Objective

2

Hyperspectral image classification is a challenging task because of the large number of spectral channels

small-sized labeled training samples

and large spatial variability. Most existing hyperspectral image classification models only consider spectral feature information and neglect the important role of spatial information in classification. Spatial features have become increasingly important in hyperspectral image classification because adjacent pixels are likely to belong to the same category

and the spectral-spatial classification method has the best classification accuracy. Compared with other remote sensing image data

hyperspectral remote sensing image data comprise 2D spatial plane images and increase spectral dimensions that contain the spectral information of objects. Hence

hyperspectral remote sensing image data are able to form 3D data cubes containing rich image information and spectral information. However

the number of bands increases with an increase in the dimension of a hyperspectral image. The information correlation between bands is high

and the hidden features are rich. The problem with high-dimensional small-sized labeled samples is that the number of labeled samples in a dataset is much smaller than the dimension of the sample features. The high data dimensions of hyperspectral images lead to low classification accuracy

excessive dependence on training samples

extended iteration training time

and low efficiency. First

the scale of corresponding feature space increases rapidly with an increase in feature dimensions

thereby leading to dimension issues. Second

the presence of many irrelevant or noise features means that the learning samples are few

resulting in overfitting. Therefore

a small training error still leads to the poor generalization ability of classifiers

which directly reduces the prediction capability of classification models. Multi-spectral image processing algorithms cannot be directly applied to hyperspectral image processing

which involves high-dimensional information and features that are complex

diverse

and massive. A dimensionality-varied convolutional neural network is proposed in this work to solve the problems of low classification accuracy

complex model structure

and large computational complexity of the algorithm on the basis of a convolution neural network for small-sized labeled samples of hyperspectral images.

Method

2

The dimensionality-varied convolutional neural network is an improved model that is based on convolution neural networks. Optimizing the structure of 3D convolution and 2D convolution in a network is the key to a successful classification. In a dimensionality-varied convolutional neural network

the main component of the convolution layer is a convolution core. The convolution core of the 3D feature extraction process is a 3D filter composed of a set of learning parameters. The application of the 3D convolution core to hyperspectral image classification simplifies the network structure and improves the accuracy of feature extraction. The convolution core of the 2D feature extraction process is a 2D filter composed of a set of learning parameters. If the number of convolution kernels is sufficient

then we can extract all feature types of an image synthetically and obtain an effective and rich extraction of the convolution layer. The basic function of the pooling layer is to gradually reduce the size of the feature expression space so as to reduce the network parameters and computational load and thereby control the fitting. The dimensionality-varied convolutional neural network mainly uses a max pooling operation

which is a non-linear operation

and improves the computational speed and robustness of feature extraction. The classification accuracy is higher when the classification model has many layers and convolution kernels. However

as the complexity of the model increases

the computational complexity increases as well. The dimensionality-varied convolutional neural network can be divided into spectral-spatial information fusion

dimension reduction

mixed feature extraction

and spectral-spatial classification according to the changes in the dimensions of internal feature maps. The process can ensure that the network can extract small-sized labeled sample features in a certain depth. In the feature extraction process of the dimensionality-varied convolutional neural network

the dimension of the internal feature map is changed instead of retaining the 3D structure

thereby reducing the required computation and storage space. This dimensionality-varied structure simplifies the network structure and reduces computational complexity by changing the dimension of feature mapping. The accuracy of the convolutional neural network for hyperspectral image classification of small-sized labeled samples is improved by fully extracting the spectral-spatial information.

Result

2

The experiment is divided into performance analysis and classification performance comparison of the dimensionality-varied convolutional neural network. The datasets used are the Indian Pines and Pavia University Scene datasets. Experiment results show that the dimensionality-varied convolutional neural network can achieve high classification accuracy for hyperspectral images with small-sized labeled samples. The selection of parameters in the dimensionality-varied convolutional neural network greatly influences classification accuracy. Experiments on batch size

threshold

dropout

and kernel number show that a reasonable parameter selection has an important impact on the classification performance of the algorithm. For the Indian Pines and Pavia University Scene datasets

the optimal classification performance is achieved when the batch sizes are set to 40 and 150

the thresholds are both set to 1×10

-15

the dropout values are both set to 0.8

and the kernel numbers are set to 8 and 5

respectively. The overall classification accuracies for the Indian Pines and Pavia University Scene datasets are 87.87% and 98.18%

respectively. Compared with other classification algorithms

the proposed algorithm has evident performance advantages. The combination of the two optimization methods

namely

spectral-spatial information and dimensionality reduction for feature maps

can effectively improve the classification of hyperspectral images with small-sized labeled samples.

Conclusion

2

Experimental results show that by changing the dimensions of feature maps in feature extraction

high-precision spectral-spatial feature extraction is realized

and the complexity of the model is reduced. Compared with other classification algorithms

the dimensionality-varied convolutional neural network can effectively improve the classification of small-sized labeled samples and greatly reduce the complexity of the model. Reasonable parameter optimization can effectively improve the classification accuracy of the dimensionality-varied convolutional neural network. This dimensionality-varied model can greatly improve the classification performance of small-sized labeled samples in hyperspectral images and can be further extended to other deep learning classification models related to hyperspectral images.