Instrumentation & Measurement Magazine 23-6 - 23

SAR Sensors Measurements for
Environmental Classification:
Machine Learning-Based
Performances
Aimé Lay-Ekuakille, Moise Avoci Ugwiri, John Peter Djungha Okitadiowo, Vito Telesca,
Pietro Picuno, Consolatina Liguori, and Satya Singh

A

rtificial intelligence, in particular a supervised and
unsupervised machine learning approach, has been
becoming an interest in the field of measurement
and instrumentation. Many problems of classification can be
faced by a machine learning approach. We know machine
learning is a broad area of artificial intelligence that comprises
some other lines of research and activities such as deep learning. Synthetic aperture radar (SAR) measurements by means
of its sensors are of great interest in environmental monitoring,
in particular in land classification. This paper presents findings related to measurements and characterization through
land classification of an environmentally sensitive area in Italy
over two different time periods in order to assess changing parameters. A deep learning algorithm has been designed and
implemented, and a comparison has been established with a
spectral density approach.

	

D  H  	(1)

where D is the diameter of the viewed circular ground area,
H is the flying height above the land, and β is the IFOV of the
system under test expressed in radians. Both H and β are illustrated in the figure and D is generally and approximately
considered as spatial resolution.
The offset plays a key important role since it influences the
ground resolution. This latter, along with radar footprint fluctuations, can cause a scale distortion to be compensated during
image processing.

Local Classification by SAR Imaging

Introduction

Synthetic aperture radar (SAR) imaging is connected to active detection through radiation emitted from the land/terrain
features. Each item located in terrain emits a specific spectral
signature, i.e., a reflectance trend according to wavelength.
Fig. 2 shows different materials/items' reflectance with

Measurements, through remote sensing, for retrieving information are essentially based on multispectral sensors [1],
hence multispectral scanning capable of extending their intervals of scanning by means of electronic detectors. The
detectors allow sensing in broad bands as well very narrow.
Airborne and satellite-based platforms deliver two-dimensional images of land beneath the aircraft/satellite aperture
[2]. The platforms are equipped with dedicated sensors for the
purpose of retrieving information from land. The sensors detect the energy within the instantaneous field of view (IFOV)
of the system. The IFOV [3] is the cone angle within the incident energy, from the land, that is focused on the detector. All
of the energy captured by the detector within the IFOV contributes to the sensor response at any instant. The recovered
image, due to noise, and combination of mixed and original
pixels is processed. Processing is constrained by IFOV and the
morphology of the land that affects spatial complexity. Fig. 1
illustrates the IFOV in an elliptical shape surrounded by the
radar footprint. In the case of circular IFOV, as in an aircraftbased system, given D the diameter of the circle, the IFOV is
derived from (1):

Fig. 1. Radar footprint and instantaneous field of view.

September 2020	

IEEE Instrumentation & Measurement Magazine	23
1094-6969/20/$25.00©2020IEEE



Instrumentation & Measurement Magazine 23-6

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