Novel Approach for Orbit ID from Level-0 Data Using Fuzzy Logic

Data Quality Evaluation & Orbit Identification
from SCATTEROMETER Data Products Using
Modern Computing Techniques
By:- Mudit J Dholakia (14MTPOS001)
Under the guidance of
Dr. C K Bhensdadia(Head, Department of Computer Engineering, DDU-Nadiad)
Mrs. Anuja Sharma (Scientist-’SF’, IAQD/SIPA/SPDCG/SAC/Ahmedabad)

Presentation Roadmap
1
Introduction
2
Problem
3
Literature
Survey
4
Proposal
5
Implementation
5
Results
Conclusion

1
Introduction

Introduction
• SCATTEROMETER Overview
• Data Reception
• Data Products
• Data Quality Evaluation

Data Reception
• Data Product
• Imaging Information
• Non-Imaging Information
• Formats
• HDF4/5
• Network Common Data Form
(NetCDF)
• High/Low Rate Picture Transmission
(H/LRPT)
• Measurements for Direct
Ambiguity removal
• Inner/Outer fore/aft

Data Products
A systematic collection of near real-time data for
the purpose of Retrospective analysis.[21]

1
Introduction
2
Problem

Problem
• Level wise difference
• Mathematical Model
• DQE
• Problem Statement &Objectives

Level wise difference
• Digital Geometry • Level-wise difference
281(HH)/282(VV)
6103
32 samples
1 pixel
Dump wise
Level-0
Orbit-1
Orbit-2
Orbit-3
Orbit-4
Orbit wise
Level-1

DQE
• Minimum
• Maximum
• Mean
• Histogram
• Pattern Identification
• Matching
• Digital Elevation Model
• Scattrometric Analysis

Problem Statement & Objectives
• No ways to relate level-0 and
level-1B data
• Retrospective analytical
capability is absent.
• A DQE for Level-1B and Level-2A
has to be developed.
• Level-0 DQE has to be fine
tuned.
• It is mandatory to find the
correct segments of orbits.
• Without using the meta
information an anomaly free
approach has to be driven.
• There has to some ways to
artificially evaluate the orbit
identification task.

1
Introduction
2
Problem
3
Literature
Survey

Literature Survey
• QUICKSAT-DQE
• Voyager-DQE
• RAPIDSCAT-DQE
• RISAT-DQE
• RESOURSESAT-DQE

RISAT DQE
• Features
• Parameter Evaluation
• Report Generation
• Alarm Generation
• Plots
• Parameter Extraction
• Missing Functions
• Back-tracing
• Speedy Analysis
• Absence of modern computing
approaches
• Lack of automation
[22]

QuickSAT DQE
• Features
• Automated system
• Sensor Performance Evaluation
• Digital Quality Checks
• Report Generation
• Parameter Extraction and range
analysis
• Point Marking
• Tabular Comparison
• Relational Analysis
• Multispectral function analysis
• Near real-time evaluation
[6]

OSCAT & RAPIDSCAT DQE
• Features
• Multispectral Analysis
• Display of SAR raw images
• Plots
• Retrospective Analysis
• Reports
• Alarms
• Point Marking
• Back-tracing
• Relational Analysis
• Time wise analysis
• Effective Reports
[3],[23],[24],[25]

Literature Description Pros Cons
Wind Retrieval
Functions[8]
A mathematical formulation of measurement
of wind vector using standard deviation
Manual
Evaluation,clea
understanding
It is efficient but
time consuming
approach. Accuracy
is very low.
Requires Human
efforts. No
efficiency in Orbit
identification.
Quick DQE
Jet Propulsion
Laboratory[12]
Level wise wind vector and
orbitidentification
Automatic and
capable of change
detection
Non-resusable,time
consuming,
complex to
maintain
Automatic
Weather
Station[17] A configured system with orbit analysis
Accomodates
accuracy and trend
analysis
Error may occur
while back tracing

Literature Description Pros Cons
Voyager
DQE[11] Jupiter's geometry detection algorithm
Generalised
Reference for
ellipsoidal
geometries (X,Y,Z)
3-D computations
are highly complex
and requires heavy
resource
IBEX[13] Interstellar Boundary Explorer High efficiency
Orbit change
detection without
scientific backing is
not reliable
OKID[14] Orbit identification Keysfor near surface winds
Accuracy is best of its
kind
Bigger the geometry
higher the
computation.
Virtul
Meteorology
[15] Automated accomodation of orbits
Used for scientific
calibration
Not feasible fo real
time evaluation

1
Introduction
2
Problem
3
Literature
Survey
4
Proposal

Proposal
• Orbit Identification(OI)
• Fuzzy Logic based OI
• Pattern based OI
• DQE using flag bit-4
• A neural network based approach

Fuzzy Logic based Orbit Identification

Motivation for approach 1: Fuzzy
• OAT Derived Match data
• Time
• Latitude
• Longitude
• Attitude
• Roll
• Pich
• Yaw
• Position(x,y,z)
• Velocity(x,y,z)

Motivation for approach 1: Fuzzy

Orbit Identification using fuzzy logic[18]

Consideration of South pole crossing

Pattern based Orbit Identification

Overview
• Pattern Based Identification is used for automatic retrieval of orbits
from SAR images depending upon contents of images known as
features (i.e. a black chirp in the proposed case).
• Scanning consideration describes the process of accessing the
interested patterns.
• Simplicity of Action is the outcome of the proposal.

Proposal of Identification
• A normal C program with I/O & file
operations.
• Scanning Geometry
• Pixels in beams
• Number of scans
• Asymptotic Complexity
• O(m*n)
• m is number of scans
• n is number of pixels per scan

Flag Based DQE
(Neural Network Based DQE)

A grid of 2 classes
1
1 1
1
1
1
1
1
1
1
1
11
1
11
1
1
1
1
1
1
2
2
2
2
2
2
2
2
2 22
2
2
2 2
2 2
2
2
22 2
2
2
2
2
2
2
2
2 2
2
2
2
2 2 2 2222
2
2
2
2
2
2

1
Introduction
2
Problem
3
Literature
Survey
4
Proposal
5
Implementation

Implementation
• Prerequisites
• Experiment-1
• Fuzzy logic based OI
• Experiment-2
• Pattern Based OI
• Experiment-3
• Flag DQE using Artificial Neural
Network

Prerequisites
• Matlab R2013a
• JDK+Netbeans
• C editor & compiler
• Jasper Engine
• HDFView

Experiment-1
• If time1 then orbit1
*Using north pole crossing time

Experiment-1

Experiment-1
*Using equator crossing time

Experiment-1
*Using south pole crossing time

Experiment-2
Pattern based Orbit Identification
Sensor Signal Data Orbit Count Program
Resultant Fine-tuned
Orbit Statistics

Experiment-3
Neural Network Based DQE

1
Introduction
2
Problem
3
Literature
Survey
4
Proposal
5
Implementation
5
Results

Results
• Pattern based Orbit
Identification
• Neural Network based DQE
• Retrospective GRID-GUI
• Added clarity to reports

Pattern Based Orbit Identification
Results

Neural Network based DQE
Results

Conclusion
• Conclusion
• Future work
• Bibliography

Conclusion
• A Fuzzy Logic(FL) based orbit identification(OI) has been proposed in this work. To select the
attributes a preprocessed data set of Orbit Attitude Records are selected assuming extraction of
Level-0 and some processing steps have already been performed. Selection of time and latitude
has been justified using Geo-mathematical criteria. Then a short introduction to fuzzy systems is
given and using that approach the idea is implemented using matlab. This work has made a mark
in Data Quality Evaluation Modeling. This work is useful for the researchers to develop the
modern terrain clustering systems.
• In traditional systems this redundancy leads to waste of computational efforts. Instead, this
approach provides past knowledge, using which the new data can be segmented as well as
analyzed (using further integration) and improvements to the upcoming observation systems can
be carried-out. Moreover, it also reduces modeling complexity to compute the correlation
between two images based on geometry, i.e. existing image of DQE and the image which is
currently computed. It also improves the speed of a spatial data analysis system. Using second
approach also orbit identification task has been implemented with modeling complexity of O(n).
This makes a clear sense to computational geo-informatics about the patterns which are
important aspect of consideration which was neglected earlier. Binning also improves the
analytical standard for the better understanding of anomalies in data , indirectly in sensors.

Conclusion
• At the end work has been concluded with the initial startup for future
work, i.e. Neural Network based DQE. The Neural Network based
approach has been tested on randomly selected data products from
the source NRSC even though surveying the current system with best
accuracy. Making some more efforts towards the highest accuracy
this approach also can show at least a technique other than
conventional technique of flag based DQE . Concluding with the
retrospective DQE implementation to bridge the gap described in
problem definition this work encourages many researchers to work
for DQE and OI using modern computing techniques.

Future Work
• General Model Development
• Applicability to wider scope can be designed
• No parallel processes have been defined, thus work can be initiated
for such faster models
• GRID_UI needs many improvements for the final application, i.e.
deliverable
• More powerful soft computing with highest accuracy can be used for
betterment of DQE system.

Paper:1
A Novel Approach of Orbit Identification from Level-0
Data based on Level-1B convention for Scatterometer
using Fuzzy Logic
Mudit J Dholakia, Anuja Sharma, Dr. C. K. Bhensdadia
XPLORE COMPLIANT ISBN :- 978-1-4673-7807-9
CD ISBN:- 978-1-4673-7805-5
PRINT ISBN :- 978-1-4673-7806-2
IEEE INTERNATIONAL CONFERENCE ON SYSTEMS AND CONTROL (ISCO)
KARPAGAM COLLEGE OF ENGINEERING ,COIMBATORE,TAMIL NADU

Paper:2
Pattern Based Orbit Identification for SCATTEROMETER
Level-0 Signal Images
Journal title: Procedia Computer Science
DOI information: 10.1016/j.procs.2016.03.049
INTERNATIONAL CONFERENCE ON CLOUD COMPUTING AND VIRTUALIZATION (ICCCV)
THAKUR COLLEGE OF ENGINEERING,KANDIVALI,MUMBAI,MAHARASHTRA
IN ASSOCIATION WITH ELSEVIER PROCEEDIA COMPUTER SCIENCE ,ELSEVIER B.V. AMSTERDAM, NETHERLANDS

Paper:3
Analytical Approach for identification of orbits from
SCATTEROMETER Level-0 Noise Images
International Journal of Applied Information Systems (IJAIS) – ISSN : 2249-086803.049

Paper:4
Pattern Based Orbit Identification for SCATTEROMETER
Level-0 Signal Echo Window Images
Xplore ISBN:- 978-1-4673-8208-3
DVD ISBN:- 978-1-4673-8207-6
IEEE INTERNATIONAL CONFERENCE ON INNOVATION IN INFORMATION AND COMMUNICATION
SYSTEMS (ICIIECS)
KARPAGAM COLLEGE OF ENGINEERING, COIMBATORE ,TAMIL NADU

Paper:5
Land-Sea Classification using Machine Learning
Technique for SCATTEROMETER L1B Globe
Number by ACM – ICPS for Proceeding Volume: 978-1-4503-4213-1049

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