A relative radiometric correction method for airborne SWIR hyperspectral image using the side-slither technique

Cheng, Xiaoyu; Zhuang, Xiaoqiong; Zhang, Dong; Yi, Yong; Hou, Jia; He, Daogang; Jia, Jianxin; Wang, Yueming

doi:10.1007/s11082-019-1816-x

Cited by 13 publications

(4 citation statements)

References 33 publications

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“…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. However, this method requires a high level of satellite attitude adjustment capability and stability, a challenge often faced by traditional satellites to achieve stable yaw imaging capabilities [24].…”

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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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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“…However, absolute radiation correction requires a large number of imaging and other parameters [3], so relative radiation correction is used more often [4,5]. Relative radiometric correction uses the grayscale value of the feature in a multitemporal image instead of the reflected irradiance or reflectance of the feature [6,7] and matches the grayscale value of the image to be corrected with that of the reference image. Relative radiometric correction is used in dynamic monitoring [8,9], change detection [10,11], forest resource assessment [12], etc.…”

Section: Introductionmentioning

confidence: 99%

Relative Radiation Correction Based on CycleGAN for Visual Perception Improvement in High-Resolution Remote Sensing Images

Fan

Zhang

et al. 2021

IEEE Access

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The differences between the imaging environments of sensors lead to great differences in remote sensing images of the same area in different seasons. Relative radiation correction has high practical value as the main method to reduce such differences. However, the differences in vegetation radiation caused by seasonal changes are difficult to correct by traditional radiation correction methods. The corrected results also have difficulty achieving better results at the level of human eye visual perception. Moreover, the traditional measurement of the relative radiation correction result image quality index is not consistent with the human eye visual perception effect. To address the above two problems, this paper performs seasonal relative radiation correction on high-resolution remote sensing images by CycleGAN based on a convolutional neural network, including two transformations: 1) the transformation of remote sensing images from autumn-winter to spring-summer and 2) the transformation of remote sensing images from spring-summer to autumn-winter. The similarity between the relative radiation-corrected image and the reference image is measured by the convolutional neural network model with the ability to discriminate distances. The results show that the visual effect of this method is significantly better than that of other relative radiation correction methods, and the visual perception distance is consistent with the human eye visual perception judgment. The changed area still retains its original feature characteristics. The visual perception distance of the conversion from autumn-winter to spring-summer images is improved by 9% compared with other state-of-the-art methods. The visual perception distance of spring-summer images to autumn-winter images is improved by 3%. We expect that the method in this paper can be used for preprocessing to improve the accuracy of algorithms for remote sensing image classification, image change detection, etc.

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“…The availability of open and shared bi-temporal multispectral/hyperspectral imagery datasets, as well as Synthetic Aperture Radar (SAR) imagery datasets, facilitates researchers in testing the superiority of proposals for alteration detection. Moreover, many imagery datasets are radiometric corrected [1][2][3][4], which offers a foundation for the following works such as image recognition [5][6][7], analysis, and classification [8,9]. Therefore, we can focus straightforwardly on constructing alteration detection models with good performance.…”

Section: Introductionmentioning

confidence: 99%

Alteration Detection of Multispectral/Hyperspectral Images Using Dual-Path Partial Recurrent Networks

Yuan

Feng

2021

Remote Sensing

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Numerous alteration detection methods are designed based on image transformation algorithms and divergence of bi-temporal images. In the process of feature transformation, pseudo variant information caused by complex external factors will be highlighted. As a result, the error of divergence between the two images will be further enhanced. In this paper, we propose to fuse the variability of Deep Neural Networks’ (DNNs) structure flexibly with various detection algorithms for bi-temporal multispectral/hyperspectral imagery alteration detection. Specifically, the novel Dual-path Partial Recurrent Networks (D-PRNs) was proposed to project more accurate and effective deep features. The Unsupervised Slow Feature Analysis (USFA), Iteratively Reweighted Multivariate Alteration Detection (IRMAD), and Principal Component Analysis (PCA) were then utilized, respectively, with the proposed D-PRNs, to generate two groups of transformed features corresponding to the bi-temporal remote sensing images. We next employed the Chi-square distance to compute the divergence between two groups of transformed features and, thus, obtain the Alteration Intensity Map. Finally, threshold algorithms K-means and Otsu were, respectively, applied to transform the Alteration Intensity Map into Binary Alteration Map. Experiments were conducted on two bi-temporal remote sensing image datasets, and the testing results proved that the proposed alteration detection model using D-PRNs outperformed the state-of-the-art alteration detection model.

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A relative radiometric correction method for airborne SWIR hyperspectral image using the side-slither technique

Cited by 13 publications

References 33 publications

A General Relative Radiometric Correction Method for Vignetting Noise Drift

A General Relative Radiometric Correction Method for Vignetting Noise Drift

Relative Radiation Correction Based on CycleGAN for Visual Perception Improvement in High-Resolution Remote Sensing Images

Alteration Detection of Multispectral/Hyperspectral Images Using Dual-Path Partial Recurrent Networks

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