Automatic Pseudo-invariant Feature Extraction for the Relative Radiometric Normalization of Hyperion Hyperspectral Images

Dae Sung Kim; Mu Wook Pyeon; Yang Dam Eo; Young Gi Byun; Yong II Kim

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Automatic Pseudo-invariant Feature Extraction for the Relative Radiometric Normalization of Hyperion Hyperspectral Images

机译：自动伪不变特征提取Hyperion高光谱图像的相对辐射归一化

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A new relative radiometric normalization approach is presented based on the spectral profile shape of hyperspectral data. We calculate the spectral similarity value of pixels at the same location using spectral angle mapping. The cumulative moving average and its differential values are used to determine the appropriate num ber of pseudo-invariant features automatically. Band-by-band linear regression of the pseudo-invariant features is used to refine the radiometric normalization results itera tively. We tested the algorithm using six Hyperion data subset images. The proposed method yielded stable results with similar or better performance than other methods for all test sites, when assessed by visual inspection and quantitative analysis.

机译：基于高光谱数据的光谱轮廓形状，提出了一种新的相对辐射归一化方法。我们使用光谱角度映射来计算同一位置像素的光谱相似度值。累积移动平均值及其微分值用于自动确定适当数量的伪不变特征。伪不变特征的逐带线性回归用于迭代地完善放射线归一化结果。我们使用六个Hyperion数据子集图像测试了该算法。通过目视检查和定量分析评估后，所提出的方法在所有测试地点均能获得稳定的结果，其性能与其他方法相似或更好。

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《GIScience & remote sensing》 |2012年第5期|p.755-773|共19页
作者
Dae Sung Kim; Mu Wook Pyeon; Yang Dam Eo; Young Gi Byun; Yong II Kim;
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Department of Advanced Technology Fusion, Konkuk University,Seoul 143-701, South Korea;

Department of Advanced Technology Fusion, Konkuk University,Seoul 143-701, South Korea;

Department of Advanced Technology Fusion, Konkuk University,Seoul 143-701, South Korea;

Satellite Information Research Center, Korea Aerospace Research Institute,Daejeon 350-333, South Korea;

Department of Civil and Environmental Engineering,Seoul National University, Seoul 151-742, South Korea;

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1. A novel automatic method on pseudo-invariant features extraction for enhancing the relative radiometric normalization of high-resolution images [J] . Xu Hanzeyu, Wei Yuchun, Li Xiao, International journal of remote sensing . 2021,第15a16期

机译：伪不变特征提取的一种新型自动方法，提升高分辨率图像的相对辐射归归化
2. A new model for the automatic relative radiometric normalization of multiple images with pseudo-invariant features [J] . Zhou Huizhen, Liu Shaomin, He Jianjun, International journal of remote sensing . 2016,第19a20期

机译：具有伪不变特征的多幅图像自动相对辐射归一化的新模型
3. Radiometric Normalization of Temporal Images Combining Automatic Detection of Pseudo-Invariant Features from the Distance and Similarity Spectral Measures, Density Scatterplot Analysis, and Robust Regression [J] . Alan R. Gillespie, Ana Paula Ferreira de Carvalho, Cristiano Rosa Silva, Remote Sensing . 2013,第6期

机译：时间图像的辐射归一化，结合距离和相似光谱测量，密度散点图分析和稳健回归的自动检测伪不变特征
4. Automatic Relative Radiometric Normalization Algorithm Based on Pseudo-invariant Neighborhood [C] . Deng Xiaolian, Wang Changyao, Lei Bangjun International Congress on Image and Signal Processing . 2008

机译：基于伪不变邻域的自动相对辐射归算法
5. A neural relevance model for feature extraction from hyperspectral images, and its application in the wavelet domain. [D] . Mendenhall, Michael J. 2006

机译：用于从高光谱图像中提取特征的神经相关模型及其在小波域中的应用。
6. Semi-Automatic Normalization of Multitemporal Remote Images Based on Vegetative Pseudo-Invariant Features [O] . Luis Garcia-Torres, Juan J. Caballero-Novella, David Gómez-Candón, -1

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7. Radiometric Normalization of Temporal Images Combining Automatic Detection of Pseudo-Invariant Features from the Distance and Similarity Spectral Measures, Density Scatterplot Analysis, and Robust Regression [O] . Ana Paula Ferreira de Carvalho, Cristiano Rosa Silva, Alan R. Gillespie, 2013

机译：结合伪不变密度散点图分析和鲁棒回归自动检测的时间图像辐射标准化
8. Neural Relevance Model for Feature Extraction from Hyperspectral Images, and Its Application in the Wavelet Domain [R] . Mendenhall, M. J. 2006

机译：高光谱图像特征提取的神经关联模型及其在小波域中的应用

Automatic Pseudo-invariant Feature Extraction for the Relative Radiometric Normalization of Hyperion Hyperspectral Images

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