An approach to near-lossless hyperspectral data compression using deep autoencoder

Kuester, Jannick; Gross, Wolfgang L.; Middelmann, Wolfgang

doi:10.1117/12.2571302

Cited by 5 publications

(5 citation statements)

References 11 publications

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“…The second comparison method is the Deep Autoencoder (DAE). 46 It is a deep neural network underlying an Autoencoder structure. It uses only fully connected layers to reduce the spectral dimension.…”

Section: Comparison Compression Methodsmentioning

confidence: 99%

Investigating the influence of hyperspectral data compression on spectral unmixing

Kuester

Anastasiadis

Middelmann

et al. 2022

Image and Signal Processing for Remote Sensing XXVIII

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This work addresses the problem of hyperspectral data compression and the evaluation of the reconstruction quality for different compression rates. Data compression is intended to transmit the enormous amount of data created by hyperspectral sensors efficiently. The information loss due to the compression process is evaluated by the complex task of spectral unmixing. We propose an improved 1D-Convolutional Autoencoder architecture with different compression rates for lossy hyperspectral data compression. Furthermore, we evaluate the reconstruction by applying metrics such as SNR and SA and compare them to the spectral unmixing results.

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Section: Comparison Compression Methodsmentioning

confidence: 99%

Investigating the influence of hyperspectral data compression on spectral unmixing

Kuester

Anastasiadis

Middelmann

et al. 2022

Image and Signal Processing for Remote Sensing XXVIII

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“…To evaluate reconstruction accuracy of the 1D-CAE model, the results are compared with the DAE and the NLPCA method on the reconstructed data. The DAE model was introduced in (Kuester et al, 2020) and shows a high level of reconstruction accuracy, assessed using various metrics, and in target detection under challenging conditions even for sub-pixel targets.…”

Section: Methodsmentioning

confidence: 99%

“…Due to the non-linear properties of the NLPCA and its fixed number of components, the information of the original spectrum is obtained and distributed equally among all components. 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. The reconstruction accuracy is evaluated using the Signal to Noise Ratio (SNR) and the Spectral Angle Mapper (SAM).…”

Section: Introductionmentioning

confidence: 99%

“…To find relevant information and retain only the essential features for the compression, the model uses a convolutional layer and a max pooling layer. 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). The Signal to Noise Ratio (SNR) and the Spectral Angle Mapper (SAM) are used to calculate the spectral similarity between the reconstructed data set and the original.…”

Section: Introductionmentioning

confidence: 99%

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1d-Convolutional Autoencoder Based Hyperspectral Data Compression

Kuester

Gross

Middelmann

2021

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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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.

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“…This outperforms classic methods such as DWT + RLE (Discrete Wavelet Transform + Run Length Encoding) and DCT + RLE + Huffman. Similarly, Kuester et al [ 41 ] utilized a deep autoencoder to compress a representative set of spectral data in 2020, achieving a compression ratio of four with an almost lossless compression process.…”

Section: Introductionmentioning

confidence: 99%

A Data Compression Method for Wellbore Stability Monitoring Based on Deep Autoencoder

Song,

Zhao,

Zhang

et al. 2024

Sensors

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The compression method for wellbore trajectory data is crucial for monitoring wellbore stability. However, classical methods like methods based on Huffman coding, compressed sensing, and Differential Pulse Code Modulation (DPCM) suffer from low real-time performance, low compression ratios, and large errors between the reconstructed data and the source data. To address these issues, a new compression method is proposed, leveraging a deep autoencoder for the first time to significantly improve the compression ratio. Additionally, the method reduces error by compressing and transmitting residual data from the feature extraction process using quantization coding and Huffman coding. Furthermore, a mean filter based on the optimal standard deviation threshold is applied to further minimize error. Experimental results show that the proposed method achieves an average compression ratio of 4.05 for inclination and azimuth data; compared to the DPCM method, it is improved by 118.54%. Meanwhile, the average mean square error of the proposed method is 76.88, which is decreased by 82.46% when compared to the DPCM method. Ablation studies confirm the effectiveness of the proposed improvements. These findings highlight the efficacy of the proposed method in enhancing wellbore stability monitoring performance.

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An approach to near-lossless hyperspectral data compression using deep autoencoder

Cited by 5 publications

References 11 publications

Investigating the influence of hyperspectral data compression on spectral unmixing

Investigating the influence of hyperspectral data compression on spectral unmixing

1d-Convolutional Autoencoder Based Hyperspectral Data Compression

A Data Compression Method for Wellbore Stability Monitoring Based on Deep Autoencoder

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