Striping Noise Detection and Correction of Remote Sensing Images

Tsai, Fuan; Chen, Walter

doi:10.1109/tgrs.2008.2000646

Cited by 75 publications

(43 citation statements)

References 32 publications

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“…A avaliação dos métodos de atenuação dos efeitos de stripe nas imagens Hyperion revelou a necessidade de completa remoção das bandas ruidosas para posterior processamento, corroborando com os trabalhos de Goodenough et al (2003), Tsai et al (2008) et al (2010). Esse fato foi constatado pelo processamento das imagens a partir da classificação SFF (Spectral Feature Fitting) que identificou uma série de inconsistências visuais e estatísticas.…”

Section: Resultados E Discussão 31 Pré-processamento E Avaliaçãounclassified

Espacialização de sesquióxidos de ferro (Goethita e Hematita) em mancha de solo exposto por meio de sensoriamento remoto hiperespectral

2016

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RESUMO -Métodos convencionais para caracterização de solos são caros e demorados, o que abre espaço para novas técnicas, como o sensoriamento remoto. O objetivo do presente trabalho é testar o potencial do sensor Hyperion na espacialização de goethita e hematita em latossolos e testar a interferência de dois corretores atmosféricos nos valores obtidos. As imagens receberam três pré-processamentos: 1) dados brutos; 2) exclusão de bandas ruidosas; e 3) destriping. Após o pré-processamento realizou-se a análise por componentes principais, bem como correção atmosférica pelos métodos QUAC (Quick Atmospheric Correction) e FLAASH (Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubes). Posteriormente foi escolhido um local de estudo para a coleta de amostras georreferenciadas e sua análise de cor pela carta de Munsell. Os dados foram espacializados com uso do Spectral Feature Fitting (SFF), calculando-se o índice RHGtscale para a imagem. O pré-processamento que apresentou o melhor resultado foi a exclusão de bandas ruidosas, servindo de base para os processamentos posteriores. O índice RHGtscale obtido sob a cena corrigida com QUAC apresentou dados coerentes com a variabilidade de cores observada em campo. Novos trabalhos devem ser incentivados para melhor explorar a potencialidade do sensor Hyperion em estudos mineralógicos. Palavras-chave: Sesquióxidos de Ferro, Hyperion, Correção atmosférica, Spectral Feature Fitting.ABSTRACT -Conventional methods for the characterization of soils are expensive and time consuming, which makes room for new techniques, such as remote sensing. The aim of this study is to test the potential of Hyperion on the spatial distribution of goethite and hematite in Oxisols and test the interference of two atmospheric correction methods in the obtained values. The images received three preprocessing methods: 1) raw data; 2) excluding noisy bands; and 3) destriping. After preprocessing was carried out by principal component analysis, as well as methods for atmospheric correction with QUAC (Quick Atmospheric Correction) and FLAASH (Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubes). Later, we choose a study site for sample collection and analysis of georeferenced color by the Munsell color chart. The data were spatialized using the Spectral Feature Fitting (SFF), calculating the RHGtscale index for the image. The excluding noisy bands preprocessing method it was the best result, as a basis for further processing. Calculated RHGtscale index the scene corrected with QUAC presented data consistent with the field observed. New works about the Hyperion potentialities were stimulated to better knowledge in the mineralogical studies.

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Section: Resultados E Discussão 31 Pré-processamento E Avaliaçãounclassified

Espacialização de sesquióxidos de ferro (Goethita e Hematita) em mancha de solo exposto por meio de sensoriamento remoto hiperespectral

2016

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“…According to [4] the PSNR is a ratio between band maximum and standard deviation and can be considered as a contrast measurement. The second metric is the local Modified Structural Similarity Index (MSSIM) [17,45,46]. It combines local structure, luminance and contrast metrics and be considered as an overall image (band) quality index.…”

Section: For Artificially Degraded Images (Hymap)mentioning

confidence: 99%

Reduction of Uncorrelated Striping Noise—Applications for Hyperspectral Pushbroom Acquisitions

et al. 2014

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Hyperspectral images are of increasing importance in remote sensing applications. Imaging spectrometers provide semi-continuous spectra that can be used for physics based surface cover material identification and quantification. Preceding radiometric calibrations serve as a basis for the transformation of measured signals into physics based units such as radiance. Pushbroom sensors collect incident radiation by at least one detector array utilizing the photoelectric effect. Temporal variations of the detector characteristics that differ with foregoing radiometric calibration cause visually perceptible along-track stripes in the at-sensor radiance data that aggravate succeeding image-based analyses. Especially, variations of the thermally induced dark current dominate and have to be reduced. In this work, a new approach is presented that efficiently reduces dark current related stripe noise. It integrates an across-effect gradient minimization principle. The performance has been evaluated using artificially degraded whiskbroom (reference) and real pushbroom OPEN ACCESS Remote Sens. 2014, 6 11083 acquisitions from EO-1 Hyperion and AISA DUAL that are significantly covered by stripe noise. A set of quality indicators has been used for the accuracy assessment. They clearly show that the new approach outperforms a limited set of tested state-of-the-art approaches and achieves a very high accuracy related to ground-truth for selected tests. It may substitute recent algorithms in the Reduction of Miscalibration Effects (ROME) framework that is broadly used to reduce radiometric miscalibrations of pushbroom data takes.

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“…In this work the same metrics are used. Those were the global Peak-Signal-to-Noise-Ratio (PSNR) (Rogass et al, 2010;Wang and Bovik, 2009), the global Shannon Entropy (Rogass et al, 2010, Frank andSmith, 2010) and the local Modified Structural Similarity Index (MSSIM) (Tsai and Chen, 2008;Bovik, 2009, Wang et al, 2004). In case of available ground truth as for the HyMAP scene the metrics were applied on the result and on ground truth.…”

Section: Image Quality Metricsmentioning

confidence: 99%

“…In the literature, specific approaches for destriping of slope stripes, offset stripes or both exist, and these are primarily based on methods such as interpolation (Oliveira and Gomes, 2010;Tsai and Chen, 2008), local or global image moments (Datt et al, 2003;Cavalli et al, 2008;Le Maire et al, 2008;Liu et al, 2009), filtering (Garcia andMoreno, 2004;Shen et al, 2008;Simpson et al, 1995, Simpson et al, 1998 or complex image statistics of log transformed slopes (Bouali and Ladjal, 2010;Carfantan and Idier, 2010;Gomez-Chova et al, 2008). Most methods replace original, miscalibrated radiances.…”

Section: Introductionmentioning

confidence: 99%

A Contribution to the Reduction of Radiometric Miscalibration of Pushbroom Sensors

Rogaß¹,

Spengler²,

Bochow³

et al. 2012

Remote Sensing - Advanced Techniques and Platforms

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Striping Noise Detection and Correction of Remote Sensing Images

Cited by 75 publications

References 32 publications

Espacialização de sesquióxidos de ferro (Goethita e Hematita) em mancha de solo exposto por meio de sensoriamento remoto hiperespectral

Espacialização de sesquióxidos de ferro (Goethita e Hematita) em mancha de solo exposto por meio de sensoriamento remoto hiperespectral

Reduction of Uncorrelated Striping Noise—Applications for Hyperspectral Pushbroom Acquisitions

A Contribution to the Reduction of Radiometric Miscalibration of Pushbroom Sensors

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