Side-Slither Data-Based Vignetting Correction of High-Resolution Spaceborne Camera with Optical Focal Plane Assembly

Chen, Chaochao; Pan, Jun; Wang, Mi; Zhu, Ying

doi:10.3390/s18103402

Cited by 9 publications

(6 citation statements)

References 31 publications

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“…Stripes have different scale characteristics depending on their sources. For a single channel (tap), the sources of the stripes include photo shot noise, reset noise, dark current, fixed pattern noise, and quantization noise [18,19]. The stripes in the channel appear as thin lines in the image and appear as small-scale impulse noise on the corresponding CMV.…”

Section: Scale Characteristics Of Stripesmentioning

confidence: 99%

Removal of Large-Scale Stripes Via Unidirectional Multiscale Decomposition

Chang

et al. 2019

Remote Sensing

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Stripes are common in remote sensing imaging systems equipped with multichannel time delay integration charge-coupled devices (TDI CCDs) and have different scale characteristics depending on their causes. Large-scale stripes appearing between channels are difficult to process by most current methods. The framework of column-by-column nonuniformity correction (CCNUC) is introduced to eliminate large-scale stripes. However, the worst problem of CCNUC is the unavoidable cumulative error, which will cause an overall color cast. To eliminate large-scale stripes and suppress the cumulative error, we proposed a destriping method via unidirectional multiscale decomposition (DUMD). The striped image was decomposed by constructing a unidirectional pyramid and making difference maps layer by layer. The highest layer of the pyramid was processed by CCNUC to eliminate large-scale stripes, and multiple cumulative error suppression measures were performed to reduce overall color cast. The difference maps of the pyramid were processed by a designed filter to eliminate small-scale stripes. Experiments showed that DUMD had good destriping performance and was robust with respect to different terrains.

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Section: Scale Characteristics Of Stripesmentioning

confidence: 99%

Removal of Large-Scale Stripes Via Unidirectional Multiscale Decomposition

Chang

et al. 2019

Remote Sensing

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“…The appearance of this effect is particularly undesirable when there is a need for radiometric or quantitative image analysis, which is very common in different areas, e.g., astronomy [ 1 , 2 ]; microscopy [ 3 , 4 , 5 , 6 ]; and remote sensing applications using terrestrial [ 7 , 8 ], airborne [ 9 , 10 , 11 , 12 , 13 ] and spaceborne sensors [ 14 , 15 ], to name just a few of them. This phenomenon is also undesirable in the case of the use of computational imaging algorithms, such as the creation of high dynamic range (HDR) images [ 16 , 17 ], the stitching of static images to create panoramic [ 18 , 19 , 20 ] or mosaic images [ 3 , 4 , 5 , 6 , 21 ], as well as a panoramic real-time view [ 22 ]. Vignetting also affects the results of image analysis, including the results obtained using neural networks [ 23 , 24 ].…”

Section: Introductionmentioning

confidence: 99%

Image Vignetting Correction Using a Deformable Radial Polynomial Model

Bal

Palus

2023

Sensors

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Image vignetting is one of the major radiometric errors that occur in lens-camera systems. In many applications, vignetting is an undesirable effect; therefore, when it is impossible to fully prevent its occurrence, it is necessary to use computational methods for its correction. In probably the most frequently used approach to the vignetting correction, that is, the flat-field correction, the use of appropriate vignetting models plays a pivotal role. The radial polynomial (RP) model is commonly used, but for its proper use, the actual vignetting of the analyzed lens-camera system has to be a radial function. However, this condition is not fulfilled by many systems. There exist more universal models of vignetting; however, these models are much more sophisticated than the RP model. In this article, we propose a new model of vignetting named the Deformable Radial Polynomial (DRP) model, which joins the simplicity of the RP model with the universality of more sophisticated models. The DRP model uses a simple distance transformation and minimization method to match the radial vignetting model to the non-radial vignetting of the analyzed lens-camera system. The real-data experiment confirms that the DRP model, in general, gives better (up 35% or 50%, depending on the measure used) results than the RP model.

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“…This method ensures the focal plane detector array's alignment parallel to the imaging direction, enabling each detector to traverse the same ground stretch, thus receiving an identical amount of light. Chaochao and Chen [23] applied the yaw calibration method, introducing a side-slither data-based vignetting correction technique for a high-resolution spaceborne camera with an optical focal plane assembly. The yaw calibration method facilitates relative radiometric calibration for focal plane pixels, and its high timeliness and ability to capture more gray-level responses in a single imaging enhance the universality of the relative radiometric calibration coefficient.…”

Section: Introductionmentioning

confidence: 99%

A General Relative Radiometric Correction Method for Vignetting Noise Drift

Fan,

Yu,

Zhong

et al. 2023

Remote Sensing

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Due to the limitation of the number of sensor pixels, optical splicing is commonly used to improve the imaging width of remote sensing satellites, and this optical stitching can cause vignetting in the image data of adjacent sensors. The weak energy, low signal-to-noise ratio, and poor response stability of vignetting are key factors that restrict the relative radiometric correction of optical splicing remote satellites. This paper proposes a stability analysis method and a relative radiometric correction method for vignetting. First, we analyzed the stability of the response and the noise impact of vignetting. Massive data from the Jilin-1 GF03D satellites was used to analyze the stability of the response using the vignetting stability analysis method. Secondly, the data on the deep sea during nighttime (DDSN) of Jilin-1 GF03D satellites was used to obtain the characteristics of the sensors’ noise. Thirdly, by building a noise drift model, we calculated the coefficient of the noise drift according to its characteristics. Using the coefficient to eliminate the noise drift of each pixel in vignetting can improve the response stability of vignetting. The average response stability increased by 37.64% by this method. Finally, the automatic relative radiometric correction method was completed through histogram matching. Furthermore, we proposed color aberration metrics (CAMs) to evaluate the multi-spectral images after relative radiometric correction, and massive data from the 16 satellites of Jilin-1 GF03D was used to verify the effectiveness and generality. The experimental results show that the average CAM of the images increased by 15.97% using the proposed method compared to the traditional method.

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Side-Slither Data-Based Vignetting Correction of High-Resolution Spaceborne Camera with Optical Focal Plane Assembly

Cited by 9 publications

References 31 publications

Removal of Large-Scale Stripes Via Unidirectional Multiscale Decomposition

Removal of Large-Scale Stripes Via Unidirectional Multiscale Decomposition

Image Vignetting Correction Using a Deformable Radial Polynomial Model

A General Relative Radiometric Correction Method for Vignetting Noise Drift

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