Image analysis and A.I. for Earth Observation applicationsSRBIJA 2018 exercice (8-11/10/18) Video (1...
Transcript of Image analysis and A.I. for Earth Observation applicationsSRBIJA 2018 exercice (8-11/10/18) Video (1...
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Image analysis and A.I. for Earth Observation applications
From data to information
RMA/ Brussels 26_03_2019
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remotesensing.vito.be
VITO RS in a nutshell
• VITO (Flemish Technological Research Institute)
• ~ 700 people
• Private but non-profit (shares in the hand of the Flemish government)
• Bridge between universities and industry (very applied research)
• 172 M€ turnover in 2017
• Research domains : Energy, Chemistry, Materials, Health and Environment
• Remote Sensing Department
• 85 people mostly working on (automated) image processing
• Archiving and Data Processing Center > 7 PBy
• More than 20 years operational satellite data processing
• More than 10 years operations with drones (environment, agriculture,
water, infrastructure, forestry and security)
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remotesensing.vito.be
Satellite/airborn activities
• Data archiving and processing (geometric, radiometric corrections,…)
• Data fusion (SAR, Lidar, RGB, Thermal, Hyperspectral)
• Belgian Ground segment (e.g. Copernicus Sentinels satellites)
• Daily coverage of the whole world at 300/100 m resolution
• 20 years data
• S1 and S10 products
• Service for e.g. private company, farmer groups, FAO, …
• Satellite sensors (also cubsat)
• Specifications
• Design
• Performances assessment (modeling)
• PI of consortium with industrial partners
• Sensor calibration
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remotesensing.vito.be
RS products and solutions
Sensors
Vegetation Agriculture Water Environment
& Security
Markets
Value Added Services
& Information
Products
UAV AIRBORN HALE UAV SATELLITE
Infrastructure
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STANDARD IMAGE
PROCESSING
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remotesensing.vito.be
From data to information
• Stitching (video or fixed frame)
• Image georeferencing => orthophoto
• 3D reconstruction and analysis
• Automated object detection (color, shape, …)
• Automated segmentation and classification
• Time series analysis
• Data fusion (e.g. RGB-LIDAR or SAR)
• Spectral signature (multi and hyperspectral sensors)
• Visualization and data management (Terrascope, Watchitgrow, Cropmap,…)
• Using the cloud of our own data center (7 Pby)
LIDAR
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remotesensing.vito.be
Test performed live during the NATO
SRBIJA 2018 exercice (8-11/10/18)
Video (1 min.) = 750 MB
Orthophoto : 72 MB
Thanks to Mister W. Vanhamme
who provided the images
Medium resolution
Size = 10 MB
(factor 75 lower)
Degradated version
0.06 MB
(factor 12.500 lower)
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USING DEEP
LEARNING ALGORITHMS
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remotesensing.vito.be
Use of Artificial Intelligence (Neural Networks) for
image processing in Remote sensing applications
Convolutional Neural Networks (CNN) => Spatial information
• Diabetic retinopathy (not remote sensing)
• Cooling systems
• Asbestos
• Small canals
• Parcels
Recurrent Neural Networks (RNN) = > Temporal information
• Fraction of Absorbed Photosynthetically Active Radiation
(fAPAR) using fusion of S-1 and S-2 data
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remotesensing.vito.be
Detection of Diabetic retinopathy based on
Retinal image analysis
Diabetic retinopathy (DR) is the leading cause of blindness in the working-age (20 – 74) population of
the developed world and is estimated to affect over 93 million people worldwide.
Accuracy 97% (allowing error of 1 class)
Automatic vessel
segmentation
Took 2 weeks to train the ‘high
resolution’ (512 x 512 pixels) models
Allowed us to check thousands of retina
pictures on DR for the Qatar Biobank
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remotesensing.vito.be
Detection of industrial cooling systems
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remotesensing.vito.be
Water based cooling systems are
potential sources for Legionella
Mandatory to register
Facts :
~ 300 registered,
~ 4.000 estimated (based on sales)
Control/detection happens currently with inspectors on terrain
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remotesensing.vito.be
Examples of cooling towers (training)
Positive examples Negative examples
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remotesensing.vito.be
Examples of cooling tower detection
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remotesensing.vito.be
Examples of cooling tower detection
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remotesensing.vito.be
Detection quite successful
Limited data set for training and validation (~ 300)
Large variety of systems
Detected ~3.000 installations
False positive were eliminated manually
Some unit not detected as not provided for the training set
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remotesensing.vito.be
Automatic detection of corrugated sheets and
grey slates (potential sources of asbestos)
Idea :
• Use DSM to detect/segment roofs
• Detect Grey Slates (GS) and Corrugated Sheets (CS) using CNN
=> 2 training sets
• Compare results with cadastrial data (construction year)
• Identify places (X,Y) that could contain Asbestos
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remotesensing.vito.be
Automatic detection of grey slates (GS)
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remotesensing.vito.be
Automatic detection of corrugated sheets (CS)
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remotesensing.vito.be
Detection sucessfull
• Very few false positie and/or false negative
• Trail performed for the region Mol and Mechelen
Mol (5 cm GSD): 95 % for CS and 85 % for GS
Mechelen (10 cm GSD): 90 % for CS - 80% for GS
• Now for all flanders
• Detection expected to be even better as training/validation
sets will be larger
https://blog.vito.be/remotesensing/deep-learning-keeps-your-feet-dry
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remotesensing.vito.be
Detection of small canals in Flanders
• Small canals : l ~20 cm; H ~40 cm
• No digital atlas available for the small canals
• If some data, mostly position not correct (1 – 2 meters error
on X,Y coordinates)
• Altlas for the larger canals is also “old”
Idea :
=> use LIDAR data to detect automatically all canals
=> fuse with RGB data
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remotesensing.vito.be
Using the hydrological atlas
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remotesensing.vito.be
Small canals : l ~20 cm; H ~40 cm
DEM CNN output
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remotesensing.vito.be
Automatic detection of small canals in Flanders
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remotesensing.vito.be
Automatic detection of small canals in Flanders
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remotesensing.vito.be
Main challenges
No real ground true available
Control of the results has to be done visually
LIDAR detect altitude: if canal full water no detection
Some false negative were observed for some slopes
Advantages :
Enhanced real positioning (1-2m error with manual GPS)
Make easier the work op operator (manual work) for new atlas
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remotesensing.vito.be
Automatic delineation of agricultural parcels in
Belgium using Copernicus Sentinel-2 data
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remotesensing.vito.be
CROPSAR : using Recurrent NN (temporal
evolution) for classification of crops
fAPAR can be deducted from Sentinel-2 multispectral data
S-2 revisiting time : 5 days
Problem : when cloudy => no data
As a result: only very limited amount of values for each parcel
Idea : enhance the timely information using Sentinel-1 SAR data
that offer a 6 days revisiting period
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remotesensing.vito.be
Combining Sentinel-1 and Sentinel-2 data to beter
estimate the temporal evolution of the fAPAR
Multispectral
images 20 m GSD
SAR data
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Sentinel-1
uninterrupted
Sentinel-2
interrupted
Sentinel-2 uninterrupted
Example potato
field monitoring
based on a deep neural network
CropSar: Optical – Radar fusion
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remotesensing.vito.be
Better classification achieved
Field-based
fused S1-S2 time
series for all
agricultural
parcels of 2017
growing season
in Flanders
CropSar: Current test dataset:
Pre
dic
ted b
y f
usi
on
Observed
https://blog.vito.be/remotesensing/cropmap
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THANK YOU
remotesensing.vito.be
Sentinel-2 image Copernicus Sentinel data (2016)
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b l o g . v i t o . b e / r e m o t e s e n s i n g
Boeretang 2002400 Mol - [email protected]
r e m o t e s e n s i n g . v i t o . b e
SEETHEBIGGERPICTURE
NICOLAS
LEWYCKYJ
Project Manager
Security Applications
Contact :