论文标题
来自光谱信息的土壤特性估算的深度可伸缩神经体系结构
A deep scalable neural architecture for soil properties estimation from spectral information
论文作者
论文摘要
在本文中，我们提出了一种自适应的深神经结构，以通过分析高光谱特征来预测多种土壤特征。所提出的方法克服了先前方法的局限性：（i）允许一次预测多个土壤变量； （ii）它允许回顾最大程度地估算给定变量的光谱带； （iii）它基于一种灵活的神经体系结构，能够自动适应分析的光谱库。提出的架构是在卢卡斯（Lucas）的，一个大型实验室数据集和通过模拟Prisma Hyperspectral传感器实现的数据集上进行的。与其他最先进的方法相比，结果证实了所提出的解决方案的有效性。
In this paper we propose an adaptive deep neural architecture for the prediction of multiple soil characteristics from the analysis of hyperspectral signatures. The proposed method overcomes the limitations of previous methods in the state of art: (i) it allows to predict multiple soil variables at once; (ii) it permits to backtrace the spectral bands that most contribute to the estimation of a given variable; (iii) it is based on a flexible neural architecture capable of automatically adapting to the spectral library under analysis. The proposed architecture is experimented on LUCAS, a large laboratory dataset and on a dataset achieved by simulating PRISMA hyperspectral sensor. 'Results, compared with other state-of-the-art methods confirm the effectiveness of the proposed solution.
