Instrumentation & Measurement Magazine 23-6 - 24

Fig. 3. Area of interest located in the municipalities of Castellucciosuperiore
and Latronico, Italy.

Fig. 2. Response to radar by reflectance versus wavelength.

respect to wavelength. For this paper, we are interested in vegetation monitoring such as grass and trees.
The reflectance indicates the response of the specific item
to radar signal. It is actually spectrometry through which a
spectral response is obtained. For processing, some considerations must be pointed out. The input signal is the distribution
R (u,v) of radiances of the scenario, and the output signal is the
distribution I (x,y) of image luminosity; both R and I are bidimensional functions of spatial coordinates (u,v) and (x,y).
If the object under test is pin-point source, its input signal to
the system is an isolated pulse, while its output signal is the
response to the pulse called PSF (point spread function) that
becomes the bi-dimensional image of the pin-point source.
The dimension and the shape of the PSF [4], [5] represent the
measurement of the quality of the system to recover useful information from the scenario. The quality depends upon inner
factors within the system such as optical aberration, wavelength, and external factors like atmosphere clearness, etc.

Machine Learning
The Machine learning approach is generally used for classification of a huge amount of data. One can expect a huge
amount of SAR images for the same location. Instead, we consider features encompassed in the image as a huge number of
pixels to be classified in two different periods: 1992 and 1996.
In particular, a deep learning algorithm is proposed using a
detailed open computer vision (OpenCV) library for image
processing [6]. Therefore, the images of Fig. 4 are processed according to the scheme of Fig. 5; we point out the fact that each
image should have an identification (ID) and be converted to
JavaScript Object Notation (JSON) format within a Python environment [7], [8]. The OpenCV executes a template matching
for each image, in particular, from the second (1996) with respect to the first (1992), using distance among points by means
of the following relationship:

	

Area of Interest
The area of interest that is also a target area is situated in a rural context within the Basilicata region (Southern Italy) and
contains the municipalities of Castelluccio Superiore and Latronico (Centroid 40°03′07.5″N 15°59′07.2″E- EPSG 4326).
The area is a rectangle of about 112 ha (1.12 square km). The
area mentioned above is very close to a site registered in the
Natura 2000 network: Special Areas of Conservation (SAC)
of Monte La Spina - Monte Zaccana. It is about 1000 ha (site
code: IT9210185; 10 square km) which also falls within the Pollino National Park and is characterized by forest habitats and
mountain grasslands. It is illustrated in Fig. 3.
For the target area, two SAR images are available and related to two different dates, i.e., April 10, 1992 (Fig. 4a) and
June 21, 1996 (Fig. 4b). These images, in grey scale, report the
location of the target area. The illustration in grey scale allows
the features encompassed in the images to be enhanced.
24	

R( x , y) 

 (T(x, y)  I( x  x, y  y))
x , y

 T( x, y) . I( x  x, y  y)
2

x , y

2

	(2)

x ,y

in which (x, y) are coordinates of the point to be calculated, (x',
y') are coordinates of initial points and T is the parameter close
to gray level. Beside the map retrieval with the identification
of inner features, a histogram reflecting the amount of pixels is
calculated, according to the following steps:
1.	Declare the matrices to store SAR images and initialize
the number of bins to be used by histogram
2.	Read the input image and transform it to HSV (Hue,
Saturation and Value) format
3.	Create a Trackbar for the user to enter the bin values. Any
change on the Trackbar means a call to the Hist_and_
Backproj callback function (see text box).
4.	Show the image and wait for the user to exit the program
5.	Hist_and_Backproj functions: Initialize. The number of
bins comes from the Trackbar

IEEE Instrumentation & Measurement Magazine	

September 2020



Instrumentation & Measurement Magazine 23-6

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