At present, deep learning method that relies on its strong feature extraction ability has been successfully applied to the estimation of soil organic carbon (SOC) content with hyperspectral data. However, due to the high dimensionality of hyperspectral data and equal treatment of all bands, the performance of these methods is hampered by learning features from useless bands. To address this issue, in this letter, attention mechanism is combined with convolutional neural network (CNN) to assign different weights to different bands of the hyperspectral data. This method constructs a three-layer CNN with a squeeze-and-excitation module at the front of it. Then, five attention-based CNNs are combined to establish an ensemble regression system with diversity. The inputs of each branch in this system are the original hyperspectral data and its transformed data. Moreover, an improved label distribution smoothing (ILDS) technique is proposed to address the problem of imbalanced samples. The experimental results on three soil datasets, Land Use/Land Cover Area Frame Survey (LUCAS) 2009, LUCAS2015, and Africa Soil Information Service (AfSIS), show that this method obtains good estimation performance compared with several state-of-the-art methods, especially in the areas with high SOC content which has small sample sizes.

中文翻译：

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基于注意力的 CNN 集成，利用光谱数据估算土壤有机碳含量


目前，深度学习方法凭借其强大的特征提取能力，已成功应用于高光谱数据土壤有机碳（SOC）含量的估算。然而，由于高光谱数据的高维性和所有波段的平等对待，这些方法的性能因从无用波段学习特征而受到阻碍。为了解决这个问题，在这封信中，注意力机制与卷积神经网络（CNN）相结合，为高光谱数据的不同波段分配不同的权重。该方法构建了一个三层 CNN，前面有一个挤压和激励模块。然后，将五个基于注意力的 CNN 组合起来，建立一个具有多样性的集成回归系统。该系统中每个分支的输入是原始高光谱数据及其变换后的数据。此外，提出了一种改进的标签分布平滑（ILDS）技术来解决样本不平衡的问题。在土地利用/土地覆盖面积框架调查（LUCAS）2009、LUCAS2015和非洲土壤信息服务（AfSIS）三个土壤数据集上的实验结果表明，与几种最先进的方法相比，该方法获得了良好的估计性能方法，特别是在 SOC 含量高、样本量小的区域。