Effective compression of hyperspectral imagery using an improved 3D DCT approach for land-cover analysis in remote-sensing applications

Qiao, Tong; Ren, Jinchang; Sun, Meijun; Zheng, Jiangbin; Marshall, Stephen

doi:10.1080/01431161.2014.968682

Cited by 22 publications

(30 citation statements)

References 69 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Hyperspectral imagery has been widely used in the detection field [1] and recent advances in remote sensing technology have made it more prevalent [2]. Hyperspectral image with high spectral resolution provides the potential for more accurate classification accuracy [3, 4]; however, the high dimensionality of these hyperspectral datasets makes it difficult to identify samples with class labels [5, 6]. Moreover, the high‐frequency noise in these datasets also increases this difficulty [7].…”

Section: Introductionmentioning

confidence: 99%

Combined FATEMD‐based band selection method for hyperspectral images

Zhang

Shen

2019

IET Image Processing

View full text Add to dashboard Cite

Section: Introductionmentioning

confidence: 99%

Combined FATEMD‐based band selection method for hyperspectral images

Zhang

Shen

2019

IET Image Processing

View full text Add to dashboard Cite

“…In addition to these applications, one of the most active areas of HSI is remote sensing, where researchers develop diverse algorithms based on it, e.g. target detection for military surveillance [5], data compression for faster transmission [6,7], surface and data classification for land-cover analysis [8][9][10][11][12]. However, due to the characteristics of HSI, data redundancy is inevitable.…”

Section: Introductionmentioning

confidence: 99%

Effective Denoising and Classification of Hyperspectral Images Using Curvelet Transform and Singular Spectrum Analysis

Qiao

Ren

Wang

et al. 2017

IEEE Trans. Geosci. Remote Sensing

Self Cite

118

View full text Add to dashboard Cite

Hyperspectral imaging (HSI) classification has become a popular research topic in recent years, and effective feature extraction is an important step before the classification task. Traditionally, spectral feature extraction techniques are applied to the HSI data cube directly. This paper presents a novel algorithm for HSI feature extraction by exploiting the curvelet transformed domain via a relatively new spectral feature processing technique -singular spectrum analysis (SSA). Although the wavelet transform has been widely applied for HSI data analysis, the curvelet transform is employed in this paper since it is able to separate image geometric details and background noise effectively. Using the support vector machine (SVM) classifier, experimental results have shown that features extracted by SSA on curvelet coefficients have better performance in terms of classification accuracies over features extracted on wavelet coefficients. Since the proposed approach mainly relies on SSA for feature extraction on the spectral dimension, it actually belongs to the spectral feature extraction category. Therefore, the proposed method has also been compared with some state-of-the-art spectral feature extraction techniques to show its efficacy. In addition, it has been proven that the proposed method is able to remove the undesirable artefacts introduced during the data acquisition process as well. By adding an extra spatial post-processing step to the classified map achieved using the proposed approach, we have shown that the classification performance is comparable with several recent spectral-spatial classification methods.Index Terms-Hyperspectral imaging (HSI), the curvelet transform, singular spectrum analysis (SSA), classification, support vector machine (SVM), spatial post-processing.Manuscript received on October xx, 2015. This work is partially funded by the following projects:

show abstract

“…In 3D DCT 8 × 8 × 8, a sub-cube is applied. A number of improved 3D-DCT based approaches are proposed in [ 21 ][ 22 ]. Distributed source coding (DSC) [ 23 – 25 ] schemes have received more attention due to their low complexity and error resilience which satisfy the requirements of onboard compression systems.…”

Section: Introductionmentioning

confidence: 99%

Reflectance Prediction Modelling for Residual-Based Hyperspectral Image Coding

et al. 2016

View full text Add to dashboard Cite

A Hyperspectral (HS) image provides observational powers beyond human vision capability but represents more than 100 times the data compared to a traditional image. To transmit and store the huge volume of an HS image, we argue that a fundamental shift is required from the existing “original pixel intensity”-based coding approaches using traditional image coders (e.g., JPEG2000) to the “residual”-based approaches using a video coder for better compression performance. A modified video coder is required to exploit spatial-spectral redundancy using pixel-level reflectance modelling due to the different characteristics of HS images in their spectral and shape domain of panchromatic imagery compared to traditional videos. In this paper a novel coding framework using Reflectance Prediction Modelling (RPM) in the latest video coding standard High Efficiency Video Coding (HEVC) for HS images is proposed. An HS image presents a wealth of data where every pixel is considered a vector for different spectral bands. By quantitative comparison and analysis of pixel vector distribution along spectral bands, we conclude that modelling can predict the distribution and correlation of the pixel vectors for different bands. To exploit distribution of the known pixel vector, we estimate a predicted current spectral band from the previous bands using Gaussian mixture-based modelling. The predicted band is used as the additional reference band together with the immediate previous band when we apply the HEVC. Every spectral band of an HS image is treated like it is an individual frame of a video. In this paper, we compare the proposed method with mainstream encoders. The experimental results are fully justified by three types of HS dataset with different wavelength ranges. The proposed method outperforms the existing mainstream HS encoders in terms of rate-distortion performance of HS image compression.

show abstract

Effective compression of hyperspectral imagery using an improved 3D DCT approach for land-cover analysis in remote-sensing applications

Cited by 22 publications

References 69 publications

Combined FATEMD‐based band selection method for hyperspectral images

Combined FATEMD‐based band selection method for hyperspectral images

Effective Denoising and Classification of Hyperspectral Images Using Curvelet Transform and Singular Spectrum Analysis

Reflectance Prediction Modelling for Residual-Based Hyperspectral Image Coding

Contact Info

Product

Resources

About