HIGH RESOLUTION LAND- COVER MODELLING WITH PLÉIADES …

Mark ThompsonGeoTerra Image

HIGH RESOLUTION LAND-

COVER MODELLING WITH

PLÉIADES IMAGRY AND

DEM DATA IN SUPPORT OF

FINE-SCALE LANDSCAPE

THERMAL MODELLING

37th ISRSE Conference Pretoria, South Africa, 05 2017

Authors:

Mr Mark Thompson, GeoTerra Image, South Africa

Ms Lungile Moyo, GeoTerra Image, South Africa

Dr Corné Eloff, Airbus, South Africa

Mr Thinus Prinsloo, Pinkmatter Solutions, South Africa

Mr Neels Brink, DENEL Dynamics, South Africa


Airbus Channel Partner Conference – 11/5/201637th ISRSE Conference Pretoria, South Africa, 05 2017

INTRODUCTION

Using detailed land-cover data to

determine thermal landscape signatures …

Thermal signatures used as a navigation

aid by sensors onboard autonomous aerial

vehicles …

Airbus Channel Partner Conference – 11/5/2016

INTRODUCTION

Land-cover classes converted into thermal

units to match navigation sensor

requirements…



INTRODUCTION

0.5m resolution Pléiades imagery

used to generate high detail land-

cover and terrain data…


Data generated from Tri-Stereo

Pléiades imagery for enhanced

relief representation and metric

accuracy…


INTRODUCTION

Why? …

… using satellite data to simulate

landscape thermal radiance is significantly

cheaper than in-situ field measurements …



STUDY AREA

Complex mix of land-cover, land-use

and terrain variation…



PLEIADES TRI-STEREO TERRAIN DATA

Tri-stereo data uses 3 x views compared

to conventional stereo which uses 2 x

views…


• combination of 2 x oblique & 1 x

nadir views of the terrain…



Tri-Stereo provides enhanced 3D

modelling, especially in areas of high, local

relief variation, in both natural and man-

made landscapes…


Tri-Stereo approach ‘minimises’ data loss in

stereo ‘loss’ areas…


Stereo collection over very steep areas


View 1 View 2

Over large mountainous areas, where slopes are very high,

some « hidden » surfaces may remain

Front telescope

Rear telescope


“3-stereo” collection over very steep areas


View 2 (nadir)View 1 View 3


“tri-stereo” impact over steep areas


Tri-stereo

Stereo only


“tri-stereo” benefits: Melbourne


DEM from images 1 + 2

Merge of the 3DEMs

Melbourne:3D-wise,

a difficult area.

Hidden areas

extent (in red) are

reduced by approx.

75% when using 3

images

DEM from images 1 +3

DEM from images 2+3

//upload.wikimedia.org/wikipedia/commons/1/14/Melbourne_CBD_2008.jpg

//upload.wikimedia.org/wikipedia/commons/1/14/Melbourne_CBD_2008.jpg



High detail 1m resolution DSM and (derived) DTM

output, with ≤ 1.5m vertical and horizontal

accuracies …



MODELLING THE TRI-STEREO TERRAIN DATA

Challenges:


• Heterogenous landscape

• Thick vegetation

• Housing and infrastructure

• Waterbodies

• Mountains and lowlands



Further challenges:


• Large study area: 385 km2 extent

• requires 2 x tri-stereo acquisitions

• with 6 days acquisition difference



4 data processing steps:


4. Product generation Cycle

3. Orthorectification Cycle

2. Reference PAN Cycle

1. DEM Extraction Cycle




DEM Extraction Cycle:

Aim is to generate high geometric precision

0.5m DSM from which equivalent quality

DTM can be extracted.


Tri-Stereo generated DSM and derived DTM




Reference PAN Cycle:


Aim is to generate 0.5m high geometric

precision PAN reference images for future

MS data orthorectification…using the DTM /

DSM and external local ortho-photography

for ground control.



Orthorectification Cycle:


Aim is to generate comparable orthorectified

images (all views, all tri-stereo datasets), with

equivalent geometry and radiometric

properties.

Orthorectification model now uses reference

PAN image instead of local ortho-photo data



Product Generation Cycle:


1. PAN sharpened MS orthorectified image

2. Top-of-Atmosphere corrected

3. Full study area image mosaic


Final PAN Sharpened Pléiades ortho-image






GENERATION OF LAND-COVER / USE DATA

Object rather than pixel-based landscape

segmentation and classification for best

object boundary delineation.




Detailed LCLU data required to enable ID of

thermally equivalent thematic info classes.


• Going beyond normal LCLU detail…

• Roof material and colour

• Road markings and material type



Landscape segmentation based on PAN

sharpened imagery, since DTM exhibited local

object-edge gradient degradation…


Structural “gutter-lines” not

always represented as sharp

edges …



2m DTM threshold used to separate

ground from above ground features.


Separate, object-based classification of

ground and above-ground features, using

spectral & height characteristics…









Externally sourced spatial masks used to

assist with separation of objects with similar

spectral & height characteristics…


Buffered road centre-line

mask used to separate

white road markings from

similar light coloured

road-edge kerbstones.



Finally, shadowed areas identified and

removed for land-cover classification

consistency…



DSM Terrain Data



Hill-shaded DSM Terrain Data



Separation of 2m above ground objects



Identification of all shadow areas



Land-cover versus original PAN sharpened



Land-cover with and without shadows



CONVERSION OF LCLU TO THERMAL UNITS

Aerial navigation sensor relies on thermal

landscape representations…


• convert LCLU classes into thermal

equivalent units.

• LCLU categories matched to thermo-

dynamic materials database

• unclassified PAN image used to generate

pixel-level conversion template


Comparison of PAN, Land-cover & Thermal


PAN Land-cover Thermal units


CONVERSION OF LCLU TO THERMAL UNITS

Thermodynamic modelling can simulate any

time of day (incl. variable shadow extent)


Airborne camera effects (motion, noise,

blur) added for final navigation dataset.

Airbus Channel Partner Conference – 11/5/201637th ISRSE Conference Pretoria, South Africa, 05 2017

Documents

HIGH RESOLUTION LAND- COVER MODELLING WITH PLÉIADES …