Synthetic Images Simulation (SImS): A Tool in Development

Carlos Alberto Stelle

, Francisco Javier Ariza-López

and Manuel Antonio Ureña-Cámara

Brazilian Army Geographic Service, Quartel General do Exército, Bloco “F”, 2º Piso, Setor Militar Urbano,

70630-901, Brasília, DF, Brazil

Department of Cartographic, Geodesic and Photogrammetry Engineering, University of Jaén, Edificio A3,

Campus Las Lagunillas, s/n, 23071, Jaén, Spain

Keywords: Synthetic Image, Remote Sensing, Simulation, Blender.

Abstract: Images from remote sensing are presented as the main and most relevant data produced by this technology

due to the numerous applications in the most diverse areas of knowledge. In this context, simulating these

products can mean a significant reduction in costs, time, as well as assisting in the design stages of future

sensors in the laboratory. One of the challenges of simulation is to reduce as much as possible the gap between

it and the reality one wishes to study. In this context, the purpose of this work is, from a brief review of the

methods of simulation of passive sensor images, present a proposal to classify them, to cite some examples

of each, to present the conceptual model that is being developed, to mention aspects which provide versatility

and functionality as well as some results.

1 INTRODUCTION

Synthetic Images (SI) are those created by using of

computational resources and/or Virtual Reality (VR)

with specific modelling software or methods to make

it possible to explore and suggest different situations

to visualization, immersion and interaction as

facilitator in complex works of learning, through the

creation an environment in which the data generation

conditions are reproduced.

The evolution of computational resources in

hardware and software has allowed advances in

visualization and manipulation of information due to

the growing need to represent the reality and many

real-world information. Thus, simulation and

modelling tools represent one way to support a

number of the design, implantation and operational

studies (Schott et al., 2010).

On the other hand, Remote Sensing (RS) images

from optical satellites allow to allow to acquire

information about the surface of the Earth by means

of capture energy reflected or emitted energy without

physical contact between the system sensor and the

object or sensed area. These images are used by

professionals from numerous and different areas. In

this sense, simulation of RS images, even previous to

have a physical device, would permit to determine

usability and the capabilities of and existing or

planned RS system.

Following the previous ideas, simulation and

modelling tools represent one way to support a

number of the design, implementation and

operational studies, being more common:

 Meteorology – weather forecasting, monitoring

of atmospheric changes, pollutant control,

measurement of greenhouse effect and hole in

the ozone layer

 Civil Defence – prediction of natural disasters

that allow preventive measures to be taken

 Planning and monitoring of agricultural crops

 Planning and monitoring of urban growth

 Monitoring of forest areas to detect fires and

other forms of deforestation

 Military uses – espionage, tracking of enemy

movements and strategic planning ot troop

positioning

 Sensors development

 General users

Compared to other areas of RS, the few works

published on simulated images usually are focused on

specific applications. For example, an existing web-

based hyper-spectral image generator for cotton crop

(Alarcon and Sassenrath, 2004; Sassenrath et al.,

2003) was enhanced to output averaged continuum-

removed reflectance curves for synthetically

Stelle, C., Ariza-López, F. and Ureña-Cámara, M.

Synthetic Images Simulation (SImS): A Tool in Development.

DOI: 10.5220/0006807903130318

In Proceedings of the 4th International Conference on Geographical Information Systems Theory, Applications and Management (GISTAM 2018), pages 313-318

ISBN: 978-989-758-294-3

313

generated images. Another application can be seen in

Marcal et al. (2010) in the use of Synthetic Image

TEsting Framework (SITEF) as a tool to evaluate and

compare image segmentation results.

An efficient way to produce a simulated image is

to create an environment in which the data generation

conditions are reproduced. Ientilucci and Brown

(2003) emphasizes that the image simulation

surrogate must ideally match real world

scenes in both spatial and spectral complexity for one

to have faith in algorithm performance. Thus,

radiometrically, as well as geometrically, correct

synthetic imagery offers algorithm developers a

surrogate to potentially unattainable field campaigns.

To this end, the NASA, through the Rochester

Institute of Technology (RIT), has initiated a program

to build a synthetic scene-sensor model called Digital

Imaging and Remote Sensing Image Generation

(DIRSIG) model is designed to produce end-to-end

image simulations incorporating all the relevant

characteristics of images (Schott et al., 2012).

This paper presents the current stage of tool

Synthetic Image Simulation (SImS) that aims to

create passive remote sensing images from a 3-D

model with real-world features.

2 IMAGE SIMULATION

METHODS

An important issue about images simulation is the

quality or fidelity of the models. Some applications

need only change the pixel size of the image, others

need reproducing the radiation levels into the scene.

Thus, it is concluded that the methods must be

adjusted for each problem and it is necessary to

understand them in a brief review.

Considering that the satellite images are generated

according to four resolutions (spatial, temporal,

radiometric and spectral) and that to simulate is to try

to reproduce a data under controlled conditions, then

it is reasonable to say that the efforts of the simulation

should be directed to the spatial and spectral

questions, which are the characteristics most

evidenced in the applications. Simulating the

temporal question can be obtained from the

ephemeris of a sensor and in this way, use a model to

estimate the data at a desired time. Simulating the

data with the least amount of bits is also relatively

easy, as it is enough to compress a range of gray levels

to a single value, which is no longer so trivial in the

inverse process. Usually the increase of bits in the

quantization after the produced image is achieved by

the addition of noise. Thus, at this moment, this work

will focus on the spatial and spectral issues and the

components that affect them.

Although there are many simulation methods,

there is no one that consolidates them. Therefore, in

this paper we propose that we can classify them in two

main categories: Computational Based (Based on

Existing Images and Totally Synthetic) and Analogue

Based.

Table 1: Proposed classification of methods.

Analogue

Based

Models /

Dioramas

Francis et al. (1993)

Maver and Scarff (1993)

Computational

Based

Based on

Exiting

Images

Justice et al. (1989)

Esposito et al. (1998)

Boggione et al. (2003)

Chen et al. (2008)

Yang et al. (2009)

Nelson et al. (2009)

Maeda et al. (2008)

Totally

Synthetic

Ientilucci and Brown (2003)

Schott (1997)

Latorre et al. (2002)

2.1 Analogue based

Physical models may include terrain, ground cover,

structures and vehicles, with scene detailing

depending on the resolution of the sensor to be

simulated. This approach is described by Francis et

al. (1993). The scene is illuminated with a collimated

beam to simulate the sun and many diffuse sources to

simulate the sky as shown in Figure 1. The sensors

are located above the model and the optics is adjusted

to simulate the desired field of view. The image in this

case is designed to represent the radiance field in the

sensor. It is also easy to change the camera and the

sun angles to generate multiple images of the same

scene. This methodology has the disadvantage that it

is necessary to ensure that high reflectance and

reflectance variation are included in this scenario.

This problem becomes severe when this approach is

used to simulate multispectral scenes. Maver and

Scarff (1993) describe a hybrid approach to simulate

Figure 1: Simulation using physical model (Schott, 1997).

GISTAM 2018 - 4th International Conference on Geographical Information Systems Theory, Applications and Management

314

multispectral scenes where physical models and

lighting are used to generate scenarios that are then

processed through computer vision.

The advantage of this approach is that some of the

spatial variations and interactions of certain materials

can be included in the design of the model. The

disadvantage is that generating complex scenes can

become difficult, requiring in many cases

considerable manual editing.

2.2 Computational based

This approach involves simulation techniques that

propose methods in which the simulated images are

generated from other images or a synthetic scene.

These techniques typically address the degradation of

a better-resolution image to generate images at worse

resolutions, although there are reverse cases. These

techniques allow to simulate scenes close to reality.

2.2.1 Based on Existing Images

Justice et al. (1989) present aspects of spatial

degradation generating simulated images in six grids

of different resolutions in a range of 79 m to 4000 m

from the Multispectral Scanner / Landsat (MSS)

sensor.

Esposito et al. (1998) present the simulation of the

images of the CBERS cameras, which at the time of

this work had not yet been released, using AVIRIS

(Airborne Visible / Infrared Imaging Spectrometer)

images. It was necessary to extrapolate the spectral

radiance measured by AVIRIS at 20 km altitude to

the altitude of the CBERS orbit at 778 km and the

MODTRAN 3.0 program was used for this

calculation, providing the transmittance values at

each wavelength. For the difference in time taken for

the images (CBERS programmed to pass over the

equator at 10:30 a.m., and the AVIRIS images of the

study were collected between 1:30 p.m. and 4:30

p.m.), and knowing the lighting conditions of the

scene, the solar zenith angle was calculated for both

the AVIRIS transit time and the CBERS transit time,

obtaining a correction factor that was applied

throughout the scene.

Boggione et al. (2003) present the possibility of

simulating a panchromatic band for Landsat 5 from

its spectral bands. In this work the restoration

technique combined with interpolation is used to

generate images in smaller grids. The spectral

question is solved using the relation between areas of

the spectral curves of the bands of Landsat 7 and its

panchromatic.

Chen et al. (2008) propose a simulation method to

acquire simulated hyperspectral images using

spectral low resolution images. The proposed method

uses the idea of pixel mixing to understand the

relation between the spectral values of a pixel of the

image and to simulate the processes of radiation

transmission.

Yang et al. (2009) presents the simulation of high

resolution images in the mid-infrared spectral range

using an analytical model of radiative transfer of the

atmosphere. Unlike other spectral ranges, the average

infrared is highly dependent on atmospheric

dispersion and emissions.

Nelson et al. (2009) who used the simulation of

images to study the effects of resolution in the

estimation of forest areas, simulated images of 90 to

990 meters of resolution from images of 30 meters

resolution of the Landsat system.

Maeda et al. (2008) using the ETM + / Landsat7

image degradation technique and nearest neighbour

resampling simulated and evaluated the potential of

WFI / CBERS-3 images for land use and land cover

classification in two regions with distinct landscape

characteristics.

2.2.2 Totally Synthetic

With DIRSIG model, Ientilucci and Brown (2003)

produced imagery that can be used to test the

performance of spatial and spectral image

exploitation algorithms and concluded that synthetic

data should be considered a powerful tool to assist in

the testing of algorithms and potentially as a surrogate

when real data is not available.

According to Schott (1997), an alternative to the

use of analogue based models is a totally synthetic

approach, where scene elements, radiation

propagation, and sensor effects are simulated using

computational modeling. This approach is more

interesting because it allows infinite variations,

essentially, in the processes of adjustment of scene

elements and interaction. On the other hand, the

computational complexities of this approach in terms

of coding and run time are disadvantages. In practice,

the idea is to accurately model the physical processes

that occur in the process of imaging. The result is a

hi-fidelity model that can provide information about

the imaging process as well as the simulated image

itself. Typically, a model is used to generate an

estimate of the radiation from the target to the sensor.

This model is often associated with a model of

radiation propagation, such as MODTRAN

(MODerate resolution atmospheric TRANsmission)

used to calculate the level of surface radiation in the

Synthetic Images Simulation (SImS): A Tool in Development

315

microwave, near infrared, visible, and ultraviolet

(Latorre, 2002). It is a code that can be used to predict

spectral radiance for various geometries and

atmospheric conditions. Atmospheric propagation

models often have a database of atmospheric

conditions that are needed as input. Finally, a sensor

model must be available to characterize the sensor

location, acquisition geometry, field of view,

resolution, spectral response and radiometric.

3 RESULTS

The development of this simulator (SImS) is centered

in this last proposal (Schott, 1997) and it aims to

generate, from free software and vector cartography,

in an automated way satellite synthetic images. This

simulator presents independence from analogue

based, other images or commercial software which

means that images can be generated from random

scenarios and created according to input parameters

of users, as well as pseudo-synthetic images from

cadastral data for example.

Following this, we select the free software

Blender 2.78 to create tools that generate images

using its integrated Python 3 programming language.

Some strategic decisions are needed to be defined

for the development of SImS and the main ones were:

 The indicated free software Blender 2.78 – is

the free and open source 3D creation suite. It

supports the entirety of the 3D pipeline —

modelling, rigging, animation, simulation,

rendering, compositing and motion tracking,

even video editing and game creation. Also

customize the interface layout and colours and

combine 2D with 3D.

 Python programming language – Blender has a

flexible Python controlled interface and layout,

colours, size and even fonts can be adjusted as

well as it is possible create own using Blender's

accessible Python API.

 Geospatial Data Abstraction Library (GDAL) -

is a translator library for raster and vector

geospatial data formats and the gdal_edit.py

script can be used to edit in place various

information of an existing GDAL dataset

(projection, geotransform, metadata).

 OpenCV - is the computational view in which

we can use computational algorithms to

describe and analyze the content of any image.

It is free for academic use and it has Python

interface and support Windows.

 Object format - the Wavefront .obj file is a

simple data-format and it is considered a

"universal format" for 3D object

representation. It is represented in ASCII

format and recognized by most 3D

modelling/visualization software.

The conceptual model of the main modules can be

seen in Figure 2.

Figure 2: Conceptual model.

The main parameters to be inserted in the satellite

sensor module should allow the definition of the

program, platform and sensor from a library of

satellite platforms. Their spatial position will be

obtained from their own orbital parameters or

ephemeris.

Currently, the parameters that the user can define

are those shown in Figure 3. In this figure, Band 2 of

the OLI sensor (Operational Land Imager), Landsat 8

is highlighted as spectral information to be simulated.

Figure 3: Simulator toolbar.

GISTAM 2018 - 4th International Conference on Geographical Information Systems Theory, Applications and Management

316

Information such as the geographical coordinates

(latitude, longitude), date, time and types of roads can

also be defined.

The Illumination module consists of simulating

the behaviour of incident solar rays in a raytracing

system compatible with reality.

The Atmospheric Model module, also comes from

a library to be implemented, will aim to predict in a

simple way only the dynamic state of the atmosphere.

These simple model will be defined only for RS

purpose and will not suitable for weather or climate

forecasting.

With regards to the module Digital Surface Model

(MDS), in development, automatically and according

to a user’s defined criteria, different layout of streets,

blocks, vegetation and buildings are textured.

Nowadays, this model allows to simulate static textures

defined from the ASTER Spectral Library (Baldridge

et al. 2009) like Construction Asphalt, Construction

Concrete, Conifer and Aluminium Metal, respectively.

Developing importers of MDS and spectral libraries

are the future stage, mapping validity will ensure

greater variability of 3D scenarios.

The synthetic image will also be associated with a

synthesis metadata file. Nowadays, the export module

allows to create a synthetic image and in its future

version, it will create a text file with the creation

parameters that corresponds to the end of the creation

process.

Figure 4 and 5 show, respectively, a simulated

scenario in its real appearance given the pre-defined

geographical and lighting conditions, and the sensor

display in nadir position (upper view). The

parameters defined as follows:

 Latitude: 37.78º

 Longitude: 3.78º

 Day: 25

 Month: August

 Year: 2017

 Hour: 1000

 UTC: 2

Figure 4: Simulated scenario (real appearance).

Figure 5: Simulated scenario (upper view).

Finally in order to show the versatility and

potentiality of the simulator, Figure 6 shows the same

image that corresponds to the OLI sensor, Landsat 8,

Band 5 Near-Infrared (0.85 - 0.88 μm), but with a

spatial resolution of approximately 1m and not 30m.

Figure 6: Simulated scenario (upper view).

4 CONCLUSIONS

In this paper we described the present state of a

software and its model to create synthetic images

from the simulation of a 3D scenario.

The development of this tool, based on free

software and using independent sources of

geographical information, allows the user to generate

a scene in which it is able to control all the stages of

Synthetic Images Simulation (SImS): A Tool in Development

317

creation and to obtain an image as similar as possible

to what would obtain a real system. In this sense, the

simulator tries to generalize its use to different

disciplines.

Currently the user is allowed to define day, month,

year, time, latitude, longitude, streets, structures and

vegetation for the random creation of a scenario that,

with probabilities or specific quantities of objects, can

be exported in the respective spectral and spatial

resolutions of interest.

Moreover, the present tool allows the user to

define the desired spectral band and spatial

resolution. This flexibility is fundamental to the make

SImS a universal tool. For this reason, as an example,

images corresponding to the OLI (Landsat 8) sensor

spectral resolutions were presented in this paper.

Our future work will consist of integrating other

elements of the territory, such as elevation models

and different atmospheric models with different

meteorological parameters. In this way it will be

possible to parameterize sensors and platforms for the

effective integration in the generation of synthetic

images, considered by the countries strategic factor.

ACKNOWLEDGEMENTS

The authors acknowledge the Regional Government

of Andalusia (Spain) for the financial support since

1997 for their research group (Ingeniería

Cartográfica) with code PAIDE-TEP-164 and the

Department of Science and Technology of the

Brazilian Army.

REFERENCES

Alarcon, V. J. and Sassenrath, G. F. 2004. Cotton crop

spectral imaging analysis: a web-based hyperspectral

synthetic imagery simulation system. In: Remote

Sensing and Modeling of Ecosystems for Sustainability,

(Gao, W. and Shaw, D.R., Eds.). Proceedings of SPIE,

Vol. 5544, 178-185.

Baldridge, A. M., S. J. Hook, C. I. Grove and G. Rivera,

2009.. The ASTER Spectral Library Version 2.0.

Remote Sensing of Environment, vol 113, pp. 711-715.

Boggione, G. A.; Pires, E.G; Santos, P. A; Fonseca, L. M.

G. Simulation of Panchromatic band by spectral

combination of multispectral ETM+ bands.

International Symposium on Remote Sensing of

Environment (ISRSE), Hawai, Nov. 2003.

Chen, F.; Niu, Z.; Sun, G.; Wang, C.; Tang, J. Using low-

spectral-resolution images to acquire simulated

hyperspectral images. International Journal of Remote

Sensing, v. 29, p. 2963-2980, 2008.

Esposito, E. S. C. Simulação das bandas espectrais das

câmaras CCD e WFI do satélite CBERS, a partir de

dados do sensor hiperespectral AVIRIS. versão: 1970-

01-01. Dissertação (Mestrado em Sensoriamento

Remoto) - Instituto Nacional de Pesquisas Espaciais,

São José dos Campos, 1998.

Francis, I; Maver, L.; Schott, J.R. Comparison of physically

and computer generated imagery. Proceedings SPIE, v.

1904, p. 20-23, 1993.

Ientilucci, E.J.;Brown, S.D. Advances in Wide Area

Hyperspectral Image Simulation. Proceedings of SPIE

Vol. 5075 (2003).

Justice, C. O.; Markhan, B. L.; Townshend, J. R. G.;

Kennard, R. L. Spatial degradation of satellite data.

International Journal of Remote Sensing, v. 10, n. 9, p.

1539-1561, 1989.

Latorre, M.; Abílio, O.; Júnior R, D. C.; Paula, A.;

Carvalho, F. D.; Shimabukuro, Y. E. Correção

Atmosférica: Conceitos e Fundamentos. Espaço &

Geografia, v.5, n. 1, p. 153-178, 2002.

Maeda, E. E.; Arcoverde, G. F. B.; Formaggio, A. R.;

Shimabukuro,Y. E. Evaluation of the potentiality of

WFI/CBERS-3 Sensor data for land use and land cover

classification. Revista Brasileira de Cartografia, v. 1,

n. 60, p. 79-87, 2008.

Marcal, A. R. S.; Rodrigues, A.; Cunha, M. "Evaluation of

satellite image segmentation using synthetic images,"

2010 IEEE International Geoscience and Remote

Sensing Symposium, Honolulu, HI, 2010, pp. 2210-2213.

Nelson, M. D.; McRoberts, R. E.; Holden, G. R; Bauer,

M.E. Effects of satellite image spatial aggregation and

resolution on estimates of forest land area.

International Journal of Remote Sensing, v. 30, n. 8, p.

1913-1940, 2009.

Maver, L.; Scarff, L. Multispectral image simulation.

Proceedings SPIE, v.1904, p. 144-160, 1993.

Sassenrath, G. F., Alarcon-Calderon, V. J., Pringle, H. C.

2003. “Synthetic imagery of cotton crops: Scaling from

leaf to full canopy.” Digital Imaging and Spectral

Techniques: Applications to Precision Agriculture and

Crop Physiology. T. van Taoi, ed.. pp. 111-133.

Agronomy Society of America Special Publication

Number 66, Madison, WI.

Schott, J. R. Remote sensing the image chain approach.

New York, NY: Oxford University, 1997 394305169.

Schott, J.R.; Raqueno, R.V.; Raqueno, N.G.; Brown, S. D.

A Synthetic Sensor/Image Simulation Tool to Support

the Landsat Data Continuity Mission (LDCM). In

Proceedings of ASPRS 2010 Annual Conference, San

Diego, CA, USA, 26–30 April 2010.

Schott, J.; Gerace, A.; Brown, S.; Gartley, M.; Montanaro,

M.; Reuter, D.C. Simulation of Image Performance

Characteristics of the Landsat Data Continuity Mission

(LDCM) Thermal Infrared Sensor (TIRS). Remote

Sens.2012, 4, 2477-2491.

Yang, G.; Liu, Q.; Liu, Q.; Huang, W.; Wang, J. Simulation

of high-resolution mid-infrared (3–5 mm) images using

an atmosphere radiative transfer analytic model.

International Journal of Remote Sensing, vl. 30, n. 22,

p. 6003–6022, 2009.

GISTAM 2018 - 4th International Conference on Geographical Information Systems Theory, Applications and Management

318