A SAR image compression algorithm based on Mallat tower-type wavelet decomposition

Li, Jia; Chang, Liping

doi:10.1016/j.ijleo.2015.07.196

Cited by 20 publications

(4 citation statements)

References 5 publications

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“…It is assumed that a conjugate mirror filter is produced by the orthogonal scaling function and wavelet function [ 29 , 30 ]. The scaling coefficients and wavelet coefficients of the WT as c j,k and d j,k , and the recursive formulas are shown in Equations (4) and (5), which enable the calculation { c j,k , d j,k } [ 31 ]. Thus, with the initial sequence { c j,k } k ∈Z in space V j , we can calculate all the scale coefficients and wavelet coefficients of any spaces V j (j < J).…”

Section: Methodsmentioning

confidence: 99%

Modeling of Chromium, Copper, Zinc, Arsenic and Lead Using Portable X-ray Fluorescence Spectrometer Based on Discrete Wavelet Transform

Wang

2017

IJERPH

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A modeling method based on discrete wavelet transform (DWT) was introduced to analyze the concentration of chromium, copper, zinc, arsenic and lead in soil with a portable X-ray fluorescence (XRF) spectrometer. A total of 111 soil samples were collected and observed. Denoising and baseline correction were performed on each spectrum before modeling. The optimum conditions for pre-processing were denoising with Coiflet 3 on the 3rd level and baseline correction with Coiflet 3 on the 9th level. Calibration curves were established for the five heavy metals (HMs). The detection limits were compared before and after the application of DWT, the qualitative detection limits and the quantitative detection limits were calculated to be three and ten times as high as the standard deviation with silicon dioxide (blank), respectively. The results showed that the detection limits of the instrument using DWT were lower, and that they were below national soil standards; the determination coefficients (R2) based on DWT-processed spectra were higher, and ranged from 0.990 to 0.996, indicating a high degree of linearity between the contents of the HMs in soil and the XRF spectral characteristic peak intensity with the instrument measurement.

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Section: Methodsmentioning

confidence: 99%

Modeling of Chromium, Copper, Zinc, Arsenic and Lead Using Portable X-ray Fluorescence Spectrometer Based on Discrete Wavelet Transform

Wang

2017

IJERPH

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“…e signal is decomposed into an approximate signal of a large time scale and sets the detail signal at different small scales by wavelet transform. Mallat algorithm [30,31] is used to decompose the original high-frequency component sequence. Considering that Daubechies wavelet has compact support and regularity, the information energy of the nonstationary random load sequence can be relatively concentrated after decomposition.…”

Section: Input Gatementioning

confidence: 99%

Short-Term Load Forecasting Based on Frequency Domain Decomposition and Deep Learning

Zhang

et al. 2020

Mathematical Problems in Engineering

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In this paper, we focus on the accuracy improvement of short-term load forecasting, which is useful in the reasonable planning and stable operation of the system in advance. For this purpose, a short-term load forecasting model based on frequency domain decomposition and deep learning is proposed. The original load data are decomposed into four parts as the daily and weekly periodic components and the low- and high-frequency components. Long short-term memory (LSTM) neural network is applied in the forecasting for the daily periodic, weekly periodic, and low-frequency components. The combination of isolation forest (iForest) and Mallat with the LSTM method is constructed in forecasting the high-frequency part. Finally, the four parts of the forecasting results are added together. The actual load data of a Chinese city are researched. Compared with the forecasting results of empirical mode decomposition- (EMD-) LSTM, LSTM, and recurrent neural network (RNN) methods, the proposed method can effectively improve the accuracy and reduce the degree of dispersion of forecasting and actual values.

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“…The results indicated that, under equivalent compression ratios, the JPEG2000 method exhibited a performance comparable to SPIHT [22]. Li and Chang optimized the wavelet transform and proposed an enhanced SAR image compression model based on tower-shaped wavelet decomposition [23]. Subsequently, Zemliachenko et al developed a compression ratio prediction algorithm for a discrete cosine transform (DCT) encoder, utilizing remote sensing images.…”

Section: Introductionmentioning

confidence: 99%

“…In practical applications, most models often overlook errors at data input [21][22][23][24][25][26][27][28][29][30][31][35][36][37][38][39][40]. Commonly, traditional techniques such as linear stretching are employed for quantization processing [41].…”

Section: Introductionmentioning

confidence: 99%

Synthetic Aperture Radar Image Compression Based on Low-Frequency Rejection and Quality Map Guidance

Deng,

Huang

2024

Remote Sensing

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Synthetic Aperture Radar (SAR) images are widely utilized in the field of remote sensing. However, there is a limited body of literature specifically addressing the compression of SAR learning images. To address the escalating volume of SAR image data for storage and transmission, which necessitates more effective compression algorithms, this paper proposes a novel framework for compressing SAR images. Experimental validation is performed using a representative low-resolution Sentinel-1 dataset and the high-resolution QiLu-1 dataset. Initially, we introduce a novel two-stage transformation-based approach aimed at suppressing the low-frequency components of the input data, thereby achieving a high information entropy and minimizing quantization losses. Subsequently, a quality map guidance image compression algorithm is introduced, involving the fusion of the input SAR images with a target-aware map. This fusion involves convolutional transformations to generate a compact latent representation, effectively exploring redundancies between focused and non-focused areas. To assess the algorithm’s performance, experiments are carried out on both the low-resolution Sentinel-1 dataset and the high-resolution QiLu-1 dataset. The results indicate that the low-frequency suppression algorithm significantly outperforms traditional processing algorithms by 3–8 dB when quantifying the input data, effectively preserving image features and improving image performance metrics. Furthermore, the quality map guidance image compression algorithm demonstrates a superior performance compared to the baseline model.

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A SAR image compression algorithm based on Mallat tower-type wavelet decomposition

Cited by 20 publications

References 5 publications

Modeling of Chromium, Copper, Zinc, Arsenic and Lead Using Portable X-ray Fluorescence Spectrometer Based on Discrete Wavelet Transform

Modeling of Chromium, Copper, Zinc, Arsenic and Lead Using Portable X-ray Fluorescence Spectrometer Based on Discrete Wavelet Transform

Short-Term Load Forecasting Based on Frequency Domain Decomposition and Deep Learning

Synthetic Aperture Radar Image Compression Based on Low-Frequency Rejection and Quality Map Guidance

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