Impact of On-Board Hyperspectral Data Compression on Mineral Mapping Products

Hyperspectral imaging (HSI) technology has been used for various remote sensing applications due to its excellent capability of monitoring regions-of-interest over a period of time. However, the large data volume of four-dimensional multitemporal hyperspectral imagery demands massive data compression techniques. While conventional 3D hyperspectral data compression methods exploit only spatial and spectral correlations, we propose a simple yet effective predictive lossless compression algorithm that can achieve significant gains on compression efficiency, by also taking into account temporal correlations inherent in the multitemporal data. We present an information theoretic analysis to estimate potential compression performance gain with varying configurations of context vectors. Extensive simulation results demonstrate the effectiveness of the proposed algorithm. We also provide in-depth discussions on how to construct the context vectors in the prediction model for both multitemporal HSI and conventional 3D HSI data.

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Hyperspectral Data Compression by Using Rational Function Curve Fitting in Spectral Signature Subintervals

2018 11th International Symposium on Communication Systems, Networks & Digital Signal Processing (CSNDSP) ◽

10.1109/csndsp.2018.8471809 ◽

2018 ◽

Cited By ~ 1

Author(s):

Mersedeh Beitollahi ◽

S. Abolfazl Hosseini

Keyword(s):

Data Compression ◽

Rational Function ◽

Curve Fitting ◽

Hyperspectral Data ◽

Spectral Signature ◽

Fitting In

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Hyperspectral data compression based upon the principal component analysis

Computer Optics ◽

10.18287/2412-6179-co-806 ◽

2021 ◽

Vol 45 (2) ◽

pp. 235-244

Author(s):

A.S. Minkin ◽

O.V. Nikolaeva ◽

A.A. Russkov

Keyword(s):

Principal Component Analysis ◽

Data Compression ◽

Principal Component ◽

Component Analysis ◽

Hyperspectral Data ◽

Gas Absorption ◽

Absorption Bands ◽

Spectral Bands ◽

The Mean ◽

Retrieval Errors

The paper is aimed at developing an algorithm of hyperspectral data compression that combines small losses with high compression rate. The algorithm relies on a principal component analysis and a method of exhaustion. The principal components are singular vectors of an initial signal matrix, which are found by the method of exhaustion. A retrieved signal matrix is formed in parallel. The process continues until a required retrieval error is attained. The algorithm is described in detail and input and output parameters are specified. Testing is performed using AVIRIS data (Airborne Visible-Infrared Imaging Spectrometer). Three images of differently looking sky (clear sky, partly clouded sky, and overcast skies) are analyzed. For each image, testing is performed for all spectral bands and for a set of bands from which high water-vapour absorption bands are excluded. Retrieval errors versus compression rates are presented. The error formulas include the root mean square deviation, the noise-to-signal ratio, the mean structural similarity index, and the mean relative deviation. It is shown that the retrieval errors decrease by more than an order of magnitude if spectral bands with high gas absorption are disregarded. It is shown that the reason is that weak signals in the absorption bands are measured with great errors, leading to a weak dependence between the spectra in different spatial pixels. A mean cosine distance between the spectra in different spatial pixels is suggested to be used to assess the image compressibility.

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Exploitation-Based Hyperspectral Data Compression

Hyperspectral Data Processing ◽

10.1002/9781118269787.ch19 ◽

2013 ◽

pp. 545-580

Keyword(s):

Data Compression ◽

Hyperspectral Data

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A Combined Quantitative Evaluation Model for the Capability of Hyperspectral Imagery for Mineral Mapping

Sensors ◽

10.3390/s19020328 ◽

2019 ◽

Vol 19 (2) ◽

pp. 328 ◽

Cited By ~ 3

Author(s):

Na Li ◽

Xinchen Huang ◽

Huijie Zhao ◽

Xianfei Qiu ◽

Kewang Deng ◽

...

Keyword(s):

Remote Sensing ◽

Statistical Analysis ◽

Imaging System ◽

Evaluation Model ◽

Spectral Response ◽

Influence Factors ◽

Hyperspectral Data ◽

Statistical Characteristics ◽

Mineral Mapping ◽

Proposed Model

To analyze the influence factors of hyperspectral remote sensing data processing, and quantitatively evaluate the application capability of hyperspectral data, a combined evaluation model based on the physical process of imaging and statistical analysis was proposed. The normalized average distance between different classes of ground cover is selected as the evaluation index. The proposed model considers the influence factors of the full radiation transmission process and processing algorithms. First- and second-order statistical characteristics (mean and covariance) were applied to calculate the changes for the imaging process based on the radiation energy transfer. The statistical analysis was combined with the remote sensing process and the application performance, which consists of the imaging system parameters and imaging conditions, by building the imaging system and processing models. The season (solar zenith angle), sensor parameters (ground sampling distance, modulation transfer function, spectral resolution, spectral response function, and signal to noise ratio), and number of features were considered in order to analyze the influence factors of the application capability level. Simulated and real data collected by Hymap in the Dongtianshan area (Xinjiang Province, China), were used to estimate the proposed model’s performance in the application of mineral mapping. The predicted application capability of the proposed model is consistent with the theoretical analysis.

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