A Robust Recoverable Algorithm Used for Digital Speech Forensics Based on DCT

Liu, Zhenghui; Li, Yanli; Sun, Fuchun; He, Jingsha; Qi, Chuanda; Luo, Da

doi:10.1007/978-3-030-00021-9_28

Cited by 2 publications

(1 citation statement)

References 21 publications

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“…As a discrete Fourier transform at real number domain, DCT is often used to integrate and compress data from different sources [31][32][33], which can effectively retain the main information and features of the dataset and own the characteristics of the fast calculation [34]. The DCT-PLS plus Generalized Cross-Validation (GCV) criteria have been proved effective in data denoising and reconstruction both from a theoretical and computational level [35][36][37][38]. For the application of reconstructing satellite data, Wang et al introduced a three-dimensional method based on DCT-PLS to reconstruct the global soil moisture dataset with high accuracy [39], which was also suggested in other reconstruction applications of soil moisture [40,41].…”

Section: Introductionmentioning

confidence: 99%

Filling Gaps of Monthly Terra/MODIS Daytime Land Surface Temperature Using Discrete Cosine Transform Method

Liu

Jiang

et al. 2020

Remote Sensing

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Land surface temperature (LST) is a key parameter in geophysical fields. The Moderate Resolution Imaging Spectroradiometer (MODIS) onboard Terra provides an accurate LST dataset with global coverage and monthly series, but the monthly MODIS LST data are often obscured by clouds and other atmospheric disturbances and consequently exhibit significant data gaps at a global scale, resulting in a difficult interpretation of LST trends and climatological characteristics. In this paper, an effective and fast LST reconstruction method to fill data gaps in monthly MODIS LST is presented. The proposal combines the Discrete Cosine Transform (DCT) and the Penalized Least Square approach (PLS) together with the Generalized Cross-Validation (GCV) criterion. It depends only on the spatial high-frequency information from original LST estimates and allows a fast and automatic filling process without the help of any other ancillary data. To analyze its performance, the method is applied to fill data gaps on three continents with synthetic random missing values introduced as validation sets. The statistical evaluation shows that this method is capable of filling a large number of missing values in MODIS LST datasets with very high accuracy. In addition, the trend differences between the original LST and reconstructed LST have assessed the significance by computing 95% confidence intervals for a time series of trend differences is examined. Simulated experiments show that data gaps with large missing counts lead to significant differences in trend patterns and the patterns on validation sets are well estimated by this method, which confirms that the filling process of MODIS LST is necessary and favorable results can be produced for substantial data gaps by the DCT-PLS method.

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