Generating higher accuracy digital data products by model parameter

International Journal of Electronics and Communication Engineering & Technology (IJECET),
INTERNATIONAL JOURNAL OF ELECTRONICS AND
ISSN 0976 – 6464(Print), ISSN 0976 – 6472(Online), Special Issue (November, 2013), © IAEME

COMMUNICATION ENGINEERING & TECHNOLOGY (IJECET)

ISSN 0976 – 6464(Print)
ISSN 0976 – 6472(Online)
Special Issue (November, 2013), pp. 196-201
© IAEME: www.iaeme.com/ijecet.asp
Journal Impact Factor (2013): 5.8896 (Calculated by GISI)
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IJECET
©IAEME

Generating Higher Accuracy Digital Data Products by Model Parameter
Adjustment and Bias Estimation for Indian Satellites
Vivek Sharma1, Debajyoti Dhar2, S M Moorthi3, R Ramakrishnan4
Data Products Software Group, Signal and Image Processing Area
Space Applications Centre, Ahmedabad, INDIA
1vivek@sac.isro.gov.in, 2deb@sac.isro.gov.in, 3smoorthi@sac.isro.gov.in, 4rama@sac.isro.gov.in

ABSTRACT: The digital images acquired by earth sensing space borne systems have acquired a
unique place in signal processing domain. This domain presents highly complex challenges to
image processing specialists to evolve efficient methods to generate high quality digital images.
Here we present an approach adopted for generation of higher geometric accuracy digital data
products for the optical sensors of Indian Remote Sensing Satellites using the formalisms of
image processing in an integrated manner. This approach uses automatic image matching
technique based on pattern identification in selected input image characterized by its correct
geo-location corresponding to its reference image. It uses the refinement of the geometric
sensor imaging model using the dependable parameters. As the imaging parameters do not
change drastically during the continuous acquisition for a given area, these selected
parameters are used to find the biases that hold for a good number of scenes. This approach is
conceptualized keeping in view the multi mission requirements of satellites wherein saving
product generation time is critical to achieve quick turn-around-time. An advantage of this
approach is to generate bias values once and use them multiple times later. This technique
ensures improved geo-location accuracy of the data products and provides the facility of
getting a useful insight into satellite behavior over the mission life.

KEYWORDS: Bias, IRS, Geometric Correction, Reference, Pattern, Sensor, Strip.
I.

INTRODUCTION

Through its indigenous remote sensing satellites Indian Space Research Organization (ISRO)
has demonstrated its expertise in design, development and operationalisation of remote
sensing programs. These satellites are well equipped with the technologically advanced optical
sensors that image earth at repeated intervals of time. The data imaged by the onboard sensors
is received on the ground by the ground Stations, processed for different levels of correction
and is then made available to the user. These processed images are used to cater to the user
applications such as agriculture, urban development, forestry etc world over. They are
expected to be of high geometric accuracy within limitations of their platform specifications.
Once the earth surface is imaged by the satellite, the signal data is transmitted to the Ground
International Conference on Communication Systems (ICCS-2013)
B K Birla Institute of Engineering & Technology (BKBIET), Pilani, India

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Station in real time and it is archived on central storage through a set of processes commonly
referred to as ground segment data processing. The high-end state of the art systems complete
all the required processing tasks in near real time to allow data products generation systems to
take over and generate the products in a systematic manner. This necessarily requires
corrections in acquired data due to earth rotations, data acquisition errors, platform biases etc.
This also involves image correction using some reference ground truth information referred to
as image registration. This demands a lot of time depending upon the required scene size and
the complexity involved in its processing. Also for every new mission separate preprocessing
parameters are to be used which are mission specific and it calls for a high maintenance over a
period of time. Hence if the gross level accuracy improvement indicator is provided at the
initial processing level itself then by applying minimal processing, quicker registration of
image data products can be achieved in sufficiently lesser time to get registered digital
products with higher accuracies. The diagrammatic representation is done in Fig. 1 to explain
the approach of providing initial level of accuracy measure through Bias Database. The
approach to generate such biases or accuracy improvement quantitative values is discussed in
next paragraphs. The pre-processing architecture of IRS core –preprocessing engine has come
quite handy in implementing this approach and is appropriately discussed wherever deemed
appropriate.

Fig. 1: Comparison of Existing and new Scenario for data processing of IRS satellites

II.

DATA PROCESSING OVERVIEW

The processing at level’0’ follows a well defined chain of steps in which acquired data file is
separated into video files and corresponding auxiliary information. The mapping of imaged
part in stored file with acquisition time is done through the time tag embedded in raw data
during acquisition. A scene is identified in raw achieved data by the fixed number of lines
characterized by a given line time {and/or) scan number that need to be separated from the
whole set of data acquired for processing purpose. To establish ease in accessing and
identifying the input data, image scene is identified based on path-row referencing scheme. It
helps in managing the association of imaged data with ground features. The downloaded video
data needs to be prepared for good visual quality and it is enhanced appropriately. This image
data is then taken as first input by Bias Estimator for matching against the reference image.
The second input taken by Bias Estimator is the ancillary information related to sensor imaging

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parameters, imaging times and other miscellaneous information related to the scene
acquisition geometry. Thus a physical imaging model of satellite is established which gives the
look direction and angle for each CCD element. Given ψ and ψ as pitch and roll angle
respectively of nth detector element, the unit vector along the look direction in Camera Coordinate system can be expressed as U = [ tan ψ, tan ψ,1 ]/sqrt(1+tan2 ψ+tan2 ψ). A series of
co-ordinate transformation is carried out to convert the look angle in Inertial Coordinate
system. Finally, using the Sidereal angle, the look vector is transformed into Earth Centered
Earth Fixed Coordinate system. Intersection of Look Direction vector with the earth surface
gives the geodetic coordinate of the ground point as represented by latitude, longitude and
elevation. The IRS Core Preprocessing Engine library [REFERENCE #1] provides this entire
sensor modeling work as a back-end to the Bias Estimator application. The engine library is
capable of using the complex functionality available in the objects of the shareable library and
is independent of any windowing toolkit. When an input image is map projected, it is typically
transformed from its input image space, through sensor model, and projected for on the fly
geo-referencing.

III.

IRS CORE LIBRARY AND ITS USAGE

The Core Engine Unified library [1] is based on generic model where different image
processing objects could be combined in an efficient manner. These intermediate objects inturn can be combined dynamically to carry out a complex process based on a template or a
model and then can be applied on any data set to get desired result such as an image mosaic.
The objects are property based and their states at any point of time can be saved or retrieved.
Thus any application can acquire the required processing potential to process a given input
using the core objects provided by the library. For example a simple display chain can have the
following objects:


A general raster image connector that can read/write/edit/ a binary file using pyramidal
structure.



A Histogram handler for handling different data types for that image



A display renderer which is responsible for creating different views for the given input and
present different abstracted views for the output

The concept of dynamic image chain provides an immense flexibility in developing sequential
operations on image set. A unique feature of the Core Engine is its capability to model any
optical IRS sensor using MVC (Model-View-Controller) approach with minimum efforts and
modifications. The abstract classes have been developed for IRS Sensor Model Factory and
binary image format that is typical input for all data preprocessing systems. The Library has
the capability to incorporate new formats and sensor models without disturbing the existing
structure. The library is designed using the concept of MVC (Model-View-Controller) as basic
architectural framework. MVC is a classic design pattern that has the ability to maintain
multiple views of the same data. The MVC pattern provides separation of objects into one of
three categories - models for maintaining data, views for displaying all or a portion of the data,
and controllers for handling events that affect the model/view(s). The raster data model of the
library is internally maintained and manipulated by the engine objects which are transparent
to the application


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IV.

BIAS ESTIMATOR

All the applications in image processing domain starting from machine learning to complex
data processing are tackled under specific formalisms, such as classification and clustering,
function approximation, data coding, restoration and enhancement, data fusion, feature
selection and extraction. The Bias Estimator is based on the identification of the corresponding
reference image, feature extraction in two similar images and mathematical computation and
optimization of the values of the sensor model parameters to generate the accuracy biases. In
Bias Estimator the core processing library provides the framework for getting on the fly
projected image of the scene through sensor model. It also finds the corresponding reference
image of the area using the scene corners in geographic space. The basic principle used in
adopting the new approach is to calculate the difference in geo-location accuracy of the
projected scene with respect to the reference image of the same area and mapping it in terms
of sensor model parameters adjustments that can be re-applied the original model as biases to
get accurately geo-located image. The process determines the scene extent in terms of world
coordinates while geo-referencing the scene on-the-fly and then searches for the
corresponding reference image in the pre-defined location to decide the ortho-image that falls
within the extent of target scene. In case of more than one reference scenes found image
mosaic can be generated for starting bias estimation process. Once the reference image is
generated through selection or mosaic, on the fly image registration is carried out using
Automatic registration technique with Harris Corner Detection and Random Sample Consensus
method [2], to find out the bias for attitude parameters of the sensor model such as roll, pitch,
yaw or their variations etc. These biases are then stored in a tuple identified row within
SatelliteBias database for usage at next level of product generation. The Bias Estimator carries
out image registration on projected image from the sensor model and reference image. It uses
the feature extraction mechanism of the two images and carries out parameter estimation,
image resampling and transformation (if required). It performs the geometric correction of the
image input on-the-fly using 2D-3D transformations. It can also be used to get an online
estimation of the biases where through the MVC architecture of the Core Engine multiple views
can be provided to display adjustable model generated geo-referenced image and can reflect
the latest adjustment of parameters simultaneously. The MVC architecture is shown in Fig. 2.

Fig. 2: Model, View, Controller arrangement in bias adjustment

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One viewer displaying the geo-referenced image and the other viewer allowing over-laying
geo-referenced image over the reference image. Each adjustment parameter after model
optimization can be expressed as follows to get calculated final value. Initially all parameters
are set to Zero by default. Each parameter is computed as:
value = initial_value + scale*sigma
Sample biases are given below:
ROLL_BIAS = 0.00345
PITCH_BIAS = -0.0023
YAW_BIAS = 0.0000
For Date of Pass: 12-feb-2012 Sensor LISS-3
This information is archived in SatelliteBias Database with database entries for almost all
dumps of data that satellite images over a period of time. The generation of the higher accuracy
data product is done easily by using these parameters directly from the database. Once the
ancillary data is ready and validated, the corresponding raw data for the scene is extracted
from archived data storage. The input parameters to identify tuple in the database are given
and the corresponding parameter values are retrieved using the database query. If biases are
already available in database they can be uniquely identified by tuple entry DOP-SEN-PATHROLL-PITCH-YAW and need simply be retried from the database and applied during the
geometric correct process. The Data Processing step then is ready to use the output of Bias
Estimation process that estimated attitude adjustment parameters earlier. These parameter
values are appropriately applied to the geometric model. This geometric model incorporates
the retrieved bias values to establish mapping (from scan, pixel) to geometric coordinates.
Hence the exact process of finding the bias with respect to reference template and estimation
need not be repeated again. In fact the entire scenes of the pass can avail this facility without
any loss of accuracy and that too products of all types that are generated for this scene. A
sample of these tuples is shown below.
DOP-SEN-PATH-ROLL-PITCH-YAW
01-JAN-2012:L3:100:+0.0003:-0.0023:-0.00034
01-JAN-2012:AW:100:-0.0020:+0.0015:-0.0000
01-JAN-2012:AW:114:-0.0032:-0.0010:-0.00001
.
.
05-JAN-2012:AW:095:+0.0005:+0.0008:+0.0010

V.

TEST RESULTS

TABLE1 shows the processing results of the application developed using the Core Engine
Library in the back-end for processing of Resourcesat-II data set for sensors Liss3 and AWiFS.
The results is the cumulative sum of time required for taking the input, pre-processing and
writing the scene data onto the disk as a product. The significant reduction in time is justified
based on the approach adopted. System Configuration used for the Exercise was HP Server
class machine having two processors Xeon @3.66 MHz with 2 MB L2 Cache and 4 GB of RAM.
Operating System is RedHat Linux Advanced Enterprise Server (AES) 4.0 with Kernel of 2.6.9,
32-bit version installed.


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Without Bias Usage existing scenario

With Bias Usage in new scenario

Sensor

Liss3/ AWiFS

Liss3/ AWiFS

Processing
Time(min)

24.38/34.93

4.3/5.2

Table 1: Results showing improvement in timings for test data sets

VI.

CONCLUSION

The suggested approach saves a lot of processing effort and time as compared to existing
scenario as it avoids multiple iterations of accuracy improvement for same scene at different
levels of data processing. It also gives a quick insight into the platform behavior i.e. stability
and deviation in Attitude and Orbit control systems of the imaging platform which is a good
analytical input for accurate measurements and health analysis of the system. The use of IRS
Core preprocessing engine has facilitated the incorporation of such estimation mechanism
through its framework support. The design of this library ensures the significant amount of
scalability and flexibility in incorporating additional functionalities such as Bias Adjustment.
The further scope of improvement is there in future in this approach as here limited overall
systematic correction is done in data products still further accuracy improvement can be
achieved using more number of iterations in parameter adjustments which may increase time
consumptions. For cases where systematic inaccuracies are as high as 2kms or more geometric
errors in input images this method can be highly efficient in practice for pre-processing of
satellite datasets with reasonable timings.

REFERENCES
[1]Vivek Sharma, Debajyoti Dhar, R Ramakrishnan “Software Architecture of IRS Core
Preprocessing Engine” International Cartographic Association INCA 2008 Gandhinagar India
[2]Misra,I.;Moorthi,S.M,;Dhar,D;Rama,R “An automatic satellite image registration technique
based on Harris corner detection and Random Sample Consensus (RANSAC) outlier rejection
model” RAIT, 2012, ISBN 978-1-4577-0694-3


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Generating higher accuracy digital data products by model parameter

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