SAR and LIDAR data fusion: project presentation
-
Upload
alessandro-coppola -
Category
Engineering
-
view
380 -
download
2
Transcript of SAR and LIDAR data fusion: project presentation
Fusion of Synthetic Aperture Radar and lidar data for mapping of semi-natural areas
©Alessandro Coppola, 2015
1
Purposes of research
• Develop an algorithm to create an enhanced classification map for semi-natural areas, based on the fusion of SAR and lidar datasets
• Apply the general technique to the Maspalomas area, Gran Canaria (case of study)
• Validate the algorithm: accuracy assessment
2
SAR 3Lidar
• SAR• Active microwave remote
sensing technology• Grey scale image, depending on
the microwave backscattering
• Lidar• Active Near-Infrared
remote sensing technology• Point cloud: measurement
of 𝑥, 𝑦, 𝑧 coordinates of the reflective target
SAR imageAerial photo (©Google)
Lidar point cloud
𝑑 =𝜏 ∙ 𝑐
2
HEI
GH
T
REF
LEC
TIV
ITY
Priority classes algorithm 4
Mask 1 Mask 2 Mask 3OFF
ONONON
OFFOFF
CLASS 1 CLASS N+1CLASS 3CLASS 2
MAP
Mask N
CLASS N
ON
OFF
Image 1
Image 2
Feature extraction
Feature level fusion
Binary Segmentation
Supervised labelling
Feature extraction CLASS
Classification
Mask
ON
OFF
• Single classes are detected with masks, in order of priority
• Every class is masked off in the following steps
• The single-class images are combined to obtain the final classification map
• Masks from features• Classification with
scattering models
Study area and datasets
Project ARTeMISat
• Protected and ecologically sensitive area threatened by human presence
• Maspalomas Natural Reserve (Gran Canaria)
Maspalomas area (©2015 GRAFCAN) – Ground truth©
SAR data
Processing level L1B
Mission/Satellite TerraSAR-X1 (9 GHz)
Date 2008-01-05
Sensor Mode Spotlight
Incidence Angle 42°
Product Type Enhanced Ellipsoid Corrected
Spatial Resolution 1.4 m
Polarization HH
Lidar data
Mission PNOA (2009)
Pulse rate 45 kHz
Spatial resolution spacing 1.41m
Altimetric accuracy RMSEz 0.20 m
Deviation from vertical axis 5°
Orthophoto pixel size 0.25 m
5
9 classes
1 Sea
2 Swimming pools
3 Sand
4 Asphalt, Trees, Shrubs
5 Grass
6 Terrain, Buildings
Lidar intensity Lidar DEM (Digital Elevation Model)
Lidar orthophoto Lidar DSM (Digital Surface Model)
6
Images
SAR image
Aerial photo (©Google)
Sea and swimming pools masks
• Swimming pools• Highest reflectance in the blue wavelengths• Lower reflectance in the red wavelengths
Orthophoto NDSPI Swimming pools mask
Spectral signatures of five swimming pools
DEM mask
ON
SEA
Normalized Difference Swimming Pool Index
𝑁𝐷𝑆𝑃𝐼 =𝐵𝐿𝑈𝐸−𝑅𝐸𝐷
𝐵𝐿𝑈𝐸+𝑅𝐸𝐷𝑁𝐷𝑆𝑃𝐼 ∈ [−1,1]
• 95% of DNs ∈ [-0.2 and 0.2]• NDSPI provides the highest values in swimming pools𝑁𝐷𝑆𝑃𝐼 > 0.18 ⇒ swimming pools
NDSPI mask
POOLS
ON
7
Sea mask
Spectral signatures of three rivers
OFF OFF
Texture analysis on SAR data: dissimilarity
• Grey-level co-occurrence matrix (GLCM)
• Co-occurrence measures (Haralick features)
• Dissimilarity
𝑓𝐷𝐼𝑆 =
𝑖=0
𝑀−1
𝑗=0
𝑀−1
𝑖 − 𝑗 ∙ 𝑝(𝑖, 𝑗)
𝑝 𝑖, 𝑗 : (𝑖, 𝑗)th entry in the normalized co-occurrence matrix
M: number of grey levels (dimension of the matrix).
𝑓𝐷𝐼𝑆 increases linearly with increased contrast between neighbouring pixels.• Dunes (sand)
• dark background peppered by bright pixels
Orthophoto Dissimilarity Sand mask
• 𝑓𝐷𝐼𝑆 < 2 ⇒ sand
• 2 < 𝑓𝐷𝐼𝑆 < 9 ⇒ darkest pixels (terrain, asphalt, grass)• 𝑓𝐷𝐼𝑆 > 9 ⇒ brightest pixels (buildings, vegetation)
The dissimilarity mask 2 is applied to the lidar intensity
Dissimilarity Masked dissimilarity Masked lidarintensity
8
ONON
OFF OFF
SAND
Dissim. mask 2
Dissim. mask 1
-SEA-POOLS
Shrubs, trees and buildings masks
Multiple returns Shrubs mask Trees mask Buildings mask
Laser pulse can penetrate the tree canopy resulting in a multiple return.
𝐼𝑀𝐿 = 𝐼𝐹𝑅 − 𝐼𝐿𝑅
Normalized DSM: nDSM = DSM - DEM
𝐼𝑀𝐿 ≠ 0 𝑎𝑛𝑑 𝑛𝐷𝑆𝑀 > 2 ⇒ Trees𝐼𝑀𝐿 ≠ 0 𝑎𝑛𝑑 𝑛𝐷𝑆𝑀 < 2 ⇒ Shrubs𝐼𝑀𝐿 = 0 𝑎𝑛𝑑 𝑛𝐷𝑆𝑀 > 5 ⇒ Buildings
9
ON ON
ON
ON
OFF OFFOFF
OFF
TREES
SHRUBS BUILDINGS
Unclass.Dissim. mask 2
ML mask
nDSMmask
nDSMmask
nDSM
-SEA-POOLS-SAND
Asphalt, grass and terrain masks
• Manmade materials have the lowest lidar intensity returns (below the 50%)
• The masked lidar intensity image has 95% of the values between 0 and 90
𝐼𝐿𝐼𝐷 < 45 ⇒ asphalt
Masked lidar intensity Asphalt mask
Grass maskNDVI
Normalized Difference Vegetation Index
𝑁𝐷𝑉𝐼 =𝑁𝐼𝑅−𝑅𝐸𝐷
𝑁𝐼𝑅+𝑅𝐸𝐷, 𝑁𝐷𝑉𝐼 ∈ [−1,1]
Healthy vegetation falls between values of 0.20 to 0.80:
𝑁𝐷𝑉𝐼 ∈ 0.20, 0.80 ⇒ grass𝑁𝐷𝑉𝐼 ∉ 0.20, 0.80 𝑎𝑛𝑑 𝑛𝐷𝑆𝑀 = 0 ⇒ terrain Terrain mask
Dissim.mask
ON
10
ON ON
OFF
OFF
TERRAINGRASSASPHALT
NDVI mask
Lidar int. mask
OFF
-SEA-POOLS-SAND
nDSMmask
ON
OFF Unclass.
Priority classes algorithm 11
DEM mask
ON
SEA
NDSPI mask
POOLS
ON
OFF
ON
ON
ON ON
ON
ON
ON
OFF OFF OFF OFF
OFF OFF
OFF
OFFSAND
TREES
SHRUBS BUILDINGS
GRASSASPHALT
Unclass.
NDVI mask
Dissim. mask 2
ML mask
nDSMmask
nDSMmask
Lidar int. mask
Dissim. mask 1
TERRAIN
nDSMmask
ON
OFF Unclass.
12
Final fused classification image
SEA
POOLS
SAND
ASPHALT
GRASS
TERRAIN
HIGH VEG.
SHRUBS
BUILDINGS
13
UNCLASSIFIED: 6.5 %
Validation
• Confusion matrix and overall accuracy
• Four “macro-classes”: built soil, vegetation, bare soil, water
• Comparison with maximum likelihood (ML) classifier on lidar data alone ⇒ overall accuracy of 71.09 %
User/Ref.class (%)
Built soil Vegetation Water Bare soil
Built soil 87.50 3.13 9.38 6.25
Vegetation 0.00 78.13 0.00 3.13
Water 3.13 0.00 84.38 0.00
Bare soil 9.38 18.75 6.25 90.63
Tot. 100.00 100.00 100.00 100.00
Overall accuracy: 85.15 %
Confusion matrix for the fused data classification
14
Conclusions
• Overall accuracy ≈ 85% ⇒ improvement of 14 % as compared to lidardata alone
• Original algorithm
• Repeatability• High resolution SAR data
• Features (Haralick measures, DEM, DSM, multiple returns, NDVI)
• Training samples for thresholding
15