Scene-based correction of fixed pattern noise in hyperspectral image data using temporal reordering

Ratliff, Bradley M.; Kaufman, Jason R.

doi:10.1117/1.oe.54.9.093102

Cited by 5 publications

(2 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We utilized the same subset of four images from [4] but processed them with an improved scene-based nonuniformity correction algorithm and performed ELM atmospheric compensation. 13 The native GSD of this imagery was near 40cm, but we also produced 100cm geo-corrected imagery for direct comparisons with the ProSpecTIR-VS imagery that was taken at 100cm.…”

Section: Experiments Methodologymentioning

confidence: 99%

On the effects of spatial and spectral resolution on spatial-spectral target detection in SHARE 2012 and Bobcat 2013 hyperspectral imagery

et al. 2015

Self Cite

View full text Add to dashboard Cite

Previous work with the Bobcat 2013 data set 1 showed that spatial-spectral feature extraction on visible to near infrared (VNIR) hyperspectral imagery (HSI) led to better target detection and discrimination than spectralonly techniques; however, the aforementioned study could not consider the possible benefits of the shortwaveinfrared (SWIR) portion of the spectrum due to data limitations. In addition, the spatial resolution of the Bobcat 2013 imagery was fixed at 8cm without exploring lower spatial resolutions. In this work, we evaluate the tradeoffs in spatial and spectral resolution and spectral coverage between for a common set of targets in terms of their effects on spatial-spectral target detection performance. We show that for our spatial-spectral target detection scheme and data sets, the adaptive cosine estimator (ACE) applied to S-DAISY and pseudo Zernike moment (PZM) spatial-spectral features can distinguish between targets better than ACE applied only to the spectral imagery. In particular, S-DAISY operating on bands uniformly selected from the SWIR portion of ProSpecTIR-VS sensor imagery in conjunction with bands closely corresponding to the Airborne Real-time Cueing Hyperspectral Reconnaissance (ARCHER) sensor's VNIR bands (80 total) led to the best overall average perfomance in both target detection and discrimination.

show abstract

Section: Experiments Methodologymentioning

confidence: 99%

On the effects of spatial and spectral resolution on spatial-spectral target detection in SHARE 2012 and Bobcat 2013 hyperspectral imagery

et al. 2015

Self Cite

View full text Add to dashboard Cite

show abstract

“…In images from military optical systems, general noise can be corrected using nonuniformity correction (NUC) and the neighbor pixels duplication (NPD) technique, but there remained unresolved noise problems [ 5 , 6 , 7 ]. The first type of noise consists of dynamic flickering dot and line noise, generated due to the semiconductor design characteristics of the focal plane area (FPA) and readout integrated circuit (ROIC) of IR sensors [ 8 ].…”

Section: Introductionmentioning

confidence: 99%

Background Registration-Based Adaptive Noise Filtering of LWIR/MWIR Imaging Sensors for UAV Applications

Kim

Chae

2017

Sensors

View full text Add to dashboard Cite

Unmanned aerial vehicles (UAVs) are equipped with optical systems including an infrared (IR) camera such as electro-optical IR (EO/IR), target acquisition and designation sights (TADS), or forward looking IR (FLIR). However, images obtained from IR cameras are subject to noise such as dead pixels, lines, and fixed pattern noise. Nonuniformity correction (NUC) is a widely employed method to reduce noise in IR images, but it has limitations in removing noise that occurs during operation. Methods have been proposed to overcome the limitations of the NUC method, such as two-point correction (TPC) and scene-based NUC (SBNUC). However, these methods still suffer from unfixed pattern noise. In this paper, a background registration-based adaptive noise filtering (BRANF) method is proposed to overcome the limitations of conventional methods. The proposed BRANF method utilizes background registration processing and robust principle component analysis (RPCA). In addition, image quality verification methods are proposed that can measure the noise filtering performance quantitatively without ground truth images. Experiments were performed for performance verification with middle wave infrared (MWIR) and long wave infrared (LWIR) images obtained from practical military optical systems. As a result, it is found that the image quality improvement rate of BRANF is 30% higher than that of conventional NUC.

show abstract

Noise Model of a Multispectral TDI CCD Imaging System and Its Parameter Estimation of Piecewise Weighted Least Square Fitting

Zheng

Jin

et al. 2017

IEEE Sensors J.

View full text Add to dashboard Cite

Scene-based correction of fixed pattern noise in hyperspectral image data using temporal reordering

Cited by 5 publications

References 37 publications

On the effects of spatial and spectral resolution on spatial-spectral target detection in SHARE 2012 and Bobcat 2013 hyperspectral imagery

On the effects of spatial and spectral resolution on spatial-spectral target detection in SHARE 2012 and Bobcat 2013 hyperspectral imagery

Background Registration-Based Adaptive Noise Filtering of LWIR/MWIR Imaging Sensors for UAV Applications

Noise Model of a Multispectral TDI CCD Imaging System and Its Parameter Estimation of Piecewise Weighted Least Square Fitting

Contact Info

Product

Resources

About