Attention Mechanism for Soil Moisture Estimation Using UAV Based SAR

This paper introduces soil moisture estimation using novel convolutional neural networks with attention mechanism and Unmanned Aerial Vehicle (UAV)-based Synthetic Aperture Radar (SAR) system. The presented system consists of a compact light weight radar using carrier frequency of 5GHz and bandwidth of 600MHz attached to a UAV, capable of generating data in HH and VV polarization. The acquired data in Strip Map mode using a dual channel acquisition are used in experiment for soil moisture estimation using Dubois and Integral Equation Model (IEM) for soil moisture detection, and a custom-designed convolutional network with an attention mechanism for soil moisture estimation. The neural network consists of encoder layered convolutional network using stacked encoder architecture and dual branch attention mechanism. The data are classified into 20 different classes from the dry area with volumetric moisture of 10 % to wet class that represents 50 % of volumetric moisture. The results of neural network for soil moisture estimation are presented using Dubois and IEM model and ground truth measurements. Experimental results showed that the neural network provides results with smallest deviation from the ground truth values.