Objective

2

Terrain classification is an important research task in the field of earth observation using remote sensing technology. The hyperspectral image has rich spectral information; thus

it can be applied to the classification of remote sensing image. With the rapid development of the hyperspectral technology

the hyperspectral remote sensing image processing and analyzing technology has attracted wide attention of academia. The hyperspectral images have dozens or even hundreds of continuous narrow spectral bands compared with the traditional panchromatic band and multi-spectral remote sensing image

which provides detailed spectral and spatial feature information. Accordingly

these images have been widely used in various aspects

such as precision agriculture

city planning

and military defense. Hyperspectral images have high dimensional data

and redundancy and noise exist; thus

transformed data must be utilized for image processing. In the application of hyperspectral image classification

the manner by which to effectively represent the features of hyperspectral image is the most critical step in current studies. In this work

we propose an approach for hyperspectral image classification by using an inverted feature pyramid network and U-Net.

Method

2

The dimension of the hyperspectral remote sensing image data is high. Principal component analysis (PCA) method plays a significant role in transforming useful information in the images to the most important

k

characteristic

thus reducing the amount of data and enhancing the data features. After PCA

the data are segmented and collected by means of sliding window. The surrounding area of each pixel is defined as a patch

which is regarded as the input of the proposed network. The category of the pixel is the ground truth label. In the first stage

U-Net is used to extract spatial features of hyperspectral image at the pixel level. The left side of the network is the contraction path

which corresponds to the encoder part of the classic encoder-decoder. The right side of the network is the extension path

which can be regarded as a decoder. The feature maps in the extension path are the result of combining two parts of a feature map along two dimensions

making the acquired features more visible. In the first part

the feature maps from the same layer of contraction path and the feature maps from the upper layer of extension path are simultaneously fed to the attention mechanism. The feature region of this part has a higher weight value. The second part is obtained by deconvolution of the feature graph from the upper layer of the extension path. In a layered way

these feature maps with rich spatial information are fused with feature maps containing rich semantic information obtained by inverted feature pyramid network layers. Therefore

the obtained feature maps have reliable spatial and strong semantic information. Finally

the weight value of the effective features in the image is increased

and the region of irrelevant background is suppressed owing to the attention mechanism. Thus

the classification result of hyperspectral image is acquired.

Result

2

We conduct experiments to evaluate the effectiveness of the proposed method and attempt to investigate the influence of PCA retained principal component number and the size of input data for the performance of classification. We conduct contrast experiments on four publicly available hyperspectral image datasets to demonstrate the performance of the proposed method: Indian Pines

Pavia University

Salinas

and Urban. Experimental results show that the proposed method for hyperspectral image classification is effective

and the best PCA retained principal component numbers are 3

20

10

and 3. Meanwhile

the best input sizes of the proposed model are 64

32

32

and 64. We obtain 98.91%

99.85%

99.99%

and 87.43% overall classification accuracy rates

98.07%

99.39%

99.09%

and 78.30% average classification accuracy rates

and 0.987

0.998

0.999

and 0.831 Kappa values for the four hyperspectral image datasets

respectively

which are higher than those of the other classification algorithms.

Conclusion

2

Hyperspectral images are capable of accurately presenting the rich terrain information contained in the specific region with the help of hundreds of continuous and subdivided spectral bands; however

useless information exists in each spectral band. The mechanism by which to effectively extract the key terrain information from the data of hyperspectral images and utilize them for classification is the most important and difficult problem. We propose to combine U-Net and the inverted pyramid network for hyperspectral image classification. First

we reduce the dimension of hyperspectral image data with the help of PCA method. We adopt the method of sliding window to build patches after the data dimension is reduced. These patches are fed into the model. U-Net is regarded as the backbone of the proposed network

and it aims to extract the characteristics of a hyperspectral image. Then

the rich characteristics of the spatial information are fused with the features from the inverted pyramid network. Subsequently

the abundant spectral and spatial information is obtained. The utilization of attention mechanism allows the model to effectively focus on spectral and spatial information and reduce the influence of signal-to-noise to classification performance. Experimental results show that the proposed method can be applied to hyperspectral image classification tasks with limited training samples and achieve good classification results. The classification accuracy of a hyperspectral image can also be improved by properly handling the input data. In our future work

we will attempt to investigate the manner by which to make the model's structure less complex while maintaining high hyperspectral image classification performance with less training data samples.