A deep scalable neural architecture for soil properties estimation from spectral information

TL;DR

This paper introduces an adaptive deep neural network that predicts multiple soil properties from hyperspectral data, offering interpretability and flexibility, validated on large real and simulated datasets.

Contribution

It presents a novel neural architecture capable of multi-variable prediction, spectral band backtracing, and automatic adaptation to different spectral libraries.

Findings

Outperforms existing methods on LUCAS dataset

Accurately identifies spectral bands relevant to each soil variable

Demonstrates effectiveness on simulated PRISMA data

Abstract

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.