Impact of different compression rates for hyperspectral data compression based on a convolutional autoencoder

Abstract

This work addresses the problem of hyperspectral data compression and compares the reconstruction accuracy for different compression rates. Through data compression, the enormous amount of data created by hyperspectral sensors can be transmitted effectively. Remote sensing-related applications such as disaster management, land cover classification, and object recognition are improved with real-time information. We propose a 1D-Convolutional Autoencoder structure for lossy hyperspectral data compression and the necessary adjustments for realizing compression ratios of C_R = 4, C_R = 8, C_R = 16, and C_R = 32. Unlike many other contributions, we not only evaluate the reconstruction accuracy based on standard metrics like Signal to Noise Ratio and Spectral Angle Mapper but also on a target application, namely land cover classification. The reconstruction accuracy of the 1D-Convolutional Autoencoder is compared to machine learning-based lossy compression methods, namely Deep Autoencoder, Nonlinear Principal Component Analysis, and the Principal Component Analysis. The compression performances are compared using two data sets with different amounts of spectral signatures. The 1D-Convolutional Autoencoder performance surpasses the benchmark methods for all compression rates using the standard metrics. In addition, the 1D-Convolutional Autoencoder achieves the highest classification results for the land cover classification for all compression rates and is able to compress hyperspectral data efficiently. Furthermore, the robustness and generalization capability of the 1D-Convolutional Autoencoder is demonstrated by using unknown data for the evaluation.