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Dr Ganesh Prusty
Defence Terrain Research LaboratoryDRDO, Metcalfe House, Delhi-110 054
Geospatial Intelligence for Defence Preparedness
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----GeoINT is integral part of Intelligence Preparation of Battlefield (IPB)
----play a key role in Military Operations: base for strategic planning & tactical decisions
----GEOINT provides innovative, versatile solutions for meeting today’s demanding intelligence requirements and predicting tomorrow’s future threat environment.
Terrain analysis consists of interpreting natural and man-made features of a geographic area, together with the influences of weather and climate, to determine their effects on military operations.
GeoINT refers to exploitation and analysis of Satellite imagery, AP and geospatial information to describe, assess and visually depict physical feature and geographically referenced activities on the Earth.
Basic input to Terrain Analysis
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MILITARY REQUIREMENTS: DRDO Endeavour•Topographic/Terrain Mapping•Visualization- Strategic Planning•Terrain (scene) Matching for cruise missile guidance•War Gaming- Tactical operations, inter-visibility for optimal positioning•X-country trafficability Assessment•Training Simulators for mission planning & rehearsal•Cover & Concealment planning•Natural (Geological) hazards result in changed surface topography
Need of the hour : Rapid-Response – conventional field survey untenableAvailable data is archaicGeoINT of unexplored virgin trans-border terrainFast changing terrain : frequent updatesData in Digital format: Input to contemporary warfareDemand for high resolution TerraINT Interoperable Data for Network centric warfare
Image Intelligence from space platforms is the solution
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Geospatial Technology for Military Application
GeoINT Level-1Parameters
GeoINT Level-2Terrain Intelligence
Contour / micro-reliefSlopeSlope aspectPattern -featuresTexture - featuresState-of-the-groundLoad bearing capacityShear strengthforest cover/agriculturegeologygeomorphologyHydrologyHazard Potential Mapping
GeoINT Level-3Military Requirements
Terrain ReliefLand use/land coverSurface waterSoil characteristicsLandformsVegetationSoil Moisture
Geo-visualization: Strategic PlanningTrn Scene MatchingWar GamingTraining SimulatorGoing maps: Mission Plnground water potentialHazard MitigationCover & Conceal. planningTarget IdentificationLine of sightHazard Susceptibility MappingLanding zonesField of fireAmphibious crossing
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Oblique Ariel Photography
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Advantages of vertical over oblique aerial photographs•present approximately uniform scale throughout the photo and making measurements (e.g., distances and directions) easier and more accurate.
•constant scale throughout a vertical photograph, the determination of directions (i.e., bearing or azimuth) can be performed in the same manner as a map.
•easier to interpret, tall objects (e.g., buildings, trees, hills, etc.) will not mask other objects.
•simple to use photogrammetrically as a minimum of mathematical correction is required.
•To some extent and under certain conditions (e.g., flat terrain), may be used as a map if a coordinate grid system and legend information are added.
•Stereoscopic study is also more effective
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Advantages of oblique over vertical aerial photographs•covers much more ground area than a vertical photo taken from the same altitude and with the same focal length.
•If an area is frequently covered by cloud layer, there may be enough clearance for oblique coverage.
•more natural view because we are accustomed to seeing the ground features obliquely, will be more recognizable because the silhouettes of these objects are visible.
•Objects that are under trees or under other tall objects such as ridges, cliffs, caves, etc., may not show on a vertical photograph if they are directly beneath the camera.
•Determination of feature elevations is more accurate
•Because oblique aerial photos are not used for photogrammetric and precision purposes, they may use inexpensive cameras.
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Terrain Analysis Applications in tactical operationsArea analysis study
Line of Communication (LOC)Cover & ConcealmentCross Country Movement (CCM)
•Line of Site and Zone of Entry
•Geo-Visualization
•Flood modeling for strategic perception
•Mission planning & execution
•Geo-environmental analysis
•Decision Support System for hazard mitigation
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Worldview (2m)Cartosat (5m)DTED 30m
Input Data Resolution Dictates the Feature Scale
Improvement in Elevation Contour Resolution
Contours cannot be generated
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Cartosat1
Worldview
Input Data Resolution Dictates the Feature Density
For mapping 1:25K
For mapping 1:5K
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INPUTS FOR GIS
Geographical Information System
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Digital Elevation Model (DEM)DEM are the terrain elevations at regularly spaced horizontal intervals, i.e., a grid of regularly spaced elevations.
DEM reconstruction from Satellite ImageryInSAR Radargrammetry Stereogrammetry Clinometry
BandF
BandA Reconstructed DEM of Ladakh test Site
Cartosat 1 stereo pair
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DEM is the key to all scientific research related to earth surface.
Elevation modeling has become an important part of geo-spatial intelligence required for Military Operations & Planning. However, the data need to be contiguous.
Optical stereo mapping depends on appropriate weather & illumination conditions. Sometime it excludes certain region of earth temporarily.
Radar-grammetry present an alternative due to its cloud penetration capability.
Optical data is available with higher resolution, whereas, Radar data mostly available with lower resolution.
Synergistic use of optical & SAR data for DEM reconstruction can facilitate contiguous mapping and enhance the accuracy.
Comparison & Fusion of DEMs Derived FromComparison & Fusion of DEMs Derived FromMulti-date and Multi-sensor Satellite Data SourcesMulti-date and Multi-sensor Satellite Data Sources
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Scientific Rationale
1. DEM reconstruction
2. Co-registration & DEM normalization
3. Void filling
4. Accuracy assessment
5. DEM fusion based on weighted average (correlation image)• Expt-1 with multi-date same sensor data with different cloud
localization• Expt-2 with multi-polarization data• Expt-3 with multi-sensor data having different resolution and
characteristics
6. Validation
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Model Development
Ortho Img-O1DEM-1Corr Img-C1 Co-registration
MaskedDEM-1(D3)
MaskedDEM-2(D4)
Demarcation of cloud/shadow regions
•If C1=C2=0 then D1else[(D1*C1)+(D2*C2)] / (C1+C2)
•If D3<0 then D2
•If D4<0 then D1
Fused DEM
Ortho Img-O2 DEM-2 Corr Img-C2
Normalisation Normalisation
D1 D2
Validation
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Reconstruction of DEM & Ortho-rectification of PAN image
2008
2009
PAN-OrthoCorrelation ImgDEM
PAN-OrthoDEM Correlation Img
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Fusion
DEM Minimum Maximum Mean Standard Deviation
2008 256 953 587.28 114.66
2009 242 921 587.95 112.524
Fused DEM 388 950 588.52 112.67
Statistical observations
Fusion of Multi-date DEMs
2008
2009
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Elevation Histogram
Accuracy Assessment
Elevation
No
of
Pix
els
Results
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Cartosat
Radarsat
DEM Correlation Image
Multi-sensor DEM Fusion: Reconstruction
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Model Developed
Cartosat Radarsat
EITHER $n7_norm_radarsat IF ( $n12_masked==0. ) OR float (($n12_cartosat * $n13_c_score) + ($n7_norm_radarsat * $n5_r_score)) / float ($n13_c_score + $n5_r_score) OTHERWISE
Fusion
Fused DEM
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Error Histogram
Relief Category
SD
Accuracy assessment based on Relief categories
Accuracy Assessment
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Central Rimo G.
Teram Shehr G.
Siachen
G.
Saltaro Hills
Bila
fond
G.
Teram Shehr Group
Gyongla G.
N
Geo-VisualizationGeo-Visualization
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3D Visualization Engine
DTM Generation
Digital database creation
Terrain skin
generation
Feature Extraction+Merging
Enhanced DEM texture Image
Texture based Modeling and Visualization System
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Design of process flowDesign of process flow
DEM TIN Modeling DTM
Satellite Imagery Topographical Map
Texture Tile
Geo-visualized model
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Trafficability Potential / Going Maps
GM for Tracked Vehicle (L) and GM for Wheeled Vehicle (R)
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Texture based 3-D visualization System
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Test Sites: Desert, Runn, Coastal Plain, Alluvial plain & Cold desert
Remote Sensing Inputs
TerrainVariables
VIR band data
SAR (MW)data
SignatureExtraction
Model Building
SM estimationand Mapping
Approach Strategies
Multi-polarization strategyMulti-incidence angleChange detection strategy
FieldCampaigns
Data Processing
SynchronizedSame period
SOIL MOISTURE INVERSION MODEL
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=1-10%
=11-20%
= 21-30%
Soil Moisture Mapping
Data input
Multi incidence angle SAR
Multi polarized SAR
Surface roughness
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Apr_03
Oct_03
Jan_04
Feb_04
Apr_04
Moisture Mapping System
Soil Moisture temporal dynamics: Inversion Model Based on Microwave RS
Data: RADARSAT SAR
Also have Agriculture application
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TPMS: Automatic Generation of Terrain TPMS: Automatic Generation of Terrain Parameters- Soil Texture MappingParameters- Soil Texture Mapping
Technique: Rough Set Theory& CBR hybridization
AREA –DIAMOND HARBOUR
CLAY LOAM
SIL.C. LOAM
SILTY CLAY
SILTY LOAM
SANDY LOAM
SAN.C.LOAM
SANDY CLAY
LOAM
CLAY
SANDY
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Automatic Terrain Feature Extraction-Landuse Feature
• Features Extraction using Chip Mining approach
• Technique Used-Rough Set theory
• Better Accuracy achieved than statistical techniques of standard I/P software
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Automated extraction of Landforms from Multi-spectral imagery
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Area details• Physiographic Setting• Geological Set-up• Terrain Characteristics • Trafficability Analysis with Maps
Military Potential • Camouflage and concealment• Movement of Men and Animal• Camping Sites and Dropping Zones• Areas of Artificial Triggering• Areas susceptible to inundation
AREA ANALYSIS STUDY
Going MapIMAGE
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Cross country Movement (CCM)
•Trafficability potential of the denied or otherwise inaccessible areas.
•An assessment of trafficability requires knowledge of soil types (which are in turn controlled by the underlying bedrock type); the physical, chemical, and biological soil forming processes at work; and meteorological conditions.
•Creation of computer expert systems that will be able to combine map layers showing roads, soil types, topography, rivers, vegetation, and land use to produce probabilistic estimates of trafficability for specific vehicle types and weather conditions.
•Terrain is classified into three categories based upon trafficability: go, slow-go, and no-go.
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Natural Habitat Characterization: Turtle rookery dynamics using multi-temporal & multi-spectral RS data
Video Clip
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MAGIC: Progressive change of landforms’ configuration, evident from historical satellite datasets
Nov.’1988 Mar.’1991
Ekakula spit
Nasi sandbar
Satabhaya gap
Jan’1997
Feb.’1998 Mar.’2001Apr.’2003
Mar.’2004
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LISS 3PAN
Data fusion Land-water Delineation
Elevation Leveling & Contouring
Shoreline Extraction
Classification (20 classes)
Surfacing for DEM
Acquiring Timely Tidal measurement for each Images from Indian Tidal Tables
Target Shoreline(1.68m tide level)
Reference Shoreline (1.69m ref. level)
Process flow of Tide Elevation Normalized Change Detection: Nasi sandbars
Set of 7 multi-sensor reference data
PAN
01-01-97
Change detection
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0 0 0
4.50
2.302.98
2.90
6.206.50
6.80
3.40
6.956.72
3.84
6.59
-30
-20
-10
0
10
20
30
40
50
60
70
80
0 10 20 30 40 50 60 70 80 90 100 110 120 130 140 150 160 170 180 190
% s
urfa
ce a
rea
chan
ge w
ith
resp
ect
to r
efer
ence
yea
r
Bar turtle emergence in lakhs
Turtle nesting emergence in relation to surface area dynamics of Nasi barrier bar
Time period in number of months since Nov.1988
Turtle rookery dynamics characterization using multi-temporal & multi-spectral RS data
% S
urf
ace
are
a c
ha
ng
e w
rt r
efe
ren
ce y
ear 70%
-30%
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Digital Elevation Model (DEM) of Nasi I and Nasi II barrier bars indicatingeffective nesting surface area above highest high tide line of the season
1999Nasi I
1999Nasi II
2000Nasi I
2000Nasi II
2001Nasi I
2001Nasi II
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Estimation of dune celerity and sand flux using Cartosat-1 images: A case study of Gadra,
South Rajasthan, India
Study of dune migration in the dynamic desertic environment is of immense importance for military planning and operation. The Western sector of India shares a strategic international boundary. Since it consists of the dunes which are dynamic, dune celerity and sand flux studies are vital Geospatial Intelligence.
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2010 2011
Total no. of tie points 43 31
RMSE 0.665 0.87
Residual Report of DEM reconstruction and orthorectification
Residual Report of 2010 and 2011 registration
2010 & 2011
Total no. of Ground Control Points 23
RMSE 0.43
DEM Reconstruction and Co-registrationDEM Reconstruction and Co-registration
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Correlation and Dune Migration• Horizontal ground displacements are retrieved from the sub-pixel correlation of the pre and post-orthorectified images.
• Image correlation is achieved with an iterative, unbiased processor that estimates the phase plane.
• This process leads to two displacement images, each representing one of the horizontal ground displacement components (East-West and North-South)
Vector Field showing directional trend of displacement (left)
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Euclidean Displacement Map
2010 2011
Ortho Image
DEM
Euclidean Displacement Map
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5151
Accuracy Assessment
SNRMeasure of quality of correlation performedFor our study pixels < 0.9 SNR were excluded because of noisePrecision of correlation
To validate the correlation results observations are taken in the inter-dunal region where the displacement is supposed to be minimum.
Dune elevation map accuracyCalculated by seeing the variation of the reconstructed DEM with a reference
The standard deviation was found to be 2.5m.
Results
IndicesArea1 Area2
Mean SD Mean SD
EDM (m) 1.2959 0.8813 1.2079 0.8878
Sand Flux(m3/m/day) 0.0141 0.0384 0.0171 0.03409
Celerity (m/day) 0.0035 0.0024 0.0033 0.0024
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Decision Support System for Landslide Decision Support System for Landslide mitigationmitigation
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WATER : INDIA’S NEW BATTLEGROUNDWATER : INDIA’S NEW BATTLEGROUND (TerraINT as Force Multiplier) (TerraINT as Force Multiplier)
• Geo- politics on main river systems- Indus, Ganga and Brahmaputra
• AGRICULTURE• Per-capita demand
• HOSTILE NEIGHBOURS
WATER
Demographic factors
Energy(hydro-power)
Climate change
Industrialization
UrbanizationIrrigation
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Trans-border Rivers: Indo-Pakistan borderTrans-border Rivers: Indo-Pakistan border
• Control of the River Jhelum by India • Strategic edge to India and serious threat to Pakistan
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Trans- Border Rivers: Indo- China BorderTrans- Border Rivers: Indo- China BorderTibetan Plateau source of Indus, Brahmaputra and Satluj rivers.
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China Hydro-Hegemony in South AsiaProposed Projects in TAR
•Tibet water tower- supply 25%world•Diversion of trans-border river water: N-S Link•Using water as weapon: deny (drought)/ oversupply (flood)•Planned/executed 3 Major Dam Systems near IB•Indus, Sutlej & Bramaputra: Indo-China trans-border rivers•Present study: Flooding due to artificial triggering
Shanuan (Cascade)
Zangmu (3 gorges)
Motuo (U Bend)
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FMSP: Inundation mapping (in case of artificially triggered flood event)
TEST SITE: Trans- boundary River Siang
• ‘River characterization: Field input & RS
• Scene Modeling using Remote sensing data
• Geometric data input: Flood plain, channel c/s, discharge, channel roughness
•Modeling Envn: HEC-RAS, ArcGIS, CCH2D, River2D
Boundary Initial
Conditions
Flood plain DEM & Channel cross
section
Scene Modeling (RS data)
Hydrodynamic Modeling
(Numerical and Physical)
Depth, Velocity, Discharge
Inundation Area
3D visualized model
Flood Inundation Map
GIS Integration
MSS Imagery(23.5 m) Cartosat-1 DEM (20m)
Field Photograph: 14th- - 25th Mar10•Lead hr with dam burst
•Max flow depth for peak discharge
Collaborators: IIT-G & IIT-K
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Steady/unsteady flow: Physical Model (Hydraulics Laboratory at IIT, Kanpur)
Great ‘U’ Bend being exploited by China
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Longitudinal Profile of the river
Simulation Results of 1D- Steady flow conditions
Maximum flow depth for peak discharge
Lead Hour at different locations
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Simulation Results of 1D- Unsteady flow conditions
Initial and boundary condition for simulation : Normal depth as 0.004 m
Channel velocity at 80,000 cumecs
Hydraulic depth variation:
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MappingIMINT
Electro-Optical
DTMNight/Day
RADAR/IFSAR
Terrain classification
Camouflage detection
Multi/Hyper Spectral
GeoINT: supporting military operations
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HELICOPTERLANDING ZONES
OBJ
DEM Generation
MissionMission Planning & ExecutionPlanning & Execution
GeoINT typical scenarios
• Problem: Where can I land my humanitarian assistance team?
– Must consider slope, vegetation, obstacles, and proximity to Lines of Communications
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Geospatial Intelligence Mission Digital Elevation Models (DEM):
Reconstruction & fusion
Ortho image for Thematic Mapping: Natural & man-made feature extraction
DEM derivatives – contours, spot ht, slope and slope aspect
Flood modeling for strategic perception
Situational Awareness & Analysis System (SAAS) S/W : GeoINT analysis and 3D Geo-visualization
GeoINT products for Tibet & Myanmar (400km depth, 12.7lac Sq Km)
200km
400km
200km
400km
COK
T I B E T
I N D I A
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