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.
As one of the most popular topics in the field of Earth observation using remote sensing images, change detection (CD) provides great practical and valuable significance for many fields. Although the majority of supervised methods have made great progress by introducing deep learning in the CD field, they are still limited by manually labeled data. In comparison, unsupervised methods do not require manually labeled data, but the accuracy of CD is difficult to be improved due to the lack of constraints or guidance during training. To tackle these issues, we propose a novel domain knowledge-guided self-supervised learning approach for unsupervised CD by fusing the domain knowledge of remote sensing indices during training and inference. Furthermore, we calculate cosine similarity to select the high-similarity feature vectors outputted by the mean teacher and student networks to implement the hard negative sampling strategy, which effectively improves the CD performance. Compared with other supervised and unsupervised CD methods, our proposed approach achieves state-of-the-art performance with a Kap of 53.34% and an F1 of 55.69% on the Onera Satellite Change Detection dataset. Fusing domain knowledge to guide model training and inference obtains an improvement of 5.83% in Kap and 5.13% in F1, which further narrows the performance gap between unsupervised and supervised CD.
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