A Low Cost Solution for NOAA Remote Sensing

Edoardo Ardizzone, Alessandro Bruno, Francesco Gugliuzza and Roberto Pirrone

Dipartimento dell’Innovazione Industriale e Digitale (DIID), Universit

a degli Studi di Palermo,

Viale delle Scienze Ed. 6 - 90128 Palermo (PA), Italy

Keywords:

Remote Sensing, Satellite Communication, Signal Processing.

Abstract:

United States National Oceanic and Atmospheric Administration (NOAA) weather satellites adopt Advanced

Very High Resolution Radiometer (AVHRR) sensors to acquire remote sensing data and broadcast Automatic

Picture Transmission (APT) images. The orientation of the scan lines is perpendicular to the orbit of the

satellite. In this paper we propose a new low cost solution for NOAA remote sensing. More in detail, our

method focuses on the possibility of directly sampling the modulated signal and processing it entirely in

software enabled by recent breakthroughs on Software Deﬁned Radios (SDR) and CPU computational speed,

while keeping the costs extremely low. We aim to achieve good results with inexpensive SDR hardware, like

the RTL-SDR (a repurposed DVB-T USB dongle). Nevertheless, we faced some problems caused by hardware

limits such as high receiver noise ﬁgure and low ADC resolution. Furthermore, we detected several inherent

drawbacks of frequent tuner saturations. For this purpose we developed a software-hardware integrated system

able to perform the following steps: satellite pass prediction, time scheduling, signal demodulation, image

cropping and ﬁltering. Although we employed low cost components, we obtained good results in terms of

signal demodulation, synchronization and image reconstruction.

1 INTRODUCTION

Airborne and satellite sensors allow us to analyze a

huge amount of data used for many tasks such as

signal and image processing for GIS applications,

weather and storm predictive analysis, earth obser-

vation and more generally, remote sensing. Every-

day several United States National Oceanic and At-

mospheric Administration (NOAA) weather satellites

pass over us: each NOAA weather satellite broadcasts

an Automatic Picture Transmission signal, which

contains a live weather image of the area overﬂown

by the satellite. The satellite scanner systems in-

clude line scanning devices observing the earth per-

pendicular to the orbital plane. The signal mea-

surements are performed in different spectral bands

thanks to the Advanced Very High Resolution Ra-

diometer (AVHRR) instrument. The visible light and

the infrared images are combined in a row vector;

the combination is done using the Automatic Picture

Transmission (APT) system.

Although many scientiﬁc progresses have been

made, there are still many open scientiﬁc challenges

for NOAA satellites, such as information calibration

or the physical layer (synchronization, data detection,

channel codec). Many satellite-based platforms are

dedicated to remote sensing data analysis for weather

prediction as well as for climatological studies. In

this paper we present a new easily accessible satellite

based platform for signal processing, installed in our

lab: the platform consists of a low cost receiver sub-

system for public weather satellites and a signal pro-

cessing subsystem for different applications (image

reconstruction, image enhancement and clouds seg-

mentation). The rest of the paper is organized as it

follows: in section 2 we describe the state of the art

methods for satellite signals modulation and imagery,

in section 3 we show in greater detail our proposed

platform for the reception of NOAA signals, in sec-

tion 4 we show the experimental results for the recon-

structed images and the section 5 ends the paper with

conclusions and future works.

2 STATE OF THE ART

In this section we give a description of the state of

the art of the techniques for the demodulation of sig-

nals coming from NOAA satellites. Furthermore, a

brief overview of the image processing techniques for

clouds detection is given.

128

Ardizzone, E., Bruno, A., Gugliuzza, F. and Pirrone, R.

A Low Cost Solution for NOAA Remote Sensing.

DOI: 10.5220/0006639101280134

In Proceedings of the 7th International Conference on Sensor Networks (SENSORNETS 2018), pages 128-134

ISBN: 978-989-758-284-4

2.1 Satellite Communications and

Signals Reception

Artiﬁcial satellite development was originally fuelled

by the need for superpowers to show their ”status”

to their opponents, during the Space Race. After

a few years, however, it became clear that satel-

lites could be used to establish a reliable communi-

cation infrastructure or for remote sensing. In the

last decades many communications satellites were

launched into space: some of them are equipped with

low bandwidth communication systems (VHF/UHF),

while some others are equipped with high bandwidth

communication systems (even more recently, commu-

nications are carried out by arrays of high bandwidth

microwave transponders, designed to offer multime-

dia services (Farserotu and Prasad, 2000)). Modern

satellites receive and transmit simultaneously thou-

sands of signals: hyperspectral cameras and radiome-

ters have been widely used to analyze Earth’s visi-

ble and infrared radiation directly from the spacecraft;

Synthetic Aperture Radars (SAR) have been used to

map surface features and texture, even through cloud

cover. The result is the possibility to analyze data like

vegetation moisture (to estimate wildﬁre risk) (Al-

Moustafa et al., 2012) or reconstruct 3D models of

conﬁned zones of the Earth (recently even at 1-meter

resolution). Due to sensor nonlinearities, raw sen-

sor data can be affected by noise and distortions, and

needs to be corrected with classiﬁcation or regression

methods (Camps-Valls et al., 2011).

The APT format used by NOAA satellites was

ﬁrst introduced in the 1960s (Barnes and Smallwood,

1982) (Winston, 1997) and has been only slightly

modiﬁed since, so it’s no surprise that many sta-

tions still use analog-radio equipment designed in the

1980s or the 1990s. The APT signal is formed by am-

plitude modulating a 2400 Hz carrier with each line of

pixel data obtained by merging two images and cali-

bration data (see subsection 3.1), and then frequency

modulating the result; it is obvious that the ease of

building a basic receiver composed of a FM demodu-

lator followed by an AM demodulator and an analog-

digital converter has sparked the interest of many sci-

entists and amateur radio operators. However, re-

ceiving and demodulating signals coming from satel-

lites not placed in geostationary orbits presents many

challenges because the radio wave itself changes fre-

quency due to Doppler effects and free space attenu-

ation is not constant but, instead, varies with distance

from the satellite. Another problem is that due to

satellites’ variable rotation relative to Earth’s axis, lin-

early polarized antennas become unsuitable and cir-

cularly polarized antenna are required to ensure reli-

able and effective demodulation and synchronization,

as well as a good quality output image.

Image synchronization and calibration, unlike de-

modulation and frequency tracking, are usually per-

formed in software instead of hardware (Benabadji

et al., 2004) because they are non-real time tasks that

require some CPU power (mainly signal down sam-

pling, synchronization pulse convolution and line-by-

line pixel value processing); it is also important to say

that it would be somewhat challenging to implement

those tasks in hardware (at least for non-experts).

Sensor data can then be further corrected to compen-

sate small sensor alignment errors, orbit uncertainty

and on-board clock offset, by comparing expected and

measured emissivity over land and sea and binning re-

sults into a high-resolution grid (Moradi et al., 2013).

2.2 Clouds Segmentation

The accurate separation of clouds from lands in satel-

lite imagery is a critical step, due to the varying

conditions of surface properties, such as reﬂectance

and emissivity. Furthermore, the classiﬁcation of

the cloud types from satellite imagery is useful for

weather research and forecast applications (forecast-

ing of meteorological phenomena) (Feidas et al.,

2000). Heidinger et al. in (Heidinger et al., 2012)

performed a naive Bayesian approach for cloud de-

tection. This kind of methodology has been used for

cloud detection on NOAA AVHRR data. Generally,

NOAA AVHRR sensors have coarser spatial resolu-

tion than most modern satellites. Some methods face

the problem of the enhancement of NOAA AVHRR

data (such as remote sensing images). In (Alkhatib

et al., 2012) the authors used region growing method

for dust clouds segmentation. In (Karlsson et al.,

2015) the authors examined two probabilistic meth-

ods for cloud masking of images from NOAA satel-

lites, obtained with Advanced Very High Resolution

Radiometer.

Simpson and Gobat in (Simpson and Gobat, 1996)

used AVHRR Split-and-Merge Clustering (ASMC)

for cloud detection to overcome the problem of vary-

ing land surface reﬂectance and emissivity. Split-and-

merge clustering allows to segment the scene in its

natural groupings and label them as cloud, cloud-free

land, uncertain. In (Yhann and Simpson, 1995) the

authors performed a supervised approach based on

neural networks for cloud screening of AVHRR data

over the ocean. In our work the signals coming from

NOAA satellites are demodulated, synchronized and

enhanced by using image processing techniques (see

section 4), then clouds detection is performed by us-

ing the Otsu’s method (Zhang and Hu, 2008).

A Low Cost Solution for NOAA Remote Sensing

129

3 PROPOSED METHOD

Our method focuses on the possibility of directly

sampling and processing of the modulated signal en-

tirely in software enabled by recent breakthroughs on

Software Deﬁned Radios (SDR) and CPU computa-

tional speed. Our objective was achieving good re-

sults with inexpensive SDR hardware like RTL-SDR

(Sruthi et al., 2013) (a repurposed DVB-T USB don-

gle), high receiver noise ﬁgure and low ADC reso-

lution. We also dealt with the inherent drawbacks

caused by frequent tuner saturations. An integrated

hardware and software system is proposed to perform

satellite pass prediction, time scheduling, signal de-

modulation, image ﬁltering.

3.1 APT Signal

The APT was introduced in 1960s and it shows its

age in some respects: it is a mixed modulation sig-

nal (AM+FM) carrying a completely analog payload.

The standard APT format consists of around 2080

pixels row, which is divided in two sub-rows of 909

pixels each belonging to two different sub-images (A

and B) and then padded with synchronization and di-

agnostic information words. Images A and B dur-

ing daytime are acquired in the visible range and in

the infrared range of the electromagnetic spectrum re-

spectively. At night-time, image A is replaced with

one acquired at an infrared wavelength different from

that of B. Rows can be grouped logically in frames of

128 lines each: a complete frame contains image cal-

ibration data and dynamic range references (wedges)

(Winston, 1997). The payload is used to amplitude

modulate a 2400 Hz carrier according to (1)

s(t) = [1 +m(t)] A cos(2π f

t) (1)

Where m(t) is the modulating signal, A the carrier’s

amplitude and f

the carrier’s frequency. The ampli-

tude modulated signal is then frequency modulated

with a frequency deviation of about 18-20 kHz and

transmitted in the 137 MHz band at about 5 W EIRP

(36.99 dBm) and 4160 sym/s symbol rate. Right

hand circularly polarized (RHCP) antennas transmit

the signal, so particular care had to be taken when

choosing which antenna type to use in the receiving

station.

3.2 Hardware

First of all we build a circularly polarized antenna:

the ”turnstile” crossed-dipoles design was chosen be-

cause of its simplicity and the good performance pro-

vided when placed over a ground plane. Ease of con-

struction, durability and suitability of common ma-

terials compensate the drawbacks of the system: the

medium gain offered (6 dBi) and many nulls present

in such a design (Grifﬁths, 2014). The antenna has

been built exclusively off PVC pipes, threaded bars

as dipole elements, nuts and bolts and 75 Ω generic

coaxial cable. The antenna is directly connected to a

repurposed old TV ampliﬁer (SIEL .269 01), modiﬁed

to behave like a wide-bandwidth ampliﬁer. The input

variable attenuator and high-pass ﬁlter have been re-

moved, and the RF output has been separated from

DC power path, allowing power without a bias tee.

The ampliﬁer uses two active components:

• BFR90A - 16 dB gain, 1.8 dB NF

• BFR91A - 14 dB gain, 1.6 dB NF

The results are 30 dB gain and ~1.52 dB NF (cal-

culated using Friis’ formula (Promwong and Takada,

2004)). Ignoring noise introduced by ampliﬁer’s pas-

sive components, and considering room temperature

of 290 K and 40 kHz bandwidth, the noise output of

the ampliﬁer can be calculated with formula (2).

+ g + NF (2)

is the input noise (-127.95 dBm) calculated using

formula (3), g the gain (30 dB) and NF the total noise

ﬁgure (1.52 dB). The result is a noise output of -96.43

dBm.

10log

(kT B) + 30 (3)

k is Boltzmann constant, T is absolute temperature

and B is signal bandwidth, while +30 has been added

to convert results from dBW to dBm. Considering

NOAA satellites transmit at 5 W EIRP, when they are

at receiving station’s zenith the signal power at the

receiver’s input can be calculated as follows

36.99 dBm - 133.34 dB + 6 dBi - 3 dB + 30 dB =

= -63.35 dBm

(4)

133.34 dB is free space loss and 3 dB is the estimated

loss due to cable and connectors. Output SNR in best-

case conditions is then 33.08 dB, more than enough to

ensure good image quality. The ampliﬁer’s output is

connected to a SAW ﬁlter (Tai-Saw TA1581A) cen-

tered on 137.5 MHz to attenuate out-of-band signals

and reduce the severe distortion caused by high-power

FM broadcast stations and GSM signals. The TA1581

is a 50 Ω device, but an impedance matching circuit

has not been installed because of the low mismatch

loss (0.177 dB from antenna to ﬁlter and another

0.177 dB from ﬁlter to next RF device, which has 75

Ω impedance) compared to ﬁlter’s 3.5 dB insertion

loss. We added a standard ferrite bead near the re-

ceiver to shield common-mode interference caused by

SENSORNETS 2018 - 7th International Conference on Sensor Networks

130

high-frequency equipment (PC, lab instruments, elec-

tronic ballasted lamps, etc.). The SDR hardware is

widely available at a very low cost and is built around

two chips: a RF tuner and an ADC/COFDM demod-

ulator combo chip. The ﬁrst used to be an Elonics

E4000 (zero-IF), but has been replaced on new mod-

els with two superheterodyne alternatives, the Rafael

Micro R820T and R820T2. The demodulator chip,

a Realtek RTL2832U, is commonly used to demodu-

late the QPSK or QAM DVB-T signal into a MPEG

stream and send it to a PC via an USB interface, but

has a hidden passthrough mode which allows passing

the ADC sample stream instead. The samples are in-

ternally ﬁltered by a 32 coefﬁcients FIR ﬁlter. For this

study a dongle containing an E4000 tuner has been

used.

3.3 Software

Managing tuner’s and RTL2832U’s hardware regis-

ters is not an easy task, and it has been hindered by

the lack of a public datasheet for the Realtek chip, but

the RTL-SDR team has coded a very efﬁcient open

source C library which abstracts from hardware difﬁ-

culties (Osmocom, 2012), allowing developers to tune

the receiver and get ADC samples with few function

calls. Thanks to the Mathworks RTL-SDR library

we focused on the development of DSP algorithms

on Simulink (Sergienko, 2014), taking advantage of

its model-to-code conversion functionality. The FM

demodulation was initially delegated to Kyle Keen’s

rtl fm (Keen, 2013) software, but it was plagued by

frequent saturation problems. It was then decided to

implement a simple FM demodulation algorithm di-

rectly in the Simulink model. The software devel-

oped consists of the following parts: satellite pass pre-

dictor, digital down-converter, FM demodulator, FIR

low-pass ﬁlter, AM demodulators, synchronization

block, pixel dynamic range calibration block, model’s

parameters generator.

3.3.1 Satellite Pass Predictor

The ﬁrst step in obtaining a completely automatic

system was to develop a method to automatically

schedule the appropriate start time for the receiv-

ing/demodulating software. John Magliacane’s PRE-

DICT (Magliacane, 2001) software has been used to

predict start/end times and maximum elevations of

the next satellite passes. This software supports com-

mand line parameters and text output, enabling the ex-

ecution of a script for multiple pass predictions. PRE-

DICT’s output is processed in order to make it com-

patible with Microsoft Windows schtasks.exe syntax,

but it can be easily modiﬁed to use Linux/UNIX cron

instead.

3.3.2 RTL-SDR Library for Simulink

The library used in this work includes the DLL ﬁles

for hardware-software communication, the required

precompiled code to interface them with MATLAB

and a source block that has to be added to Simulink

models.

3.3.3 Digital Down-converter

It was necessary to overcome the DC offset issue

caused by the Zero-IF tuner architecture, so we de-

signed and implemented an offset tuning mechanism.

The RTL-SDR is tuned 500 kHz lower than the target

frequency, and a Simulink block performs a circular

frequency shift to shift the desired signal back to base-

band using (5).

− jΩ

= e

− j2π f

(5)

3.3.4 FM Demodulator

There are many examples of DSP FM demodulators

in literature, some of which use the computation-

ally expensive arctangent function. Experimental test

showed that the non-CPU intensive demodulator pre-

sented in (Lyons, 2004) (capable of scaling the output

signal according to input modulus amplitude) was in-

appropriate as it caused uncontrolled phase shift in the

demodulated output, preventing correct APT format

decoding. Therefore an arctangent-based demodula-

tor (Shima, 1995) was chosen (equipped with a signal

scaler), as shown in Fig. 1.

3.3.5 Low-Pass FIR Filter

A linear phase FIR ﬁlter designed using a Kaiser win-

dow has been added to the Simulink model in order to

attenuate noise present in the FM demodulator output.

Its optimal cutoff frequency (5000 Hz) has been found

experimentally and varies with hardware receiver and

FM demodulation algorithm characteristics.

3.3.6 AM Demodulators

We developed two DSP AM demodulators (Fig. 1):

the ﬁrst is based on a discrete-time PLL followed by

a low-pass ﬁlter with a cutoff of 1500 Hz to attenu-

ate 2π f

frequency components. As with any PLL, its

performance is determined to a large extent by Ki and

Kp coefﬁcients (a wrong choice leads to instability,

especially with low SNR signals). Initially, FIR ﬁl-

ter delay in the feedback path was considered when

A Low Cost Solution for NOAA Remote Sensing

131

choosing coefﬁcients’ values, as in (Wilson et al.,

2009), but unsatisfactory experimental results sug-

gested a different approach was necessary; in the end,

atpdec’s (Leconte, 2003) values ensured good demod-

ulation and image decoding results. The second de-

modulator is a simple envelope detector: the input

signal gets full-wave rectiﬁed by the absolute value

function and ﬁltered by a linear-phase FIR ﬁlter with a

steep transition between passband and stopband. The

ﬁlter greatly attenuates the fc component while leav-

ing the modulating signal intact. Demodulation qual-

ity is satisfactory at high SNR, though PLL demod-

ulation shows better performance with low SNR sig-

nals. Furthermore, PLL demodulation has high com-

putational complexity. On the other side, the software

envelope detector solution has lower computational

complexity; its hardware implementation is not eas-

ily accessible because it would require the addition of

a low-pass ﬁlter with a large number of poles.

3.3.7 Synchronization Block

APT format is equipped with two synchronization

pulses: horizontal synchronization (sync A) is used

as a boundary between video lines and inter-channel

synchronization (sync B) is used as a boundary be-

tween the two video channels (Winston, 1997). In

our work, we only used sync A as we implemented

channel separation in the Image Processing code by

splitting every line into two parts, each consisting of

1040 pixels. Synchronization block processes signals

sampled at a multiple of the symbol rate (4160 sym-

bols/s), a requirement easily met by ADCs clocked by

modern PLL clock generators. That being said, the

signal is downsampled by selecting the median of the

N samples instead of one sample every N. The median

ﬁlter is widely known to greatly reduce impulse noise.

Part (a) of Fig. 2 down-samples the signal, while (b)

correlates previous part’s output with a pattern of sync

A; synchronization pulses are detected comparing lo-

cal correlation peaks with maximum correlation in the

last 3/4 seconds (c) (time during which one and a half

video lines get transmitted). If correlation at discrete

instant k-1 is a local peak and is greater than 80% of

maximum correlation in said interval, it is considered

as a synchronization pulse and a row formed by the

last 2080 samples is inserted as a new element at the

end of queue (e). Sometimes, synchronization pulses

are lost or are erroneously detected due to signal fad-

ing or noise, so a ﬁltering technique was devised (d):

it ﬁlters pulses detected less than 2070 samples after

last one. If a pulse has not been detected after 2090

samples the line is inserted at the end of the queue

anyway and the 2090 samples counter is reset; the

2070 samples counter is not reset not to interfere with

Out

num(z)

Discrete FIR Filter

|u|

Abs

Figure 1: a) FM demodulator; b) PLL-based AM demodu-

lator c) Envelope detector AM demodulator.

(c)

(d)

(e)

(f)

Figure 2: Synchronization block.

SENSORNETS 2018 - 7th International Conference on Sensor Networks

132

Figure 3: AVHRR image after automatic cropping.

Figure 4: Left: disk ﬁltered remote sensing image from

NOAA. Right: clouds segmentation achieved with the Otsu

method.

subsequent synchronization pulses. Finally, the Rate

Transition block (f) outputs two lines of 2080 pixels

each per second.

3.3.8 Pixel Dynamic Range Calibration Block

Each frame, composed of 128 rows, contains 16

wedges (or patches), the ﬁrst nine carry data about

the image dynamic range: wedges 1-8 represent val-

ues from 1/8 to 8/8, while 9 is the zero reference. The

calibration block detects the exact position of those 9

wedges cross-correlating a vertical band of the image

with an example pattern, then the values of wedges

8 and 9 are sampled to compute gray level offset and

dynamic range. Offset is subtracted from pixel val-

ues and the result is divided by the detected dynamic

range, in order, to normalize pixel values to the [0

1] interval: values outside this range can be treated

as noise and clipped to 0 or 1. This block outputs a

stream of pixel rows that can be further improved us-

ing Image Processing algorithms.

4 EXPERIMENTAL RESULTS

The output of our model is a 2080x2*T image, where

T is running time in seconds. The image includes

white noise or artifacts when PLL lock is not asserted,

but these are cropped out by an algorithm using pixel

variance in calibration wedges to estimate row recep-

tion quality. Only rows having an entropy below a cer-

Table 1: Objective metrics results for image enhancement

ﬁltering.

Method PSNR [dB] RMSE

None (raw image) 17.788 0.129

3x3 arithmetic mean ﬁlter 17.981 0.126

3x3 geometric mean ﬁlter 13.655 0.208

3x3 harmonic mean ﬁlter 13.295 0.216

3x3 contraharmonic

mean ﬁlter with Q = 1 18.023 0.126

3x3 gaussian ﬁlter 18.485 0.119

3x3 disk ﬁlter 18.590 0.118

Adaptive median ﬁlter

(min: 3x3 - max: 5x5) 18.444 0.120

Adaptive ﬁlter 3x3 16.298 0.153

tain threshold remain, of which an example is shown

below (as a 2D plot). The result is a smaller im-

age which uses less storage space and can be post-

processed faster. As can be easily seen in Fig. 3, the

image is corrupted by noise due to attenuation/fading

of the satellite signal and intermodulation with strong

out-of-band emissions, mainly from FM broadcasting

transmitters and GSM base stations. Various Image

Processing ﬁltering methods have been used to en-

hance the images and objective metrics (PSNR and

RMSE) allow us to compare the results. The results

are reported in Table 1. The disk ﬁlter is shown to be

achieving the best results of PSNR and RMSE. Once

the image is denoised (Fig. 4 left), we used a state of

the art cloud segmentation method to isolate clouds

pixels both from land pixels and the rest of the image.

Otsu method (Zhang and Hu, 2008) is a segmentation

method widely used for its simpleness and effective-

ness and its robustness against noise (usually present

in APT images). That being said, our main objec-

tive was the release of a new accessible platform to

receive NOAA signals and to enhance noisy images

and achieve better results in terms of remote sensing

imagery. We also show the results of clouds segmen-

tation achieved by Otsu method (Fig. 4 right).

5 CONCLUSIONS AND FUTURE

WORKS

In this paper we proposed a new easily accessible so-

lution for NOAA remote sensing. Although we used

low cost elements and hardware, we achieved good

results in terms of signal reception, demodulation,

synchronization and image enhancement. The sys-

tem is able to predict NOAA satellite passes, lock, de-

modulate and synchronize the signals. The output of

the demodulator is an image of the area scanned by a

A Low Cost Solution for NOAA Remote Sensing

133

line perpendicular to the ﬂight direction of the NOAA

satellite. We detected the clouds in the image by using

Otsu method (Zhang and Hu, 2008). In future works

we want to include the following solutions:

A more performant model for the receiver, such as

FUNcube Dongle Pro+ or HackRF One; the adoption

of a numerically controlled and built QFH antenna

and an ampliﬁer with lower noise ﬁgure and low sig-

nal loss; the development of an algorithm for thermal

maps generation based on the analysis of the infrared

images; noise proﬁling based on long time analysis

of radio frequency interferences located near the re-

ceiver. We aim to develop a modular solution for the

prediction of atmospheric phenomena, based on the

analysis of the images retrieved from several satellites

(each one transmitting at its own frequency and with

its own modulation scheme) at different times of the

day.

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