The development of modern high-altitude wide-swath imaging systems has brought about adaptive quantization bit-depths(10 ~ 16 bits) and large-scale datasets with a single frame approaching I0 GB,posing two major techn...The development of modern high-altitude wide-swath imaging systems has brought about adaptive quantization bit-depths(10 ~ 16 bits) and large-scale datasets with a single frame approaching I0 GB,posing two major technical challenges for thin-cloud removal in large-format aerial images.Firstly,it is difficult to construct a unified model across different bit-depths,resulting in poor model reusability and the need for high retraining costs in new domains.Secondly,traditional neural networks have to segment images into sub-blocks for processing and then splice them,which is prone to generating chromatic artifacts.To address these issues,we propose the Seamless Cloud Elimination Network(SCENet),whose core innovations are as follows:I achieving bit-depth unification through 8-bit standardization of paired images to support unified model training;2 adopting an adaptive transfer learning architecture that freezes encoder weights and fine-tunes decoders to realize efficient domain adaptation and rapid cloud removal;3 innovating a white-balance-aware cross-patch network architecture,which avoids chromatic artifacts during reconstruction while learning cloud features.Experiments show that this method performs excellently on real datasets,and SCENet achieves the highest Peak Signal-to-Noise Ratio(PSNR) compared with eight existing state-of-the-art methods.展开更多
文摘The development of modern high-altitude wide-swath imaging systems has brought about adaptive quantization bit-depths(10 ~ 16 bits) and large-scale datasets with a single frame approaching I0 GB,posing two major technical challenges for thin-cloud removal in large-format aerial images.Firstly,it is difficult to construct a unified model across different bit-depths,resulting in poor model reusability and the need for high retraining costs in new domains.Secondly,traditional neural networks have to segment images into sub-blocks for processing and then splice them,which is prone to generating chromatic artifacts.To address these issues,we propose the Seamless Cloud Elimination Network(SCENet),whose core innovations are as follows:I achieving bit-depth unification through 8-bit standardization of paired images to support unified model training;2 adopting an adaptive transfer learning architecture that freezes encoder weights and fine-tunes decoders to realize efficient domain adaptation and rapid cloud removal;3 innovating a white-balance-aware cross-patch network architecture,which avoids chromatic artifacts during reconstruction while learning cloud features.Experiments show that this method performs excellently on real datasets,and SCENet achieves the highest Peak Signal-to-Noise Ratio(PSNR) compared with eight existing state-of-the-art methods.
文摘基于预处理后的无人机运动恢复结构点云数据,通过随机抽稀、间距抽稀和体素抽稀调整参数,减少数据冗余并均化密度.将优化后的点云结合反距离权重法、不规则三角网、克里金(Kriging)插值法,构建0.5 m分辨率的数字高程模型(DEM),采用平均绝对误差(M_(AE))与均方根误差(R_(MSE))指标评估误差.结果表明,3种抽稀和插值方法排列组合所得DEM的M_(AE)和R_(MSE)均整体随密度减小而增大;点云密度处于16~60个/m^(2)时宜采用体素抽稀和Kriging插值,体素抽稀和Kriging插值组合在点云密度为58.14个/m^(2)时,M_(AE)与R_(MSE)最小,分别为0.0023、0.0149 m,点云密度下降至17.21个/m^(2)时,M_(AE)与R_(MSE)变化趋于稳定,分别为0.0289、0.0546 m;点云密度处于10~16个/m^(2)时宜采用间距抽稀和Kriging插值,点云密度下降至14.55个/m^(2)时,M_(AE)与R_(MSE)变化趋于稳定,分别为0.0357、0.0670 m.
