Moving cameras provide multiple intensity measurements per pixel, yet often semantic segmentation, material recognition, and object recognition do not utilize this information. With basic alignment over several frames of a moving camera sequence, a distribution of intensities over multiple angles is obtained. It is well known from prior work that luminance histograms and the statistics of natural images provide a strong material recognition cue. We utilize per-pixel {\it angular luminance distributions} as a key feature in discriminating the material of the surface. The angle-space sampling in a multiview satellite image sequence is an unstructured sampling of the underlying reflectance function of the material. For real-world materials there is significant intra-class variation that can be managed by building a angular luminance network (AngLNet). This network combines angular reflectance cues from multiple images with spatial cues as input to fully convolutional networks for material segmentation. We demonstrate the increased performance of AngLNet over prior state-of-the-art in material segmentation from satellite imagery.

中文翻译：

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材料分割的角亮度

移动相机为每个像素提供多个强度测量，但语义分割、材料识别和对象识别通常不利用此信息。通过在移动摄像机序列的几个帧上进行基本对齐，可以获得多个角度上的强度分布。从先前的工作中众所周知，亮度直方图和自然图像的统计数据提供了强大的材料识别线索。我们利用每像素 {\it 角亮度分布} 作为区分表面材料的关键特征。多视角卫星图像序列中的角度空间采样是材料底层反射函数的非结构化采样。对于现实世界的材料，可以通过构建角亮度网络 (AngLNet) 来管理显着的类内变化。该网络将来自多个图像的角度反射线索与空间线索相结合，作为材料分割的全卷积网络的输入。我们证明了 AngLNet 在卫星图像材料分割方面的性能优于先前的最新技术。