辐射归一化旨在减小不同时相遥感影像间因获取条件不一致而导致的非地表辐射变化的差异,是土地覆盖变化监测的重要前提条件.本文根据高光谱图像上同类地物的谱形及数值的相似性,利用光谱角距离(SAD)和欧氏距离(ED)双重判定选取不变特征点,提出了一种基于光谱角—欧氏距离的辐射归一化方法.在评价指标中除了常用的均方根误差和相对偏差,更增加了高光谱特色的衡量光谱保真性指标:皮尔森系数、光谱扭曲程度.利用高光谱遥感CHRIS图像对本文提出方法进行验证,并与基于多元变化检测(MAD)的辐射归一化方法比较.结果表明,本文方法不仅在辐射特性上优于基于多元变化检测(MAD)的方法,而且具有保持光谱特性的优势,具有较好的应用前景.

This paper proposes an automated radiometric normalization method based on Spectral Angle Distance (SAD) and Euclidean Distance (ED). The method is implemented on two hyperspectral images taken by the Compact High Resolution Imaging Spectrometer (CHRIS) sensor. Experimental results confirm that the proposed method is not only superior to the Mean Absolute Difference (MAD)-based normalization of radiation characteristics, but also has the advantage of commendably preserving the spectral characteristics.
Radiometric normalization minimizes the radiometric differences between two images caused by unstable factors in the acquisition conditions rather than by changes in surface reflectance, which is crucial to land-cover change detection. Radiometric normalization is known as relative radiometric correction. In contrast to absolute correction, the relative method does not need atmospheric data at the moment of image acquisition. This method uses one of the images as reference and then adjusts the radiometric characteristics of the other image, known as subject image, to match the reference image.This paper proposes an automated radiometric normalization method based on Spectral Angle Distance (SAD) and Euclidean Distance (ED). The method is implemented on two hyperspectral images taken by the Compact High Resolution Imaging Spectrometer (CHRIS) sensor. Experimental results confirm that the proposed method is not only superior to the Mean Absolute Difference (MAD)-based normalization of radiation characteristics, but also has the advantage of commendably preserving the spectral characteristics.Radiometric normalization minimizes the radiometric differences between two images caused by unstable factors in the acquisition conditions rather than by changes in surface reflectance, which is crucial to land-cover change detection. Radiometric normalization is known as relative radiometric correction. In contrast to absolute correction, the relative method does not need atmospheric data at the moment of image acquisition. This method uses one of the images as reference and then adjusts the radiometric characteristics of the other image, known as subject image, to match the reference image.This paper proposes an automated radiometric normalization method based on SAD and ED. The proposed method selects unchanged pixels by SAD and ED by considering that the same feature has a similar spectrum shape on hyperspectral images. Therefore, we make an attempt to validate the SAD-ED radiometric normalization of multitemporal hyperspectral satellite images. The Mean Absolute Difference (MAD)-based normalization method is also applied for comparison. In the essay, common evaluation index Root Mean Square Error (RMSE) and Relative Deviation Index (RDI) are used to verify the normalized results. Considering the features of hyperspectral remote sensing images, we also apply the spectral fidelity indexes, i.e., Pearson Correlation Coefficient (PCC) and spectral distortion degree.The method is implemented on two hyperspectral images taken by the CHRIS sensor. Common evaluation index RMSE and (RDI) are used to verify the normalized results. PCC and spectral distortion degree are also applied to evaluate spectral fidelity for radiometric normalization of multitemporal hyperspectral satellite imagery. The evaluation results of RMSE and deviation D show that the SAD-ED normalization of CHRIS images is feasible and more effective than the MAD-based normalization. In addition, the evaluation results of PCC and DD show that the SAD-ED normalization performs better in keeping the spectral-dimensional information of hyperspectral images compared with the MAD-based normalization.Experimental results confirm that the proposed method in this essay is not only superior to the MAD-based normalization of radiation characteristics, but also has the advantage of commendably preserving the spectral characteristics of hyperspectral images, thereby having good application prospects.This paper proposes an automated radiometric normalization method based on Spectral Angle Distance (SAD) and Euclidean Distance (ED). The method is implemented on two hyperspectral images taken by the Compact High Resolution Imaging Spectrometer (CHRIS) sensor. Experimental results confirm that the proposed method is not only superior to the Mean Absolute Difference (MAD)-based normalization of radiation characteristics, but also has the advantage of commendably preserving the spectral characteristics.Radiometric normalization minimizes the radiometric differences between two images caused by unstable factors in the acquisition conditions rather than by changes in surface reflectance, which is crucial to land-cover change detection. Radiometric normalization is known as relative radiometric correction. In contrast to absolute correction, the relative method does not need atmospheric data at the moment of image acquisition. This method uses one of the images as reference and then adjusts the radiometric characteristics of the other image, known as subject image, to match the reference image.This paper proposes an automated radiometric normalization method based on SAD and ED. The proposed method selects unchanged pixels by SAD and ED by considering that the same feature has a similar spectrum shape on hyperspectral images. Therefore, we make an attempt to validate the SAD-ED radiometric normalization of multitemporal hyperspectral satellite images. The Mean Absolute Difference (MAD)-based normalization method is also applied for comparison. In the essay, common evaluation index Root Mean Square Error (RMSE) and Relative Deviation Index (RDI) are used to verify the normalized results. Considering the features of hyperspectral remote sensing images, we also apply the spectral fidelity indexes, i.e., Pearson Correlation Coefficient (PCC) and spectral distortion degree.The method is implemented on two hyperspectral images taken by the CHRIS sensor. Common evaluation index RMSE and (RDI) are used to verify the normalized results. PCC and spectral distortion degree are also applied to evaluate spectral fidelity for radiometric normalization of multitemporal hyperspectral satellite imagery. The evaluation results of RMSE and deviation D show that the SAD-ED normalization of CHRIS images is feasible and more effective than the MAD-based normalization. In addition, the evaluation results of PCC and DD show that the SAD-ED normalization performs better in keeping the spectral-dimensional information of hyperspectral images compared with the MAD-based normalization.Experimental results confirm that the proposed method in this essay is not only superior to the MAD-based normalization of radiation characteristics, but also has the advantage of commendably preserving the spectral characteristics of hyperspectral images, thereby having good application prospects.
This paper proposes an automated radiometric normalization method based on SAD and ED. The proposed method selects unchanged pixels by SAD and ED by considering that the same feature has a similar spectrum shape on hyperspectral images. Therefore, we make an attempt to validate the SAD-ED radiometric normalization of multitemporal hyperspectral satellite images. The Mean Absolute Difference (MAD)-based normalization method is also applied for comparison. In the essay, common evaluation index Root Mean Square Error (RMSE) and Relative Deviation Index (RDI) are used to verify the normalized results. Considering the features of hyperspectral remote sensing images, we also apply the spectral fidelity indexes, i.e., Pearson Correlation Coefficient (PCC) and spectral distortion degree.
The method is implemented on two hyperspectral images taken by the CHRIS sensor. Common evaluation index RMSE and (RDI) are used to verify the normalized results. PCC and spectral distortion degree are also applied to evaluate spectral fidelity for radiometric normalization of multitemporal hyperspectral satellite imagery. The evaluation results of RMSE and deviation D show that the SAD-ED normalization of CHRIS images is feasible and more effective than the MAD-based normalization. In addition, the evaluation results of PCC and DD show that the SAD-ED normalization performs better in keeping the spectral-dimensional information of hyperspectral images compared with the MAD-based normalization.
Experimental results confirm that the proposed method in this essay is not only superior to the MAD-based normalization of radiation characteristics, but also has the advantage of commendably preserving the spectral characteristics of hyperspectral images, thereby having good application prospects.