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首页> 外文会议>IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops >Hyperspectral Data to Relative Lidar Depth: An Inverse Problem for Remote Sensing
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Hyperspectral Data to Relative Lidar Depth: An Inverse Problem for Remote Sensing

机译：高光谱数据到相对激光雷达深度：遥感的逆问题

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摘要

Hyperspectral data provides rich information about a scene in terms of spectral details since it encapsulates measurements/observations from a wide large range of spectrum. To this end, it has been used in different problems mostly related to identification and detection processes. However, the main limitation arises for the accessibility of data. More precisely, there is no sufficient amount of hyperspectral data available compared to visible range data for trainable models. In this paper, we tackle an inverse problem to estimate the relative lidar depth from hyperspectral data. To solve its limitation, we integrate semantic information existed in data with supervised labels to decrease the possibility of parameter overfitting. Moreover, details of the output responses are enhanced with Laplacian pyramids and attention layers in which the model makes predictions from each subsequent scale instead of a single shot prediction from the top of the model. In our experiments, we use the 2018 IEEE GRSS Data Fusion Challenge dataset. From the experimental results, we prove that use of hyperspectral data instead of visible range data improves the performance. Moreover, we show that results are significantly improved if a sparse set of depth measurements is used along with hyperspectral data. Lastly, the integration of semantic information to the solution yields more stable and better results compared to the baselines.
机译：高光谱数据在光谱细节方面提供有关场景的丰富信息，因为它封装了来自各种频谱范围的测量/观察。为此，它已被用于与识别和检测过程相关的不同问题。但是，主要限制为数据的可访问性而产生。更精确地，与可培训型号的可见范围数据相比，没有足够量的超光数据。在本文中，我们解决了逆问题以估计高光谱数据的相对激光雷达深度。为了解决其限制，我们将存在于数据中存在的语义信息与监督标签中存在，以减少参数过度拟合的可能性。此外，随着LAPLACIAN金字塔和注意层的提高了输出响应的细节，其中模型从每个后续比例中取得预测而不是从模型的顶部的单次拍摄预测。在我们的实验中，我们使用2018年IEEE GRSS数据融合挑战数据集。从实验结果来看，我们证明了使用高光谱数据而不是可见范围数据的使用提高了性能。此外，如果使用稀疏的深度测量和高光谱数据，我们表明结果显着改善。最后，与基线相比，对解决方案的语义信息的集成产生更稳定和更好的结果。

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