Pyramid Convolution) module based on the combined atrous convolution. By expanding the receptive field of three layers convolution from 7 to 45, the module can obtain a distanced combination of the spectral features of hyperspectral pixels and solve the gridding problem of atrous convolution. In NGAPC module, we construct a 15layer Deep Convolutional Neural Networks (DCNN) model to classify each hyperspectral pixel. Through the experiments on the Pavia University dataset, the model reaches 97.9% accuracy while the parameter amount is only 0.25 M. Compared with other CNN algorithms, our method gets the best OA (Over All Accuracy) and Kappa metrics, at the same time, NGAPC module keeps good performance and high efficiency with smaller number of parameters.
Introduction
Hyperspectral remote sensing is a novel technology that can simultaneously acquire spectral and spatial information at the nanometer scale while maintaining the advantages of the previous wideband remote sensing technology. Moreover, hyperspectral remote sensing can cover tens or even hundreds of continuous narrowband spectrums formed from chromatic dispersion, such as ultraviolet, visible, nearinfrared, and farinfrared bands. Therefore, hyperspectral images (HSI) are often used for fine classification of features [1], such as distinguishing different types of crops or ground materials [2, 3]. In recent years, the spatial resolution of hyperspectral image sensors has been greatly improved. Using AVIRIS sensors, the 20m resolution of the Indian Pines dataset in 1992 has been improved by the 3.7m resolution on the Salinas dataset. Then, the reflective optics spectrographic imaging system (ROSIS03) has allowed to further improve to a 1.3m resolution on the Pavia University dataset. With such an improvement and an abundance of spectral features, the number of mixed pixels has been significantly reduced. Hence, the material properties of a singlepixel have become clearer, enabling to make qualitative detection out of small targets more feasible.
Nowadays, HSI classification algorithms are mainly divided into two categories: spectral information matching methods and statistical methods. The former methods directly utilize known spectral information in the spectral library to identify the types of features in the image. These methods can be used to compare and match the whole band spectral information, or to select some spectral bands information of interest, so as to achieve the purpose of classification. Examples of spectral information matching methods are minimum distance measure [4], binary code matching [5], spectral angle mapping, and spectral information divergence [6]. Such algorithms are mainly used in some HSI processing software, and the application scope and classification accuracy are limited.
Statisticalbased classification methods first convert image information into discrete digital matrices and then use strict mathematical derivation algorithms to distinguish different features. Examples of such methods are Support Vector Machine (SVM) [7], PCAbased classification method [8], and classification methods based on sparse matrix [9]. Statisticalbased classification methods usually require the processed data to meet certain conditions, such as conforming to normal distribution or supporting normalization. Thus, the high requirement of the digital matrices and the previous data processing will inevitably reduce the accuracy of classification.
In recent years, many researchers have used Convolutional Neural Networks (CNN) to classify hyperspectral images and achieved good results. CNN learn the feature maps of samples through convolution and downsampling hierarchical operations. Through multiple feedback optimizations, it automatically learns and finally obtains hierarchical features. In particular, CNNs have developed towards the direction of “deep” and many classic architectures. For instance, AlexNet [10] VGG16, GoogleNet [11], and ResNet [12] can achieve good results in target recognition and classification on huge datasets. Usually, those datasets (e.g., ImageNet, PASCAL VOC) are composed of tens of thousands of samples, i.e., the spatial features of three dimension (RGB) images. These Deep Convolutional Neural Networks (DCNN) deepen with the increase of parameters and computation; thus, the training process becomes more difficult [13,14,15]. Therefore, how to construct a lightweight model has become a hot research topic. To achieve this goal, models such as MobileNets [16], Squeezenet [17], Xception [18], and others [19, 20] use deepwise separable convolution or dilated convolution instead of full convolution to make them lightweight and effective.
Since HSI is different from an RGB image, the classical DCNN model cannot be directly applied to HSI classification. Indeed, such a classification raises three main problems:
 a)
Multiple spectral features. HSI has hundreds of dimensions of spectral features. If directly input DCNN model, the kernels of each layer should have several times the number of dimensions of the original, which causes the number of parameters to increase exponentially. This phenomenon is called dimension disaster. Moreover, a further dimensional reduction reduces accuracy, such as SVM [7].
 b)
Small spatial size of hyperspectra dataset. Usually, the HSI dataset has little spatial information, even only a picture of tens of thousands of pixels. This could be not enough for a DCNN model to achieve effectively classification by extracting spatial features, and it is easy to cause overfitting. Therefore, the common hyperspectral CNN model [21, 22] has only two convolution layers; thus, it is difficult to learn the combination features over a long distance.
 c)
The number of parameters has to be kept as small as possible to achieve fast computational speed. This is necessary because in the future, it envisioned the possibility to run a DCNN model on mobile devices and provide realtime feedback. For example, realtime land information can be provided to farmers through HIS.
To solve the above three problems, in this paper, we combine the atrous pyramid convolution to obtain HSI’s spectral features more effectively by solving the gridding problem in the atrous convolution. We acquire the feature maps of the spectral information in all dimensions and replace the fully connected layer with two convolutional layers. It significantly reduces the number of parameters and provides as output the material label of a single hyperspectral pixel.
The rest of this paper is organized as follows. In Section 2, we introduce the atrous convolution. Section 3 briefly introduces gridding and nongridding problems. Section 4 gives the algorithm of NGAPC module. In Section 5, we experimentally compare the performance of our method with SVM and some other CNNbased algorithms. Section 6 draws some conclusions.
Atrous convolution analysis
In the field of image semantic segmentation, Chen proposed the Deeplab model [23] in 2014, which introduced the atrous convolution. In 2017, Chen proposed the atrous spatial pyramid pooling [24] [25], which was applied to the upsampling part of the encodedecode architecture to expand the convolution receptive field to acquire longdistance features effectively. It performed good in RGB image semantic segmentation. The core idea is to expand the receptive field of different scales by changing the size of the atrous while maintaining the size of the convolution kernel and to quickly obtain a larger range of feature maps.
Atrous convolution, also known as dilated convolution, combines different cavitysized atrous convolutions called pyramid dilated convolutions. Atrous convolution is to inject holes into the standard convolution map to expand the reception field without increasing the parameter amount and convolution depth. Moreover, it obtains larger scale feature information. 1D atrous convolution is defined as:
where y[i] is output signal, x[i] is input signal with a filter w[k] of length K_{,} r corresponds to the dilation rate to sample x[i], and standard convolution is a special case for the rate r = 1.
As shown in Fig. 1, each small square represents a pixel. The red pixel is the center pixel of the convolution; the blue pixels are associated with the center pixel and covered by the atrous convolution. That is to say, the feature of blue pixels can be sampled at that location. The gray ones are the holes from which the convolution cannot learn the feature of the location. Through one layer of atrous convolution, it is possible to associate far apart features, that is, to obtain a combined feature of a longer distance dimension.
The 1D reception field equation is as follows:
where F_{1} is the 1D reception field, k is the size of the convolution kernel, r is the rate, and the size of hole is r − 1. For instance, in the case of kernel = 3, rate = 4, we can get F_{1} = 9. In the case of 2D convolution, the reception field is Eq. (3):
There are three ways to get the same receptive field, if we use the standard convolution. First, using the big kernel of 7, the parameters are also expanded by 7/3 times; Second, using the fourlayer convolution structure kernel of 3, the parameters are expanded by 4 times; Third, using twolayer convolution kernel of 3, the connection is expanded by max pooling or average pooling in the middle, and the parameter quantity is expanded by 2 times. However, the boundary is blurred, details are lost, and errors are introduced when pooling. It can be seen that the atrous convolution has great advantages in expanding the receptive field. At the same time, it also exists as the problem of not learning all features, which is called gridding problems.
Gridding and nongridding problems
Atrous convolutions with dilation rates larger than the one will generate holes called gridding artifacts, 2D atrous convolution with 3 × 3 kernel, and a dilation rate of r = 4 has a 9 × 9 receptive field. However, the number of points that are actually involved in the computation is only 9 out of 81, which implies that the actual receptive field is still 3 × 3 see Fig. 1b.
If we use the atrous convolution at the same rate or multiplying rate, gridding and nongridding problems will become worse. For example, if the rate is 2, 2, and 2 or 2, 4, and 8, this problem will show up [26]. In Fig. 2, the atrous convolution of different atrous rates is selected to convolute from top to bottom. The red pixel in the figure is the central pixel of the convolution kernel, which is combined with the features of other locations by different atrous rate to construct a feature map, regardless of the size of rate and the number of convolution layers. According to the principle of convolution, although the data of other positions will be introduced after the convolution, the data of the center position (red) will always exist. Blue pixels are positions where features can be obtained by atrous convolution and the more the number of combinations. The higher redundancy, the deeper of the blue color.
Gridding and nongridding sketch map is shown in Fig. 2. It can be seen from Fig. 2a, after threelayer atrous convolution where atrous rate is set to 3, the reception field can be expanded to 12. But the effective connection point is 5, and the adjacent points of the red pixels are not connected.
In this case, the efficiency of the receptive field (ERF) is 5/12, and the gridding problem is obvious. Since the convolution kernel has holes, after several superimposed atrous convolutions, there will be a problem that the features of the data in the receptive field are incomplete. Hence, it is also the essential cause of the gridding problem. For the gridding problem, the researchers have proposed two solutions:
First is the parallel method, in which we use different rates in the same layer of convolution and then concatenate the results of the convolution. Chen used the parallel atrous convolution with the rate = 6, 12, 18, and 24 in DeepLabv2. It groups several dilated convolutional layers and applies dilation rates without a common factor relationship. ASPP (Atrous Spatial Pyramid Pooling) improved encoding part and achieved a better semantic segmentation performance. Sachin Mehta improved the Resnet architecture in the efficient spatial pyramid (ESP) module [24], using atrous convolution rate = 2, 4, 8, and 16 to obtain feature maps of different receptive fields, and then concat to get the final feature maps by reusing the ESP structure. It achieved better recognition and segmentation results in the urban street scene of the PASCAL VOC dataset. Figure 2b illustrates the first approach and concatenates the result of the convolution with three branches, which atrous rate = 2, 4, and 8 on the input data. Receptive field is 17, and the ERF is 7/17, which improved the gridding problem. This approach is similar to inception, which widens DCNN and concatenates multiple branches with different size of kernel. The receptive field is 17/3 times that of a standard convolution of 3 kernel. Through only one layer of convolution, the reception field is enlarged and the parameter quantity is not increased. However, there is still a gridding problem in this approach. If you want to achieve nongridding, you need to use a continuous atrous rate. It is equivalent to a large kernel, but it loses the advantage of small convolution kernel with fewer parameters.
Second, as is shown in Fig. 2c, we call it the serial method. It used different atrous rate concatenated multilayer convolution, by controlling the size of the rate to degridding. In 2018, Wang [27] proposed hybrid dilated convolution (HDC) structure, which used a concatenated atrous convolutions of different rate = 1, 2, and 3 instead of rate = 2, 2, and 2. It is proved that this kind of setting can solve the gridding problem, and at the same time, it can expand the receptive field.
After using the rate = 1, 2, and 3 atrous convolution layers, the receptive field F_{1} = 13 and the ERF = 1, which is two times of the standard threelayer concatenated convolution of 3 kernels andF_{1} = 7. Therefore, all the features in the receptive field are obtained. We call this kind of scenario as nongridding.
Under the condition of nongridding, the calculation of rate and reception field in serial mode is obtained by formulas (4) and (5):
where r is atrous rate_{,} d is the number of layers in the serial convolution, max is the maximum value that can be obtained at nongridding, F is receptive field, and the calculation of the first layer of convolution F can be gotten in formula (2). In this paper, we only discuss and analyze the case of 1D convolution and of 3 kernels.
In summary, both of these two methods can improve the efficiency of convolution, expand the reception, and reduce the gridding problem.
NGAPC module
In this paper, combining these two methods of solving the gridding problem and introducing inception model, we propose a nongridding multilevel concatenated atrous pyramid convolution module (NGAPC). NGAPC module is divided into two parts. The first part is atrous pyramid convolution, while the second part is divided into several branches. Each branch uses a different atrous rate convolution and then merges together, which can achieve the maximum reception field of the nongridding atrous convolution.
In the first part of NGAPC module, the convolution depth is n, atrous pyramid convolution depth isn − 1. Formula (5) gives the desirable maximum value of atrous rate under nongridding conditions, which can be reduced according to the actual situation.
Layer n of NGAPC module is the second part of model. It is a convolution layer composed of multiple branches. The number of branches can be set, according to the requirement of receptive field in practical application. The rate of each branch is according to formula (6):
where \( {r}_1^1=1 \), \( {F}_{1\max}^1=3 \), and i denotes the ith branch of the module, d denotes the layer of the convolution, and max denotes the maximum value of r and F in the nongridding condition. 1D NGAPC module flow chart is shown in Fig. 3.
In Fig. 3, n is the number of convolution layers, and the default value is 3, which can be modified. The number of branches of the module is i, and \( {r}_n^i \) is atrous rate. According to formulas (5) and (6), \( {r}_n^i \) is not greater than \( {r}_{n\max}^i \), and \( {F}_n^i \) is reception field, which can obtain the global farthest distance feature. Figure 2d is an example of NGAPC module, which has three convolution layers. The first layer atrous rate is 1, the second layer atrous rate is 3, and the third layer has two branches. The atrous rate of branch 1 is 9 and reception field is 27. The atrous rate of branch 2 is 18 and reception field is 45. The holes in branch 2 can be filled by branch 1, so the whole is nongridding atrous pyramid.
In summary, it can accurately mark the category of each pixel, by using the atrous convolution to collect features between dimensions with large span and construct an appropriate deep learning architecture to learn the spectral features, for the spectral dimension of 100~200 of each pixel. In the classification of hyperspectral image, atrous convolution is effective in replacing traditional convolution in DCNN. Based on the nongridding atrous pyramid convolution, this paper designs a light weight depth learning architecture model for spectral information classification of hyperspectral images. The hyperspectral image NGAPC classification architecture model is divided into four parts.
 a)
Input the pixels as a single hyperspectral image, 1 × 1 × band numbers;
 b)
Input the pixels into the NGAPC module, where the number of convolution layers N = 3, the number of branches I = 2, the convolution of 128 kernel, the activation function uses ReLu, then the use concat function to integrate the feature map together to become the feature map of band numbers × 256.
 c)
Perform threelayer 1D convolution on the feature map while stride = 2. The number of convolution kernel is halved for every convolution layer and finally get a (band numbers/8) × 32 feature map.
Output the classification results, through the fully connected (FC) layer and the Softmax layer. As is shown in Fig. 4:
Experimental results and discussion
The goal of this algorithm is to obtain the material properties of a singlepixel for the identification of different kinds of ground objects in hyperspectral images. In the three public available hyperspectral datasets [26,27,28,29,30,31], the Indian Pines datasets and Salinas datasets target the farmland of different crops and the natural topography. They are continuous large areas of the same category, and the classification effect will be better by using the method of spatial spectrum combination. Since our algorithm aims at small target recognition of different features, we select the Pavia University dataset to verify the effectiveness of the algorithm. This dataset is one of the common scenes in the city, which is in line with the goal of this algorithm.
Experimental dataset
Pavia University dataset (as shown in Fig. 5) is from Germany’s Reflective Optics Spectrographic Imaging System (ROSIS03) in 2003.
It is a part of the hyperspectral data of the image of Pavia, Italy, with a spatial resolution of 1.3 m. The dataset has a total of 610 × 340 pixels. The spectral image resamples 115 samples in the 0.43–0.86 μm wavelength range, removing 12 bands that are subject to noise interference, hence the remaining is 103. This dataset has 42776 labeled pixels and 9 labeled samples, as is shown in Table 1.
In the dataset, when we randomly select the similar labeled pixels in different spatial locations, the features of shadows and painted metal are more consistent. However, the spectral features of bitumen, Gravel, etc., are quite different. During the experiment, we randomly divide the labeled samples into training sets and test sets according to the ratio of 80% and 20%. The unlabeled sample pixels are background pixels. Actually, there should be different material labels. Because they are not labeled, they are not used for training. Otherwise, it will affect the accuracy of the identification classification. Figure 5 shows the false color composite image and ground truth with different land cover classes.
Results and discussion
We set the experiment parameters as follows: learning rate is 0.9, the activation function is Relu, batch is 100, and the optimization strategy uses SGD. The model train and test are on a single NVIDIA GTX 980 4 GB GPUs and with 8 G memory.
In this paper, we compare NGAPC model with the classical SVM, the new 1DCNN [28, 29] 2DCNN [30], 3DCNN algorithms [31,32,33,34], and RNN [35]. We refer some of these algorithms by nshaud/DeepHyperX on GitHub. We set training sample 80%, test sample 20%, and keep some default parameter settings, such as patch size is 7 and epoch is 50.
In Table 2, we compare the classification results of the proposed NGAPC with other classification algorithms (SVM, 1D CNN, 2D CNN, 3D CNN, and RNN). NGAPC module belongs to 1D CNN. The classification results of NGAPC algorithm are better than the 1D and 2D CNN classification methods in gravel, meadows, and bitumen feature classification. Even though NGAPC algorithm has less accuracy than 3D CNN [32] classification results in bitumen and bricks features, it has much higher than the average level (0.821 and 0.900).
Table 3 shows confusion matrices for the Pavia University dataset. It can be seen from the detailed classification accuracy of all the classes, which is calculated from one arbitrary train/test partition. The cell of ith row and jth column means the probability that the ith class samples is classified as the jthclass. The percentages on diagonal line are just the classification accuracy of corresponding class. The proposed algorithm performs under 95% in only two classes (bitumen and bricks) among the nine classes. These two class samples are wrongly classified as asphalt with 3.38% and 3.26% separately. As shown in the Table 1, the two classes are the ones with smaller numbers of samples. The more similar two classes of spectral domain, the higher probability they are wrongly classified to each other.
The accuracy assessment criteria for classification
We use OA (overall accuracy) and Kappa (Kappa coefficient) as the criteria for classification of HSI. OA refers to the proportion of correctly classified samples to all classified samples. The formula is as follows (7):
where M is the total sample number of one class, K is the number of categories, and C(i, i) is the current classified sample by the algorithm.
Kappa is used to calculate the similarity between the classification results of ground and the real distribution of ground objects with disperse analysis method, the formula is as follows (8):
where C(i, +) is the total number of pixels, in which a ground object is divided into a particular category, C(+, i) is the total number of pixels, in which the ground object actually belongs to a particular category.
In Table 4, we compare five metrics (i.e., OA, Kappa, the parameters quantities, memory usage, and the runtime) with the other approaches. Compared with the 1D CNN models, whose parameters and runtime are equivalent, the NGAPC model gets better results on OA and Kappa. OA increases from 84.619% and 92.778% to 97.966%, while Kappa increases from 0.792 and 0.904 to 0.973. When comparing with the best 3D CNN [33], whose number of parameters and memory are 5.8 times and 4.68 times larger than NGAPC model. At the same time, OA of NGAPC increases 2.54% and Kappa of NGAPC increases 0.032, respectively. Therefore, the NGAPC model is the best one in those convolution models. It not only has high accuracy classification results, but also has smaller parameters and faster runtime.
Conclusions
In this paper, we propose a 15layer DCNN model with NGAPC module to classify singlepixel of HSI. This model has solved three problems in HSI classification. First, the singlepixel classification of HSI learns the whole spectral information of each pixel, which not only solves the problem of large computational complexity of highdimensional data, but also solves the problem of insufficient samples in DCNN training. Second, aiming at the reasonable combination of 1D atrous convolution, we propose NGAPC module, which solves the gridding problem and enlarges the receptive field from 7 to 45. Moreover, the classification accuracy is improved by learning the longdistance feature combination. The OA reaches 98% and the Kappa reaches 0.974 on the Pavia University dataset, whose are superior to many kinds of 1D, 2D, and 3D CNN models. Third, replacing the 1D convolution with the full connection layer, although the model is a 15layer DCNN, the parameters are similar to those of the same type of 1DCNN of five layers, which meets the requirements of small parameter models. In conclusion, NGAPC model is an excellent DCNN model for HSI classification.
Availability of data and materials
The authors declare that all the data and materials in this manuscript are available from the author.
Abbreviations
 AA:

Averaged accuracy
 CNN:

Convolutional neural networks
 DCNN:

Deep convolutional neural networks
 FC:

Fully connected layer
 HSI:

Hyperspectral images
 NGAPC:

Nongridding multilevel concatenated atrous pyramid convolution
 OA:

Over all accuracy
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Acknowledgements
Thanks for the support of the National Virtual Simulation Laboratory Center for Coal Mine Safety Mining, Shandong University of Science and Technology.
Funding
This project was supported by foundation of the National Natural Science Fund (41876202, 41774002, 61976126) and Natural Science Foundation of Shandong Province (ZR2017MD020).
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All authors contribute to the concept, the design, and developments of the algorithm and the simulation results in this manuscript. All authors read and approved the final manuscript.
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Correspondence to Weike Liu.
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Zhang, X., Zheng, Y., Liu, W. et al. A hyperspectral image classification algorithm based on atrous convolution. J Wireless Com Network 2019, 270 (2019). https://doi.org/10.1186/s136380191594y
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Keywords
 Deep Convolutional Neural Networks
 Hyperspectral image classification
 Atrous Convolution
 Gridding problem