Exploiting spatiospectral correlation for impulse denoising in hyperspectral images

Aggarwal, Hemant Kumar; Majumdar, Angshul

doi:10.1117/1.jei.24.1.013027

Cited by 22 publications

(15 citation statements)

References 26 publications

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“…Sparsity techniques or sparse and low-rank decomposition techniques have been used to remove sparse noise from the signal. In [238], impulsive noise was removed using an 1 -norm for both penalty and data fidelity terms in the minimization problem suggested.…”

Section: A Noise Source Assumptions In Hsimentioning

confidence: 99%

Advances in Hyperspectral Image and Signal Processing: A Comprehensive Overview of the State of the Art

Ghamisi

Yokoya

et al. 2017

IEEE Geosci. Remote Sens. Mag.

629

266

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Recent advances in airborne and spaceborne hyperspectral imaging technology have provided end users with rich spectral, spatial, and temporal information, which make a plethora of applications for the analysis of large areas of the Earth surface feasible. However, a huge number of factors, such as high dimensions and size of the hyperspectral data, the lack of training samples, mixed pixels, light scattering mechanisms in the acquisition process, and different atmospheric and geometric distortions, make such data inherently nonlinear and complex, which poses extreme challenges for existing methodologies to effectively process and analyze the data sets. Hence, rigorous and innovative methodologies are required for hyperspectral image and signal processing and have become a center of attention for researchers worldwide. This paper offers a comprehensive tutorial/overview focusing specifically on hyperspectral data analysis, which is categorized into seven broad topics: classification, spectral unmixing, dimensionality reduction, resolution enhancement, hyperspectral image denoising and restoration, change detection, and fast computing. For each topic, we provide a synopsis of the state-of-the-art approaches and numerical results for validation and evaluation of different methodologies, followed by a discussion of future challenges and research directions.

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Section: A Noise Source Assumptions In Hsimentioning

confidence: 99%

Advances in Hyperspectral Image and Signal Processing: A Comprehensive Overview of the State of the Art

Ghamisi

Yokoya

et al. 2017

IEEE Geosci. Remote Sens. Mag.

629

266

View full text Add to dashboard Cite

show abstract

“…Sparsity techniques or sparse and low-rank decomposition techniques are used to remove sparse noise from the signal. In [19], impulse noise is removed by using an 1 -norm for both penalty and data fidelity terms in the proposed minimization problem.…”

Section: Sparse Noisementioning

confidence: 99%

Noise Reduction in Hyperspectral Imagery: Overview and Application

et al. 2018

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Hyperspectral remote sensing is based on measuring the scattered and reflected electromagnetic signals from the Earth's surface emitted by the Sun. The received radiance at the sensor is usually degraded by atmospheric effects and instrumental (sensor) noises which include thermal (Johnson) noise, quantization noise, and shot (photon) noise. Noise reduction is often considered as a preprocessing step for hyperspectral imagery. In the past decade, hyperspectral noise reduction techniques have evolved substantially from two dimensional bandwise techniques to three dimensional ones, and varieties of low-rank methods have been forwarded to improve the signal to noise ratio of the observed data. Despite all the developments and advances, there is a lack of a comprehensive overview of these techniques and their impact on hyperspectral imagery applications. In this paper, we address the following two main issues; (1) Providing an overview of the techniques developed in the past decade for hyperspectral image noise reduction; (2) Discussing the performance of these techniques by applying them as a preprocessing step to improve a hyperspectral image analysis task, i.e., classification. Additionally, this paper discusses about the hyperspectral image modeling and denoising challenges. Furthermore, different noise types that exist in hyperspectral images have been described. The denoising experiments have confirmed the advantages of the use of low-rank denoising techniques compared to the other denoising techniques in terms of signal to noise ratio and spectral angle distance. In the classification experiments, classification accuracies have improved when denoising techniques have been applied as a preprocessing step.

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“…This algorithm takes into account both spectral and spatial correlation for denoising of hyperspectral imaging. GAP algorithm has been compared against recent denoising methods and its superiority in terms of peak signal to noise ratio (PSNR) over another methods has shown in Aggarwal and Majumdar [8].…”

Section: Gap Algorithmmentioning

confidence: 99%

“…Reducing impulse noise from the image has been addressed problem [8]. In this paper, reducing impulse noise with General Analysis Prior (GAP) algorithm as a preprocessing step for hyperspectral unmixing is considered.…”

Section: Introductionmentioning

confidence: 99%