基于雷达和光学遥感数据的云污染区域光谱重建算法

Reconstructing spectral information in cloud contaminated regions using object-oriented approach

光学遥感影像在农业领域中应用广泛,但易受天气影响,为减少云污染对影像质量的影响并恢复受污染区域的光谱信息,该研究提出了一种基于雷达和光学遥感数据的云污染区域光谱重建方法。该方法通过结合合成孔径雷达数据,将雷达信号与地类信息作为重建区域的约束条件,在雷达影像上计算相应距离矩阵并移植到光学影像上进行替换,在提高像元间匹配精度的同时最大限度地保留原始影像的光谱信息,为后续地表信息的准确提取奠定基础。为了评估所提出方法的性能,该研究采用传统像素替换法、加权线性回归方法以及基于曲率驱动的方法进行对比试验。结果表明,改进算法重建的波段反射率更接近参考影像,其中Band2、Band3相关系数达到了0.925与0.922,均方根误差分别为0.009、0.007,重建影像与参考影像间的质量损失较少,各波段的峰值信噪比与结构相似性均最高,说明该研究算法在重建影像质量、与参考影像的相似性和光谱特征一致性方面取得了较好的结果。

Optical remote sensing imagery can serve as a crucial data source in numerous fields.However,the susceptibility to weather conditions(like clouds and rain)can render the cloud contamination,leading to the significant influence on optical images.The image quality can be required to restore the spectral information in cloud-contaminated regions.In this study,the spectral information was reconstructed in the cloud-contaminated regions using an object-oriented approach.Synthetic aperture radar(SAR)data was integrated with the radar signals and land cover information,in order to serve as the constraints for the reconstruction.The distance matrix was calculated on the SAR images,and then transplant onto the optical images for the replacement.The pixel-level matching accuracy was enhanced to maximize the retention of spectral information from the original images.A solid foundation was provided for the accurate extraction of subsequent land surface information.The performance was also evaluated to verify the improved model.The comparative experiments were conducted with the traditional pixel replacement,weighted linear regression,and curvature-driven.Visual inspection was carried out over various scenes.The results revealed that the superior quality was achieved in the imagery reconstruction in the thick cloud areas and cloud shadows.Particularly,the consistency was maintained among various land cover pixels in the reconstructed areas by the object-oriented method.The boundaries and areas were accurately delineated in the various types of fine-grained land cover.Quantitative analysis was conducted to validate using some metrics,such as correlation coefficient,root mean square error(RMSE),peak signal-to-noise ratio(PSNR),and structural similarity index(SSIM).The spectral characteristic curves of the pixels were reconstructed to closely match those of the reference images,with the correlation coefficients exceeding 0.99.Notably,significant improvement was observed in the spectral curve recovery of vegetation pixels with the enhanced relative to the rest.The radar vegetation indices were optimized to employ an object-oriented matching mechanism during pixel reconstruction.The traditional pixel replacement model was optimized effectively.Furthermore,the band reflectance was closely resembled that of the reference images.Among the five bands of cloud-contaminated images after reconstruction,the highest correlation coefficients were observed with Band 2 and Band 3 reaching 0.925 and 0.922,respectively,and RMSE values of 0.009 and 0.007,respectively.The reconstructed images shared the minimal quality loss and high similarity to the reference images,with the highest PSNR and SSIM values across all bands.Specifically,the structural similarity values of the reconstructed Band 2 and Band 3 images were 0.902 and 0.910,respectively,with the PSNR values reaching 31.978 and 33.173,respectively.The superior image quality was obtained similar to reference images,which was consistence with spectral characteristics.The experimental results were used to the qualitatively and quantitatively validated the effectiveness of the reconstruction.The object-oriented approach can be expected to reconstruct the spectral information in the cloud-contaminated regions.The remotely sensed images were consistently produced with the higher spectral reconstruction accuracy in the different types of land cover and spectral bands.However,the replacing pixel spectral information was relied primarily on from single-image data.Some limitations was matched the effectiveness between cloudy and cloud-free regions,when reconstructing the large-scale,thick cloud areas in single remote sensing images.Additionally,the global searching can fail to the optimal reconstruction for the locally contaminated cloud pixels.Therefore,further investigation is required to determine the optimal local search range for the pixel matching using different extents of cloud contamination.