Radiometric Normalization for Cross-Sensor Optical Gaofen Images with Change Detection and Chi-Square Test

Remote Sensing

Xi Li¹  Li Yan¹  Jianbing Yang¹  Yi Zhang¹  Anqi Zhao¹ 

[1] School of Geodesy and Geomatics, Wuhan University, 129 Luoyu Road, Wuhan 430079, China;

关键词: relative radiometric normalization;    surface reflectance;    Gaofen images;    change detection;    chi-square test;   

DOI  :  10.3390/rs13163125

来源: DOAJ

【 摘 要 】

As the number of cross-sensor images increases continuously, the surface reflectance of these images is inconsistent at the same ground objects due to different revisit periods and swaths. The surface reflectance consistency between cross-sensor images determines the accuracy of change detection, classification, and land surface parameter inversion, which is the most widespread application. We proposed a relative radiometric normalization (RRN) method to improve the surface reflectance consistency based on the change detection and chi-square test. The main contribution was that a novel chi-square test automatically extracts the stably unchanged samples between the reference and subject images from the unchanged regions detected by the change-detection method. We used the cross-senor optical images of Gaofen-1 and Gaofen-2 to test this method and four metrics to quantitatively evaluate the RRN performance, including the Root Mean Square Error (RMSE), spectral angle cosine, structural similarity, and CIEDE2000 color difference. Four metrics demonstrate the effectiveness of our proposed RRN method, especially the reduced percentage of RMSE after normalization was more than 80%. Comparing the radiometric differences of five ground features, the surface reflectance curve of two Gaofen images showed more minor differences after normalization, and the RMSE was smaller than 50 with the reduced percentages of about 50–80%. Moreover, the unchanged feature regions are detected by the change-detection method from the bitemporal Sentinel-2 images, which can be used for RRN without detecting changes in subject images. In addition, extracting samples with the chi-square test can effectively improve the surface reflectance consistency.

【 授权许可】

Unknown