Abstract
Abstract. Hyperspectral sensor technology has been advancing in recent years and become more practical to tackle a variety of applications. The arising issues of data transmission and storage can be addressed with the help of compression. To minimize the loss of important information, high spectral correlation between adjacent bands is exploited. In this paper, we introduce an approach to compress hyperspectral data based on a 1D-Convolutional Autoencoder. Compression is achieved through reducing correlation by transforming the spectral signature into a low-dimensional space, while simultaneously preserving the significant features. The focus lies on compression of the spectral dimension. The spatial dimension is not used in the compression in order not to falsify correlation between the spectral dimension and accuracy of the reconstruction. The proposed 1D-Convolutional Autoencoder efficiently finds and extracts features relevant for compression. Additionally, it can be exploited as a feature extractor or for dimensionality reduction. The hyperspectral data sets Greding Village and Pavia University were used for the training and the evaluation process. The reconstruction accuracy is evaluated using the Signal to Noise Ratio and the Spectral Angle. Additionally, a land cover classification using a multi-class Support Vector Machine is used as a target application. The classification performance of the original and reconstructed data are compared. The reconstruction accuracy of the 1D-Convolutional Autoencoder outperforms the Deep Autoencoder and Nonlinear Principal Component Analysis for the used metrics and for both data sets using a fixed compression ratio.
Highlights
Hyperspectral sensors measures the reflected electromagnetic spectrum of a territory in hundreds of narrow and contiguous wavelength intervals, referred to as bands
Due to the increasing demand for real-time processing of hyperspectral data and industry-related applications, the enormous amounts of data resulting from the high spectral dimensionality by the multiple spectral channels must be addressed
In (Licciardi et al, 2014) and (Licciardi and Chanussot, 2018) a Nonlinear Principal Component Analysis (NLPCA) based multilayer perceptron with an autoencoder architecture is implemented to compress the spectral dimension of hyperspectral data
Summary
Hyperspectral sensors measures the reflected electromagnetic spectrum of a territory in hundreds of narrow and contiguous wavelength intervals, referred to as bands. In (Licciardi et al, 2014) and (Licciardi and Chanussot, 2018) a Nonlinear Principal Component Analysis (NLPCA) based multilayer perceptron with an autoencoder architecture is implemented to compress the spectral dimension of hyperspectral data. In (Kuester et al, 2020), a Deep Autoencoder is used to compress the spectral dimension by reducing the correlation of the hyperspectral data while maintaining significant features with a focus on minimizing the reconstruction error. This paper investigates the compression performance and the reconstruction accuracy for the spectral dimension using a 1D-Convolutional Autoencoder (1D-CAE). The accuracy of the reconstructed data from the proposed model is compared to the results from the Deep Autoencoder (DAE) (Kuester et al, 2020) and the NLPCA method (Licciardi and Chanussot, 2018).
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