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首页> 外文期刊>IEEE Transactions on Geoscience and Remote Sensing. >Superpixel-Based Adaptive Kernel Selection for Angular Effect Normalization of Remote Sensing Images With Kernel Learning
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Superpixel-Based Adaptive Kernel Selection for Angular Effect Normalization of Remote Sensing Images With Kernel Learning

机译：基于超像素的自适应核选择用于基于核学习的遥感影像角效应归一化

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摘要

Considering that satellites rarely acquire data from the exact nadir direction, angular effect normalization needs to be conducted as an important preprocessing step to correct reflectance observations from off-nadir directions into the nadir direction. Kernel-based bidirectional reflectance distribution function models have been employed for angular effect correction. The kernels used in the model are often predetermined and fixed for an entire image. However, the fixed kernels are unable to accommodate the various reflective characteristics of different ground cover types present in the imaged area. In this paper, we propose a kernel learning procedure that enables the flexible selection of kernels for different land cover types within a scene. The kernels are selected from kernel dictionaries that contain multiple candidate kernels. The selection is conducted on the superpixel level instead of the pixel level in order to reduce within-class variation and overcome the overfitting problem. Experiments are conducted on multiangular images acquired by the Sentinel-2A satellite over a rural area in southeastern Australia. Cross-validation results show that the proposed method is able to adaptively select appropriate kernels for different land cover types, leading to an improved performance for image normalization.
机译：考虑到卫星很少从精确的天底方向获取数据，因此需要进行角效应归一化作为重要的预处理步骤，以将从天底外方向反射到天底方向的反射率进行校正。基于核的双向反射率分布函数模型已用于角度效果校正。模型中使用的内核通常是针对整个图像预先确定和固定的。但是，固定内核无法适应成像区域中存在的不同地面覆盖类型的各种反射特性。在本文中，我们提出了一种内核学习程序，该程序可以针对场景中不同土地覆盖类型灵活选择内核。从包含多个候选内核的内核词典中选择内核。选择是在超像素级别而不是像素级别进行的，以减少类内变化并克服过拟合问题。对Sentinel-2A卫星在澳大利亚东南部农村地区获得的多角度图像进行了实验。交叉验证结果表明，该方法能够针对不同的土地覆盖类型自适应地选择合适的内核，从而提高了图像归一化的性能。

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